NISTHB 157 

Guidelines for 

Radiometric 

Calibration of 

Electro-Optical 

Instruments for 

Remote Sensing

Joe Tansock, Daniel Bancroft, Jim 

Butler, Changyong Cao, Raju Datla, 

Scott Hansen, Dennis Helder, Raghu 

Kacker, Harri Latvakoski, Martin 

Mlynczak, Tom Murdock, James 

Peterson, David Pollock, Ray Russell, 

Deron Scott, John Seamons, Tom Stone, 

Alan Thurgood, Richard Williams, 

Xiaoxiong (Jack) Xiong, Howard Yoon 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.HB.157

April 2015 

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards 

and Technology and Acting Director 

NIST HB 157 

Guidelines for Radiometric 

Calibration of Electro-Optical 

Instruments for Remote Sensing 

Raghu Kacker 

Applies and Computational Mathematics Division 

Information Technology Laboratory 

Howard Yoon 

Sensor Science Division 

Physical Measurement Laboratory 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.HB.157 

May 2015 

U.S. Department of Commerce 

Penny Pritzker, Secretary

\National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director 

 

i 

PREFACE 

This publication provides guidelines for conducting radiometric calibrations of electro-optical 

(EO) sensors. It is intended for use by managers, technical oversight personnel, scientists, and 

engineers as a reference for planning and successfully executing sensor calibrations. This 

document is a collaborative effort between the US government, academic institutions, and 

industry, and represents lessons learned from experts with years of accumulated knowledge and 

experience planning, reviewing, preparing, conducting, analyzing, implementing, and reporting 

calibration efforts. 

Technical terms and definitions are introduced as needed throughout the document. Important 

terms, acronyms, and common references used in this text are summarized in the glossary at the 

end of the publication. 

The manuscript contents are solely the opinions of the authors and do not constitute a statement of 

policy, decision, or position on behalf of NOAA or the U.S. Government. Any mention of 

commercial products is for information only; it does not imply recommendation or endorsement 

by NIST or author organizations. 

 
ii 

ACKNOWLEDGEMENTS 

While this publication would not be possible without modest levels of institutional support from 

Space Dynamics Laboratory (SDL), National Institute of Standards and Technology (NIST), 

National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric 

Administration (NOAA), The Aerospace Corporation1, Frontier Technology, Inc. (FTI), U.S. 

Geological Survey (USGS), University of Alabama in Huntsville (UAH), and South Dakota 

State University (SDSU), it is primarily the effort of the authors who made this work possible. 

This is especially true of Joe Tansock, who took the lead in coordinating the efforts, making 

assignments, authoring specific sections, editing, and pulling it all together. The authors 

gratefully acknowledge the efforts of Peg Cashell at SDL for providing technical writing and 

editing expertise to this document. 

1

 This work is supported at The Aerospace Corporation by the Independent Research and Development program. 
iii 

CONTENTS

AUTHORS AND AFFILIATION ...................................................................................................v

EXECUTIVE SUMMARY .......................................................................................................... vii

 INTRODUCTION .......................................................................................................................1

 PRINCIPLES OF EFFECTIVE CALIBRATIONS ...................................................................5

2.1 Lessons Learned .................................................................................................................. 5

Calibration Planning Should Begin in the Early Stages of Sensor Design .............. 5

Trade-Offs Must be Made when Planning and Implementing a Calibration Effort . 5

Calibration Measurements Must be Traceable within a Specified Uncertainty ....... 6

System-Level Testing Provides the Best Representation of Sensor Performance ... 7

Both Pre- and Post-Launch Calibrations are Critical to a Successful Calibration ... 9

2.2 Calibration Responsivity Domains ................................................................................... 10

2.3 Calibration Requirements across Disciplines ................................................................... 11

Earth Sciences ........................................................................................................ 11

Atmospheric Sciences ............................................................................................ 13

DoD Applications ................................................................................................... 14

 CALIBRATION TRACEABILITY, MEASUREMENT UNCERTAINTY, AND 

VERIFICATION AND VALIDATION ...................................................................................17

3.1 Traceability ....................................................................................................................... 17

Metrological Traceability ....................................................................................... 18

Metrological Traceability and Remote Sensing Measurements ............................. 19

Système International (SI) Traceability ................................................................. 20

3.2 Measurement Uncertainty ................................................................................................. 24

3.3 Verification and Validation (V&V) .................................................................................. 28

 CALIBRATION PLANNING ..................................................................................................31

4.1 Early Calibration Planning ................................................................................................ 31

Calibration Planning Trade-Off Space ................................................................... 31

Detailed Test Schedule ........................................................................................... 32

Component- vs. System-Level Testing .................................................................. 33

Pre- and Post-Launch Calibration Needs ............................................................... 33

Sensor Performance Model .................................................................................... 34

Calibration Parameters and Equations ................................................................... 35

Capabilities Required for Calibration Data Collection .......................................... 37

Environmental Conditions for Pre-Launch Calibration ......................................... 41

Day in the Life Tests .............................................................................................. 42

4.2 Calibration Plans and Procedures ..................................................................................... 43

Strawman Calibration Plan ..................................................................................... 43

Comprehensive Calibration Plan ............................................................................ 44

Data Collection Procedures .................................................................................... 44

Human Resource Requirements ............................................................................. 45

4.3 Data Collection and Data Management System ............................................................... 45

Data Collection and Data Management Plan ......................................................... 45

Data Collection and Data Management Hardware ................................................. 45
iv 

Data Collection and Data Management Software .................................................. 47

Automated Data Collection .................................................................................... 48

Real-Time Display and Monitoring ....................................................................... 49

 PRE-LAUNCH CALIBRATION ..............................................................................................51

5.1 Preparations....................................................................................................................... 51

5.2 Engineering Testing .......................................................................................................... 51

5.3 Ground Support Equipment (GSE) ................................................................................... 52

Test Chambers ........................................................................................................ 52

Calibration Sources ................................................................................................ 53

Sensor GSE ............................................................................................................. 58

GSE Preparation ..................................................................................................... 58

GSE Operation and Traceability Maintenance ....................................................... 60

5.4 Pre-Launch Calibration Data Collection and Data Quality Assessment .......................... 61

5.5 QuickLook Analyses ......................................................................................................... 62

 POST-LAUNCH CALIBRATION ...........................................................................................63

6.1 Early Operations ............................................................................................................... 63

6.2 Intensive Calibration and Validation ................................................................................ 63

6.3 Sensor Performance Trending........................................................................................... 64

6.4 On-Orbit Calibration Sources ........................................................................................... 65

On-Board Calibration Sources ............................................................................... 66

Stars ........................................................................................................................ 70

Lunar Calibration Source ....................................................................................... 73

Other Celestial Object Calibration Sources ............................................................ 78

Vicarious Calibration ............................................................................................. 79

Pseudo Invariant Calibration Sites (PICS) ............................................................. 81

On-Orbit Cross-Calibration .................................................................................... 83

Solar Diffusers ........................................................................................................ 84

6.5 Frequency of On-Orbit Calibration Measurements .......................................................... 85

 DATA ANALYSIS AND REPORTING ..................................................................................87

7.1 Data Analysis Software ..................................................................................................... 87

7.2 Data Authentication .......................................................................................................... 87

7.3 Calibration Report ............................................................................................................. 87

7.4 Cross-Checks and Traceability ......................................................................................... 89

7.5 Long-Term Repository of Calibration Data ...................................................................... 90

REFERENCES ..............................................................................................................................91

COMMON EO SENSOR CALIBRATION REFERENCES ......................................................103

ACRONYM LIST ........................................................................................................................104

GLOSSARY OF TERMS ............................................................................................................107

BIOGRAPHICAL SKETCHES OF AUTHORS.........................................................................123
v 

AUTHORS AND AFFILIATION 

(ALPHABETICAL ORDER BY AFFILIATION)

Ray Russell 

The Aerospace Corporation, Mail Stop M2 – 266, P.O. Box 92957 

Los Angeles, CA 90009-2957 

ray.w.russell@aero.org 

David B. Pollock 

pollockd@uah.edu

Daniel Bancroft 

Frontier Technology, Inc. 

Beverly, MA 

dbancroft@fti-net.com 

Tom Murdock 

Frontier Technology, Inc. 

Beverly, MA 

tmurdock@fti-net.com 

Jim Butler 

Goddard Space Flight Center

Greenbelt, MD 

james.j.butler@nasa.gov 

Martin Mlynczak 

Langley Research Center 

Hampton, Virginia 

m.g.mlynczak@nasa.gov 

Xiaoxiong (Jack) Xiong 

Goddard Space Flight Center

Greenbelt, MD 

xiaoxiong.xiong-1@nasa.gov 

Raghu Kacker 

Gaithersburg, MD 

raghu.kacker@nist.gov 

Howard Yoon 

Gaithersburg, MD 

howard.yoon@nist.gov 

Raju Datla 

NOAA Affiliate 

College Park, MD 

raju.datla@noaa.gov 

Changyong Cao 

NOAA 

College Park, MD 

changyong.cao@noaa.gov 

Joe Tansock 

1695 Research Park Way 

Logan, UT 84341 

joe.tansock@sdl.usu.edu 

Harri Latvakoski 

harri.latvakoski@sdl.usu.edu 

James Q. Peterson 

james.peterson@sdl.usu.edu 

Deron Scott 

deron.scott@sdl.usu.edu 

Scott Hansen 

scott.hansen@sdl.usu.edu 

John Seamons 

john.seamons@sdl.usu.edu 

Alan Thurgood 

alan.thurgood@sdl.usu.edu 

Dennis Helder 

South Dakota State University

Dennis.Helder@sdstate.edu 

 

Tom Stone 

Flagstaff, AZ 

tstone@usgs.gov

 

Richard Williams 

Northrop Grumman Corp. 

Azusa, CA 

richard.williams@ngc.com
vi 

This Page Intentionally Left Blank 

 
vii 

EXECUTIVE SUMMARY

State of the art electro-optical (EO) sensors designed for today’s space-based applications require 

thorough, system-level radiometric calibrations to characterize the instrument and to ensure that all 

mission objectives are met. Calibration is the process of evaluating the parameters required to 

understand and describe the performance of a sensor. Through the calibration process, the sensor’s 

response to a radiometric input is quantified, the interactions and dependencies between the optical 

and electronic components are characterized and systematic errors that may result are discovered 

and evaluated, and traceability to national and international standards is established by rigorous 

calculation of the associated uncertainties. Calibration increases the probability of mission success 

by verifying that the sensor will meet mission requirements with a correct interpretation of the data 

to make accurate mission decisions. 

This publication provides guidelines for conducting EO sensor calibrations. It is intended for use 

by managers, technical oversight personnel, scientists, and engineers as a reference for planning and 

successfully executing a sensor calibration. 

Lessons learned from calibration experts throughout the U.S. and the world show that successful 

and effective calibrations have various elements in common, and that these elements should always 

be considered for a calibration effort. These elements include: 

 Calibration planning should begin in the early stages of sensor design to optimize calibration 

efficiency in the final design 

 Trade-offs between performance, cost, and schedule must be made when planning and 

implementing a calibration effort 

 Calibration measurements should be traceable with a thoroughly analyzed uncertainty 

 System-level testing provides the best picture of sensor performance 

 Both pre- and post-launch calibrations are critical to a successful calibration and traceability 

of the sensor data 

CALIBRATION TRACEABILITY, MEASUREMENT UNCERTAINTY, AND VERIFICATION AND VALIDATION

The foundation of effective calibration is built upon national and international standards of 

measurement. This foundation consists of traceability, measurement uncertainty, and verification 

and validation (V&V), which work together to provide confidence in the sensor data output. 

Traceability refers to the ability to track a measurement to a known standard unit of measurement 

within a rigorous calculated uncertainty. V&V ensures that the instrument operates as designed and 

produces relevant data by proven processes and standards. All three are required to obtain reliable 

data that can be directly compared to model predictions and results from other instruments, among 

other uses. 

CALIBRATION PLANNING

To be effective, calibration must be considered at all stages of a sensor program. Calibration 

planning for the lifetime of a sensor promotes an optimum calibration approach, reduces costs and 

expenditures, and minimizes uncertainty for the intended application. This planning should begin 

during the sensor design phase and continue until the sensor is no longer collecting data. 
viii 

Experienced calibration personnel must be involved throughout the lifetime of the sensor, including 

its development phase, to optimize calibration efforts. 

The goal of calibration planning is to determine the most efficient calibration approach that meets 

performance requirements, while minimizing calibration uncertainty, schedule, cost, and risk. 

Trade-offs that must be considered during calibration planning include the test schedule, 

component-level vs. system-level testing, and pre-launch vs. post-launch calibration needs. 

In addition to trade-off studies, a sensor performance model and uncertainty budget should be 

compiled to identify the parameters and characterization required to understand the sensor's 

performance. These tools support both system design and calibration planning, and lead to the 

development of sensor-specific calibration equations that are used to convert the sensor output (in 

units of counts, volts, etc.) to the desired scientific data products. 

The calibration planning process is often initiated by first developing a strawman calibration plan 

that identifies the sensor, science, project, and mission requirements that are then used to determine 

the needed calibration parameters. Based on the strawman plan, a more mature and detailed 

comprehensive calibration plan is generated. Step-by-step data collection procedures are then 

developed to identify each step of the data collection process to ensure that the resulting data is 

adequate for subsequent analyses. 

A data collection and management system must also be developed and tested during the calibration 

planning period. This involves preparing a data collection and management plan, and developing 

the hardware and software required for the effort. Successful data collection and management 

systems can control and monitor the required tasks of setting the sensor's operating state, test 

environment, and calibration sources to known values, verifying that the sensor’s response is within 

an acceptable range, and storing data for future detailed analysis tasks. A well designed data 

collection and management system can minimize the volume of data that needs to be collected and 

the time required to collect it. It can also organize collected data so that analysts have quick access 

to the information needed to evaluate sensor performance. 

PRE-LAUNCH CALIBRATION

Execution of an effective calibration begins with pre-launch calibration measurements and 

continues after the sensor is placed in orbit. During pre-launch calibration, or ground calibration, 

tests are performed in a controlled environment with known sources that cannot be duplicated on 

orbit. Measurements made during pre-launch calibration are used to verify proper instrument 

operation, quantify calibration equation and radiometric model parameters, and estimate 

measurement uncertainties. Pre-launch testing provides information on sensor performance nuances 

that can be addressed and understood before launch. In addition, anomalies may be uncovered and 

resolved before launch. Options to measure unexpected behavior and implement corrections to 

sensor performance on orbit are often limited and expensive. 

System-Level Testing and Calibration - System-level calibration can be visualized as the quality 

control aspect of system design and testing (Wyatt 1991). Characterizing the integrated system 

identifies interactions and dependencies between the optical and electronic components, and allows 

systematic errors to be discovered, evaluated, and resolved before flight. System-level calibration 

also validates the sensor model predictions and is used to determine the sensor performance 

uncertainty. 

When conducting pre-launch calibration, it is best to follow the axiom “test like you fly” (TLYF) 

(Datla et al., 2011; Russell 2008), which states that instruments should be calibrated as closely as 
ix 

possible to the same environmental conditions expected during operation. Testing under conditions 

that simulate the on-orbit environment usually requires special equipment that is compatible with 

environmental factors such as vacuum, temperature, and contamination. Special test hardware must 

be used during calibration to simulate these on-orbit conditions and to present specific scenes to the 

unit under test. Thermal vacuum (TVAC) chambers are used to provide the mechanical, electrical, 

and thermal configurations required by the sensor. 

Ground Support Equipment (GSE) – The quality of a calibration is only as good as the tools and 

references used to perform the calibration; therefore, the equipment used in the calibration, typically 

referred to as GSE, must be well-characterized, stable, and accurate. This process can take 

considerable time at a significant cost. This equipment typically includes test chambers, calibration 

sources, electrical support equipment (ESE), the data collection and management system, and the 

sensor GSE. 

Ground Calibration Sources – Various calibration sources are used to provide well understood 

and/or repeatable flux levels as optical input to the sensor being calibrated. There are many commonly 

used ground calibration sources including spectral, spatial, linearity, radiance, irradiance, temporal, 

and scene generation sources. 

Engineering Test – An engineering test is often performed prior to the start of ground calibration 

data collection. This test helps verify sensor operation, calibration hardware operation, data 

collection automation and management, and the flow of calibration test procedures and test 

configurations. It can also help identify additional tests that may be needed to further quantify the 

sensor performance. Measurements made during engineering testing can also provide preliminary 

data that can be used for future analyses. 

Data Collection and Data Quality Assessment – During calibration testing, the data collection 

engineer will follow the procedure, fill in log entries, and make note of events or conditions that 

may affect the data. Real-time displays provide feedback to verify proper instrument configuration, 

GSE configuration, and response levels. Data quality checks should also be performed throughout 

the data collection period. Quicklook analyses of subsets of the calibration data can help evaluate 

data quality and can provide additional guidance to the remainder of the calibration campaign. The 

data quality assessment approach is unique to each payload/sensor and should be addressed in the 

calibration plan. The goal of data quality assessments is to obtain confidence that the data can be 

used for the intended calibration analyses. 

Quicklook Analyses – Quicklook analyses are performed during testing shortly after data are 

collected for each test. These analyses provide preliminary instrument performance results, and help 

provide confidence that the intended, more detailed analyses (usually performed post calibration 

testing) can be successfully completed. These results, often presented in the form of graphs and 

tables in a similar format to the intended final analyses, allow project leaders to make educated path 

forward decisions. 

Day in the Life Test – A day in the life (DITL) test may be performed pre-launch to understand the 

expected behavior of the sensor in its on-orbit environment, to flush out any residual concerns with 

how the sensor will be used on-orbit, and to implement a test-like-you-fly philosophy. This is 

usually performed for a full 24-hour period to attempt to mimic the diurnal variations of the 

expected on-orbit environment on the worst case day, and to provide the opportunity for 

commanding and data loading of the system to mimic what is expected during flight operations. The 

DITL test will help identify consequences of actual operation of the sensor that may not have been 

anticipated and thus may not be in the models for the sensor or the sensor plus space bus. 
x 

POST-LAUNCH CALIBRATION

The most effective calibration approach builds on pre-launch characterization with post-launch, or 

on-orbit, data collection. The goals of post-launch calibration are to verify and validate the 

calibration parameters determined pre-launch, characterize or update parameters that are more 

successfully characterized from on-orbit measurements, quantify calibration uncertainty, and update 

calibration coefficients if necessary to meet measurement requirements. In addition, sensor 

calibration must be maintained throughout mission life, and changes in sensor behavior due to 

component aging and/or sensor contaminations must be trended and managed. 

Once the instrument reaches orbit, on-orbit calibration operations begin. These operations are used 

to show whether the sensor is functional and/or whether any significant changes have occurred to 

the sensor during launch. Specific procedures for on-orbit operations are then implemented to 

derive/verify parameters that were not measured during ground testing or to update those 

parameters that can be conducted in both ground and on-orbit operations. Sensor performance 

trending continues for the duration of sensor operations using on-board sources and on-orbit sources 

to demonstrate that measurements collected continue to meet the standards required for the sensor. 

After launch, calibration measurements necessarily take second place to mission observations and 

must be interwoven into the mission timeline to minimize on-orbit calibration time. The decreased 

availability of on-orbit calibration time highlights the importance of a comprehensive pre-launch 

calibration. In addition, on-orbit calibration measurements typically require observation of on-orbit 

calibration sources that are different from the mission observation targets and therefore cannot be 

performed simultaneously with the mission data collections. 

On-Orbit Calibration Sources – On-orbit calibration measurements are implemented using 

whatever observable sources may be available to serve as a calibration source, including on-board 

devices, sources that are ejected from the payload, celestial objects, natural or artificial sites on the 

surface of the Earth, and solar diffusers. In addition, sensors can view space as a zero radiance 

source as part of on-board calibration, and sensors can be compared to calibrated sensors in another 

orbit viewing the same Earth scene at the same time. 

DATA ANALYSIS AND REPORTING

An unbroken process of data analysis and reporting should continue from pre-launch into postlaunch calibration to capture lessons learned from pre-launch testing and maximize program 

efficiency. Analyzing calibration and sensor performance data requires experienced analysts and a 

variety of software tools that are frequently developed, or modified, from existing tools, specifically 

for the particular sensor program. Many if not all of these tools and algorithms are applicable to 

post-launch as well as pre-launch data analysis. 

The results of the calibration effort are usually documented in a detailed calibration report. The 

overall goal of the calibration report is to provide quantitative evidence of measurement 

performance. This report can also be used for future reference to assist in answering critical 

performance/technical and pragmatic questions. 

The large amounts of data often generated during calibration testing provide a lasting resource to 

the end user. Pre-launch test and calibration data must be archived in such a way that they are 

available for the analyst at all times during the pre-launch and operational phases of the mission. 

The lessons learned and knowledge gained from analyzing the ensemble of data from all phases of 

the mission can benefit the next generation of sensors. 
1 

INTRODUCTION

State of the art, remote sensing electro-optical (EO) sensors being designed for today’s spacebased applications require thorough radiometric calibrations to characterize the instrument and to 

ensure that all mission objectives are met. The purpose of calibrating EO sensors is to measure 

characteristics of a remote object, such as the Earth or celestial objects, to estimate their 

radiometric responsivity characteristics (emissive, reflective, and transmittance), spatial (position, 

size, and distribution), spectral (spectral content), temporal (changes with time), and polarization 

properties. These properties are inferred from the sensor’s response to the flux incident upon its 

entrance pupil or aperture. Success in defining object attributes using remote-sensing techniques 

therefore requires that the sensor response be thoroughly defined and understood, which can only 

be accomplished by EO sensor radiometric calibration. (Wyatt 1991). 

The calibration guidelines provided in this document begin with the early stages of the sensor 

design and address calibration throughout the life-cycle of the sensor. The following chart maps 

the publication contents into a notional, but often typical, sensor life-cycle time line, covering 

sensor preliminary design to post-launch operations. 
2 

What is EO Sensor Radiometric Calibration for 

Remote Sensing? Calibration is the process of 

characterizing the parameters required to understand and quantify 

the performance of a sensor for its intended application. This 

calibration process also converts a sensor’s output (in units of 

counts, volts, etc.) to physical units (often radiometric units such 

as W/cm2, J/sec/cm2, etc.) with traceability to a known standard 

and within a specified uncertainty. 

Why is EO Sensor Radiometric Calibration 

Necessary? EO sensors require calibration to quantify the 

sensor’s response to known radiometric input and to characterize 

the interactions and dependencies between the optical, mechanical, 

and electronic components. In addition, sensor specific 

performance dependencies and systematic errors can be identified 

through calibration. 

Calibration increases the probability of mission success by: 

 Identifying measurement performance and limitations 

 Providing characteristic equations and parameters that relate the measured signal to the 

true scene radiance and spatial content 

 Allowing timely and correct interpretation of data to make more accurate mission 

decisions 

 Minimizing the impact of sensor behavior on the intended measurements by identifying 

and characterizing unique sensor performance characteristics 

 Quantifying measurement uncertainty that can be used to provide a clear understanding 

of the data 

 Verifying that the sensor will meet mission requirements 

Sensor calibration affects the quality of the interpretation of the data, and thus the success or failure 

of the performance of the critical task, not just the success or failure of a particular sensor or 

program. This applies to both science and Department of Defense (DoD) remote sensing 

applications. 

The following examples highlight sensor missions that owe their success in part to a thorough 

calibration. Subsequent sections in this document provide a complete overview of EO sensor 

calibration and the sensor performance obstacles that can be minimized by proper attention to 

calibration in remote sensing work. 

Calibration efforts contributed to 

the success of Landsat. 

Connecticut River after Hurricane Irene 

(Sensor: L5 TM, Acquisition Date: 9/2/11) 

(USGS/NASA Landsat)
3 

Landsat 

The Landsat program, which has provided the longest-running continuous data set of high spatial 

resolution Earth imagery, attributes its success partly to the ability to understand the radiometric 

properties of the sensors due to the combination of pre-launch and post-launch calibration efforts 

(Thome et al., 1997). Over 15,000 coefficients are issued to span distinct timeframes and are 

continually updated with improved calibration coefficients. The radiometric calibration of these 

systems allows the full Landsat data set to be used in a quantitative sense. 

SABER 

http://saber.gats-inc.com

The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument is 

a 10-channel radiometer that spans the range of wavelengths from 1.27 μm to 17 μm. The 

instrument uses state-of-the-art mechanical cooling of the detector focal plane array to 75 K to 

achieve high radiometric sensitivity, operational flexibility, and long experiment life. An in-flight 

calibration system is incorporated to provide high long-term accuracy (Russell et al., 1999). 

SABER was launched in 2001 on the NASA Thermosphere-Ionosphere-Mesosphere Energetics 

and Dynamics (TIMED) satellite to study the structure, composition, and energy balance in the 

Earth’s mesosphere and lower thermosphere (Mlynczak 1997). The instrument is still obtaining 

data in 2014. An accurate calibration and detailed instrument characterization of the SABER 

instrument were fundamental to the ability of SABER to generate meaningful geophysical data 

products. The ground calibration of this instrument is described by Tansock et al. (2003) and a 

calibration update in Tansock et al. (2006). 

CERES 

http://www.nasa.gov/miss

ion_pages/NPP/news/cer

es-on-npp.html

The Clouds and the Earth’s Radiant Energy System (CERES) instruments on the NASA Terra, 

Aqua, and Suomi/NPP spacecrafts (Wielicki et al., 1996) are examples of instruments used to 

measure the Earth’s climate system. The CERES instruments are highly accurate, broadband 

radiometers that measure top-of-atmosphere (TOA) fluxes of the reflected solar irradiance and 

the emitted infrared irradiance. TOA net radiation is the long-term global average of the incoming 

and outgoing energy from the Earth. In an equilibrium climate state this difference is zero. If the 

climate is forced, there is an imbalance in the TOA irradiances as the Earth works to restore 

balance. This imbalance may be on the order of ~ 1 W/m2, which is hard to detect. Many 

instrument issues can cause changes in the instrument calibration over time, and unless 

accounted for, will appear as changes in the climate system. Therefore, a thorough understanding 

of the sensor system is required for accurate climate measurements. 

SPIRIT III 

http://space.skyrocket.de/

doc_sdat/msx.htm

The Spatial Infrared Imaging Telescope (SPIRIT III) was launched aboard the Mid-course Space 

Experiment (MSX) spacecraft on 24 April 1996. To assure accuracy and instill confidence in the 

data, the project developed methodologies to certify the process of converting raw sensor data to 

calibration corrected output. For this project, the raw data along with the tools needed to apply 

calibration were archived, allowing the user to reprocess data as calibration was refined. Data 

quality was verified by the Performance Assessment Team (PAT) and the entire process was 

reviewed by the Data Certification and Technology Transfer (DCATT) committee and included 

end-to-end certification testing and uncertainty evaluation. A detailed and comprehensive 

calibration approach was implemented (both ground and on-orbit making use of all available 

external and internal on-board calibration sources with an emphasis on traceability). Repeatability 

was achieved by controlling the configuration of the sensor, data processing software, calibration 

software, and calibration parameters. This rigorous approach provided a common starting point 

from which the several principle investigator teams were able to proceed with confidence. The 

final MSX report is documented in MSX Data Application for Future MDA Overhead Persistent 

Infrared (OPIR) Efforts (SDL/09-576B). Publications associated with the MSX program are 

archived in MSX Bibliography, Version 3.3.2, Cleared for Public Release, SRE Log #0006-2601 

and BMDO Case 00-S-2657 (U) https://dcp.mda.mil/ 
4 

What is the Future of Calibration? Accurate calibration is becoming increasingly 

important for sensors. Calibration to achieve relative uncertainty of less than 1 % of the measured 

radiance is essential for the accurate retrieval of atmospheric temperature. Recognizing that 

absolute calibration is the limiting factor to detecting climate change from measurements made by 

orbiting satellites, industry leaders have proposed new approaches to calibration to achieve the 

required performance. One such proposal is the Climate Absolute Radiance and Refractivity 

Observatory (CLARREO) instrument (discussed in the following example), which represents the 

pinnacle of calibration and instrument characterization. The on-orbit ability to trace measurements 

to Système International (SI) units and to detect instrument changes for the life of the mission will 

produce a data set that researchers can use for decades, or return to decades later, with complete 

understanding of the data set, its measurement uncertainty, and its implications for the climate. 

CLARREO 

The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) 

mission was recommended by the National Academy of Science in 2007 (NRC 2007) 

as an innovative mission focused on detecting and attributing climate change from 

space-based measurements of Earth’s infrared and visible radiance spectra. The 

hallmark of CLARREO is its ability to tie radiance measurements, on orbit, to known 

metrological standards through the international system of units (Système 

International (SI) units). 

CLARREO is unique in that it will monitor absolute calibration and key instrument 

performance parameters, on orbit, for the life of the mission. The sensor will carry 

devices such as infrared lasers to monitor emissivity changes during the life of the 

mission, so that even these seemingly small instrument changes will not mistakenly 

be interpreted as changes in the climate system. Absolute radiometric accuracy will 

be maintained by calibration of every measured spectrum, and traceability of those 

spectra will be to the kelvin temperature standard through reference to a blackbody of 

known temperature, on orbit, for the life of the mission. The CLARREO mission is 

described in detail in Wielicki et al. (2013), and the concepts behind the infrared 

instrument on CLARREO are outlined by Anderson et al. (2004). As of late 2014, the 

CLARREO mission remains in pre-formulation status within NASA. 

The CLARREO mission benchmark measurements can provide calibrations to other 

satellite sensors through inter-comparison using the simultaneous nadir overpass 

(SNO) observations described in Section 6.4.7. NOAA researchers showed the SNO 

technique to be an effective way to intercompare satellite observations when the 

satellites cross in their orbits at the same time and make nadir observations of the 

Earth simultaneously (Cao and Heidinger 2002). 
5 

PRINCIPLES OF EFFECTIVE 

CALIBRATIONS 

Calibration is becoming increasingly more challenging as measurement 

requirements become more stringent, particularly in climate change applications. 

Scientists, engineers, and managers involved with calibration efforts often 

exchange information through conferences and publications to discuss and learn 

from past and present calibration efforts, with the goal of improving calibration 

(Tansock et al., 2006). Lessons learned from these discussions show that 

successful and effective calibrations have various elements in common and that 

these elements should always be considered when planning a calibration effort. 

2.1 LESSONS LEARNED 

 Calibration Planning Should Begin in the Early Stages of Sensor Design 

Calibration is critical to the success of a mission. Unfortunately, it is often an afterthought in the 

development of the sensor. This lack of planning can lead to increased testing times and inaccurate 

results. Early calibration planning throughout the lifetime of the program promotes an optimum 

sensor calibration approach that reduces costs and expenditures while minimizing uncertainty for 

the intended application. Experienced calibration personnel must be involved throughout the 

sensor’s development phase to optimize calibration efforts. 

A thorough system-level calibration approach should begin in the early stages of the sensor design 

and address calibration throughout the lifetime of the sensor (Tansock et al., 2004), including 

component- and system-level calibration, spacecraft integration and test, and on-orbit operations. 

Delaying calibration planning can lead to limited options for the calibration approach and can often 

be more expensive to execute. 

Calibration planning should begin during the preliminary sensor design phase to ensure the 

instrument is designed to facilitate calibration. After the sensor design has passed the critical 

design phase, an end-to-end calibration plan should be developed to ensure that the calibration 

approach meets sensor performance requirements. Data management and analysis needs must be 

considered due to the large amounts of data produced by many of today’s sensors. 

 Trade-Offs Must be Made when Planning and Implementing a Calibration Effort 

Sensor programs have limited funding, resulting in trade-offs in costs, phasing of available funds, 

and scope of calibration. There is always a trade-off between what is ideal, what is desired, and 

what is strictly required when performing sensor calibration. While it may seem expedient at the 

time, reducing the scope of the calibration effort to reduce costs may in fact lead to more costly 

issues later in the program that impact the success of the mission. The axiom “You only have one 

opportunity to collect the data” is for the most part true. Therefore, knowledgeable experts who 

Lessons learned 

provide guidance 

for successful 

sensor calibration. 
6 

can identify trade-offs among available budget, schedule, and impact to sensor performance or 

mission objectives should be included when deciding on test program specifics. 

Sufficient calibration data must be collected to span the operational envelope of the sensor, but the 

scope should not extend to the point where extrapolation beyond the bounds of the calibration data 

set is required. Attention and priority should be given to obtaining quality calibration 

measurements. 

Investment in appropriate special test equipment and calibration sources should be budgeted and 

considered as a necessity. The quality of the test setup needs to be at least as good as the sensor 

under test (SUT) (otherwise the sensor could end up being used to calibrate the special test 

equipment). In addition, it may be advantageous for a program to procure spares of key 

components for future evaluation to help explain unexpected behavior, or even a duplicate of the 

integrated sensor to exercise pre-launch calibration test activities, which could reduce the risk of 

damaging flight hardware. This approach would provide a better understanding of the sensor 

performance and identify and resolve calibration issues. 

Steps should be taken to optimize the calibration effort. Time can be saved by appropriate 

sequencing of the various tests to make the best use of analysts and resources. Optimization efforts 

should be assessed by a knowledgeable expert, as decisions need to be made about which specific 

data sets should be collected and how much data to collect. The focus of any optimization effort 

should be to address the issue of performing the overall sensor calibration more efficiently, and 

not on reducing the scope of the calibration effort. Reducing the scope of pre-launch calibration 

efforts may impart additional requirements for post-launch calibration, where options for 

collecting particular data sets are either limited or unavailable. 

 Calibration Measurements Must be Traceable within a Specified Uncertainty 

To optimize the success of a remote sensing mission, the sensor must provide measurements that 

can be trusted. For example, an Earth climate science satellite’s remote sensing mission provides 

continuous coverage and has the potential to allow observation of climate variables through longtime periods. Climate modelers require continuous data over long time periods to test their models 

and predict global climate variability. These measurements must be trusted and absolute, the 

measurement uncertainties well understood, and the measurements must be consistent with 

mission expectations to be of value to the modelers. Three properties that work together to provide 

confidence in the sensor data are traceability, measurement uncertainty, and verification and 

validation (V&V). 

Traceability refers to the ability to track a measurement to a known standard unit of measurement 

within a given measurement uncertainty. V&V ensures that the instrument operates as designed 

and produces relevant data by proven processes and standards. All three are required to obtain 

reliable data that can be directly compared to model predictions and results from other instruments. 

Traceability can be achieved by using the SI-based standards of a national measurement institute, 

such as the U.S. National Institute of Standards and Technology (NIST). During calibration, 

measurements should be compared to known reference standards, and discrepancies recorded to 

estimate the uncertainty of the calibration. The specified uncertainty of the standard itself is a 

crucial component of this estimation. A calibration will typically include a traceability chain to a 

primary standard and a quoted overall uncertainty for the performance of the unit under test. 
7 

The calibration data products and sensor performance knowledge obtained during calibration play 

a critical role in V&V at both the sensor and the mission level, creating a critical link between 

sensor calibration and overall mission-level success. Sensor calibration quantifies the as-built 

sensor performance, providing a basis for V&V of design requirements and sensor performance in 

support of mission objectives.Ensuring that calibration measurements are traceable within the 

expected uncertainty will provide V&V that the data can be used across disciplines such as Earth 

sciences, atmospheric sciences, and DoD applications. 

 System-Level Testing Provides the Best Representation of Sensor Performance 

Pre-launch calibration testing includes component-level 

testing and system-level testing of the completed sensor. 

Component-level testing is performed early during the 

assembly period, and provides a first look at the potential 

characteristics of a sensor. This testing also assists in 

developing model parameters used to estimate system 

performance. Component-level testing may help reduce costs 

and schedule by identifying problems at the lowest level of 

assembly. Component-level testing is usually not adequate to 

represent a full system-level calibration parameter, however. 

Issues with the focus of Hubble Space Telescope illustrate 

the importance of pre-launch, system-level calibration (see 

following example). For a complete system-level calibration, 

sufficient measurements must be made on the fully integrated 

sensor to cover every design configuration of the system, the sensor’s entire dynamic range, and 

all expected environmental conditions. Ideally, these performance metrics can be quantified 

completely during system-level calibration. 

System-level calibration can be visualized as the quality control aspect of system design and 

testing (Wyatt 1991). The advantage of system-level measurements is that all components are 

included in the measurement in the way they are used, as opposed to component-level 

measurements where differences in optical configuration, temperature, or orientation may be 

unavoidable. 

Characterizing the integrated system identifies interactions and dependencies between the optical 

and electronic components, and allows systematic errors to be discovered, evaluated, and resolved 

before flight. Although EO sensors may be designed and manufactured to strict specifications, 

components may behave differently than expected once installed in an instrument, and the 

interactions among integrated components makes each sensor unique. The most well thought-out 

designs and advanced fabrication techniques can still result in system-level sensor performance 

that differs from design specifications. 

Other factors that may contribute toward system-level performance variability include errors from 

the fabrication processes, changes in the components over time, and an unanticipated instrument 

behavior not covered by specifications. 

When schedule and cost constraints indicate that component-level measurements, which generally 

provide a cost advantage compared to system-level measurements, must suffice for some 

calibration parameters, careful consideration of the trade-off between component-level 

convenience and system-level accuracy is mandatory. The calibration results from the SABER and 

System-Level Testing 

System-level measurements provide 

confidence that details buried deep in 

the sensor design are fully tested. As an 

example, a sensor had spectral filters 

installed in filter wheel positions that did 

not match the documented positions. Had 

this anomaly not been identified as part of 

sensor-level verification performed during 

pre-launch calibration, confusion in the 

sensor data could have created credibility 

problems, jeopardizing the mission 

objectives. 
8 

SPIRIT III instruments, shown in the following examples, highlight the importance of performing 

system-level testing. The SABER instrument showed good agreement for 8 of the 10 bands, but 

for one of the bands, there was a difference of greater than 20 % when comparing component-level 

and system-level measurements. For the SPIRIT III instrument, sensor performance was 

compromised by an out-of-band leak that, if not discovered during system-level testing, would 

have limited the value of the data produced by the sensor. 

When component-level measurements are used to estimate system-level calibration for a sensor, 

the likely increase in uncertainty and the risk of errors greater than 20 % must be recognized and 

deemed acceptable. A minimum amount of system-level measurements should always be planned 

to verify the component-level measurements. 

Hubble Space Telescope 

A famous example of the importance of pre-launch, system-level calibration is the focus 

of the Hubble Space Telescope. Component-level testing of the primary telescope mirror 

was performed using custom equipment, and suggested that the optics were properly 

manufactured. No optical performance tests were made at higher levels of assembly. 

However, during on-orbit checkouts, it was discovered that the telescope could not be 

correctly focused because of a flaw in the optics (NASA-TM-103443, November 1990). 

This failure was traced to flaws in the custom equipment used for component-level 

manufacturing and testing, and the fact that complete reliance was placed on using a 

single test to verify the system. It is likely that this anomaly would have been identified 

during pre-launch system-level calibration, potentially saving millions of program dollars. 

SABER Component Measurements 

Courtesy of 

NASA 

An example of the trade-off between component level versus system level 

measurements is presented in “Component Level Prediction versus System Level 

Measurement of SABER Relative Spectral Response” (Hansen et al., 2003), where 

differences between component-level and system-level measurements of the bandwidth 

of 10 bands in the SABER instrument were compared. In this analysis, the bandwidths 

measured at the component level and system level for eight of the SABER bands were 

consistent within 3.3 %. However, the remaining two bands showed greater differences: 

one showed a difference of 4.5 % between component- and system-level 

measurements, and one showed a difference of 23.5 %. For the bands showing large 

discrepancies between component-level and system-level measurements, accurate 

end-to-end spectral measurements were essential to achieve correct understanding of 

the instrument science data. 

SPIRIT III Out-of-Band Leakage 

During SPIRIT III engineering calibration, a significant out-of-band spectral leak 

caused by the Stierwalt effect was discovered (Fuqua et al., 2003). This issue was 

resolved by adding a sapphire blocking filter into the sensor configuration (SDL/98-

033). Had this problem not been resolved, the measurement uncertainty would have 

been unacceptably large to accommodate this non-ideal sensor performance issue, 

limiting the value of the data produced by the sensor. 
9 

 Both Pre- and Post-Launch Calibrations are Critical to a Successful Calibration 

EO sensor calibration usually involves both a pre-launch and 

a post-launch segment. Understanding a sensor’s properties 

and its changes after launch is essential to generating high 

quality data products. Instrument issues such as optics 

degradation due to long-term exposure to ultraviolet (UV) 

radiation can cause changes in the instrument calibration 

over time. Unless accounted for, these changes will falsely 

appear as changes in the object being measured. Errors as 

small as several nanometers in spectral position knowledge 

due to improper or incomplete characterization of in-band or 

out-of-band contributions to the integrated sensor signal 

have been known to bias cloud height science products 

(Mlynczak et al., 2013). 

Pre-launch calibration, or ground calibration, provides the 

capability to perform tests in a controlled environment with 

known sources that cannot be duplicated on-orbit, and has 

the advantage of discovering and resolving anomalies prior 

to launch. Measurements made during pre-launch calibration 

are used to verify proper instrument operation, to quantify 

calibration equation and radiometric model parameters, and 

to estimate measurement uncertainties. Pre-launch 

calibration is essential to understanding sensor performance 

nuances so that they can be addressed and understood before 

launch. Options to correct unexpected sensor performance 

anomalies after launch are limited and expensive. 

Post-launch testing, or on-orbit calibration, has the 

advantage of being performed under true flight conditions 

rather than simulated flight-like conditions. The goals of 

post-launch calibration are to measure parameters that 

cannot be measured on the ground, maintain calibration 

throughout a sensor’s operational lifetime, quantify 

calibration uncertainty, and update calibration coefficients if 

necessary to meet measurement requirements. 

However, on-orbit time that is dedicated to calibration is limited (Tansock et al., 2004). It is usually 

impractical to perform all of the on-orbit measurement combinations needed to fully calibrate and 

understand the sensor. A complete and sufficiently bounded pre-launch calibration will minimize 

the satellite operational time required for post-launch calibration. 

While every effort should be made to measure and verify system parameters during pre-launch 

calibration, there are some parameters for which on-orbit sources may enable a better 

measurement, including pointing and geometrical parameters such as point response function 

(PRF), distortion mapping, pixel instantaneous field of view (IFOV), and off-axis scatter, where 

stars can provide ideal point sources, the Moon can provide a bright large area source, and ground 

test sites can provide uniform and spatial calibration targets. Even though on-orbit data for these 

Obtaining Accurate Ocean 

Color Measurements 

For ocean color, an accuracy of about 0.5 

% is needed for TOA radiance retrieval. To 

achieve this level of accuracy, the 

contributions of artifacts such as 

polarization, straylight, and non-linearity 

need to be on the order of 0.1 %. Most of 

these effects can only be characterized prelaunch. However, the sensor radiometric 

gain is often derived on-orbit. Therefore, 

both pre-launch characterization and onorbit calibration are critically important for 

ocean color remote sensing. 

The Bering Strait (MODIS, 7/8/10) 

http://oceancolor.gsfc.nasa.gov/cgi/image_ 

archive.cgi?c=COASTAL 
10 

parameters may provide a better result, prudence dictates that the best possible pre-launch 

measurements should be performed to verify and validate system performance and identify design 

and assembly “gotchas” before launch. 

2.2 CALIBRATION RESPONSIVITY DOMAINS 

Sensor performance is dependent on relationships between multiple responsivity domains. For 

characterizing EO systems, five domains should be considered: radiometric, spatial, spectral, 

temporal, and polarization. The goal of calibration is to characterize each of these responsivity 

domains independently. In an idealized setting, there would be no interactions between parameters 

and each responsivity domain could be characterized independently. In reality, there are 

interactions and much of the work of calibration goes to understanding and minimizing these 

interactions. Calibration planning during the sensor design phase helps to ensure that the design is 

compatible with the planned calibration measurements. (Hansen et al., 2011; Tansock et al., 2004). 

The radiometric responsivity domain describes the sensor’s response to electro-magnetic radiant 

energy. Calibration parameters that describe the radiometric response of a sensor include radiance 

and/or irradiance calibration coefficients, response linearity, array detector-to-detector response 

uniformity, nominal and outlying detector identification, and radiometric calibration of internal 

calibration sources. Knowledge of a sensor’s radiometric domain is key to understanding how well 

the sensor responds on an absolute and/or relative scale. Determining the absolute radiance 

responsivity provides an understanding of the measurement physics of the sensor, and is necessary 

for a complete calibration (Wyatt 1978; Tansock et al., 2004). Absolute radiance measurements 

are used to verify that specifications have been achieved relative to an internationally recognized 

standard and to assess spectral purity (Wyatt 1978). 

The spatial domain describes how measurements are affected by an object being located at a 

different spot in the sensor’s field of view. It includes the position of the detector with respect to 

the instrument boresight, the detector’s effective field of view (EFOV), and the detector’s scatter 

due to optical scatter and electrical crosstalk. These measurements enable experimenters to point 

the detectors at a desired source and to model detector responses to objects that are inside and 

outside the detector’s direct line of sight (Wyatt 1991). 

The spectral domain measures the sensor’s response to radiation as a function of wavelength, and 

describes how the sensor responds to sources of different wavelengths. It is characterized by the 

sensor spectral response or the relative spectral response (RSR) parameter, which measures the 

normalized sensor response both in and out of the intended bandpass of the sensor. Knowledge of 

a sensor’s spectral domain provides an understanding of the sensor’s response to various spectral 

sources, leading to an absolute calibration for the sensor. 

The temporal domain describes the sensor’s response to a well characterized, stable source 

throughout the mission life, including both pre- and post-launch operations. The temporal domain 

measurements consists of sensor repeatability for a specified time period (i.e., short, medium, 

and/or long) and amplitude response as a function of optical input temporal frequency. 

Understanding the sensor temporal frequency response is particularly important when the source 

being measured by the sensor has a time-varying radiometric component. 

The polarization domain describes the sensitivity of the sensor to polarized light. This domain 

becomes important when the sensor design induces polarization sensitivity and the mission targets 

and/or backgrounds contain polarized light. 
11 

2.3 CALIBRATION REQUIREMENTS ACROSS DISCIPLINES 

Sensor calibration requirements vary with the application of the sensor. The parameters to be 

measured during calibration are highly dependent on the instrument and the mission. Parameters 

that are important for one instrument may be irrelevant for another. For example, sensors for Earth 

and atmospheric science applications may be affected by atmospheric effects; thus, the sensor must 

be characterized for properties such as polarization and spectral responses to account for 

atmosphere polarization or spectral emission effects. For DoD applications that frequently observe 

above the horizon of the earth, optical performance parameters such as off-axis rejection or image 

quality characterizations that impact point source resolution may be more important. This section 

discusses calibration considerations for these different applications. 

Sensors for Earth and atmospheric science applications, as well as DoD applications, generally use 

passive remote sensing techniques rather than active techniques involving radars or lasers/lidars. 

 Earth Sciences 

Earth-observing sensors are designed for a broad range of studies of the Earth's land, ocean, and 

atmosphere, and are based on the needs of the science and user community to either enhance 

existing sensor data records or to advance new science and research applications. The Moderate 

Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imager Radiometer 

Suite (VIIRS) are examples of Earth-observing sensors that measure the Earth's radiance in a wide 

spectral range, and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) is an example of a 

sensor that measures ocean color. 

For ocean color, measurement uncertainty of about 0.5 % is needed for top of atmosphere (TOA) 

radiance retrieval. To achieve this level of accuracy, the uncertainty contributions of artifacts such 

as polarization, straylight, and non-linearity need to be on the order of 0.1 %, and both pre- and 

post-launch calibration are required to achieve this accuracy. 

The calibration requirements for Earth-observing sensors vary depending on the specific sensor 

application. Some observations, such as changes in the Earth’s surface properties are often 

determined using data from multiple sensors, which requires that the spectral characteristics and 

traceability of the sensors be thoroughly understood to permit accurate cross-comparison of data. 

These sensors must be consistently calibrated with the same traceability, ideally with the same 

calibration sources and techniques. 
12 

Calibration Traceability using Data from Multiple Sensors 

The same solar diffuser design was used to maintain calibration traceability for the 

MODIS instruments on both the NASA Terra and Aqua Earth Observing System (EOS) 

satellites, as well as the S-NPP/JPSS VIIRS reflective solar bands. 

(Courtesy of NASA)

A key element addressing the consistency of merged data quality is sensor spatial/spectral 

performance in the form of band-to-band pointing alignment, as many of the science products are 

spatially geolocated but are generated using more than one spectral band. 

For land remote sensing, the data must be corrected for atmospheric effects, which requires well 

calibrated and characterized sensor properties such as polarization sensitivity and spectral 

responses. Otherwise, the derived products could be biased. Most land studies monitor changes in 

the surface properties, and the measurement products derived from these properties are very 

sensitive to changes in the sensor’s calibration. For example, drought monitoring relies on looking 

at yearly changes in vegetation using a baseline that typically contains over 10 years of data (e.g., 

the Normalized Difference Vegetation Index (NDVI)); undetected changes in the sensor’s 

calibration could be incorrectly interpreted as vegetation stress or drought (Wang et al., 2012). 
13 

 Atmospheric Sciences 

Atmospheric science EO sensors use 

passive remote sensing techniques to 

measure radiance emitted by the Earth and 

atmosphere, or radiance from the Sun that 

is reflected by the Earth and the 

atmosphere. 

Analysis of radiance measurements 

generally falls into two categories: 

analysis for the purpose of deriving 

atmospheric state profiles of temperature 

and minor constituents (atmospheric 

sounding), and analysis for deriving 

properties related to the energy balance of 

the Earth system. Profiling involves 

retrieving atmospheric structure that is 

consistent with the measured radiance 

while requiring retrieved temperature and 

constituent abundances that are physically 

realistic. The process of “inverting” the 

radiative transfer equation to solve for the 

atmospheric structure may be highly nonlinear and small, often subtle errors or 

uncertainties in the measured radiance (or other instrument characterization uncertainties), may 

render the retrieved profile useless, or at least, non-physical. Applications of measured radiance to 

Earth’s energy balance often do not involve non-linear radiative transfer but nonetheless depend 

critically on knowing absolute calibration and its time variation. 

Spectral radiance is measured by Fourier transform (W/(m2

·sr·cm-1)) or grating spectrometers 

(W/(m2·sr μm)). Spectrally integrated radiance (W/(m2·sr)) is measured by narrow-band and 

broadband radiometers. Some of these instruments observe in the nadir (including those that scan 

cross-track) for the purpose of deriving properties at Earth’s surface, in the troposphere, and in the 

lower stratosphere. Other instruments observe the Earth’s limb for deriving the composition and 

structure of the stratosphere, mesosphere, and thermosphere. 

Accurate radiometric calibration and instrument characterization are essential for generating high 

quality data products from instruments used to remotely sense the atmosphere. Atmospheric 

properties such as water vapor content, cloud coverage, cloud and aerosol optical depth, cloud 

height, and cloud particle size and phase impact the Earth’s radiation budget. These products are 

generated using spectral bands in both reflective solar and thermal emissive regions. The quality 

of the data products relies on the spectral band radiometric bias and precision, which in turn rely 

on accurate characterization of sensor on-board radiometric sources and optical elements both in 

pre-launch and on-orbit phases of the sensor lifetime. 

Passive Sensor System 

Passive remote sensing techniques rely on natural radiation 

emitted or reflected by an object or area of interest. As shown 

in the following illustration, the land (or sea) feature is 

illuminated by the sun, providing energy for reflected/emitted 

radiance that is then measured by the remote sensing 

system. The measurement data are then transmitted to the 

ground station for data processing and science analysis.
14 

Calibration to achieve relative uncertainty of less than 1 

% of the measured radiance is essential for the accurate 

retrieval of atmospheric temperature. The science 

requirement is for atmospheric temperatures to be 

measured with uncertainty less than 1 K. Infrared 

radiometers and infrared spectrometers determine 

temperature by measuring radiance in the carbon 

dioxide (CO2) bands near 15 μm (667 cm-1). The 1 K 

uncertainty in the Planck function (due to its sensitivity 

to temperature) results in uncertainty in the radiance 

ranging from 1.36 % at 270 K to 2.2 % at 210 K). Given 

that the sum of all error sources must be less than 1 K, it 

is clear that the relative uncertainty of the absolute 

radiometric calibration must be less than 1 % over the 

range of radiances to be observed. 

 DoD Applications 

The DoD uses many types of sensors in multiple 

constellations of satellites, on drones and aircraft, and on 

the ground for a variety of applications. These 

applications include weather characterization in support 

of the warfighter, battle-space characterization, 

monitoring of missile launches (missile warning) worldwide, theater missile warning (in support of the 

warfighter), and for technical intelligence (TI). TI can 

span a large range of applications, including assessing 

the movement of resources for political situations, and 

ascertaining the capabilities of an industrial area, such as how many missiles or vehicles are being 

produced in a given plant. 

In addition, data from existing DoD systems are used in the architecture studies for future systems. 

These architecture studies rely on existing data to establish the requirements for sensitivity, spatial 

resolution, timeliness, spectral capability (filter bandpass locations and widths), absolute accuracy, 

and repeatability (precision). The expected or predicted quality of the data will also play a key 

role in establishing the number of sensors required for future missions, a fundamental property of 

the system that will be a major acquisition cost and schedule driver. For all of these applications, 

the calibration of the sensors will affect the quality of the interpretation of the data, and thus the 

success or failure of the performance of the critical task, not just the success or failure of a 

particular sensor or program. 

Atmospheric Altitude vs. 

Temperature 

http://saberoutreach.hamptonu.edu/overview. 

html (Chart by R. Bradley Pierce, NASA LaRC) 
15 

In some cases, multiple DoD sensors will be used for the same purpose, such as using different 

constellations of sensors to perform missile warning functions. Missile warning includes not only 

the simple detection of a launch, but also missile typing, launch point calculation, and impact point 

prediction, tasks that require a collection of missile trajectories and intensity profiles. This 

information ultimately comes from the data obtained over many years of observing multiple 

launches, and is used both as empirical evidence of the behavior 

of a class or type of missile and to validate the models for the 

properties of each type of missile. An issue that arises when 

multiple sensors, sensor types, and constellations are used for a 

common application is that if the calibrations of the various 

instruments are based on a different set of standards, the resulting 

data will represent the signatures on a different scale. 

Ultimately, National Metrology Institutes (NMIs), such as NIST 

in the USA, provides the absolute reference (“truth”) for 

radiometric intensity calibrations through sources or transfer 

radiometers. However, even with NIST-traceable sources, other 

issues exist. The algorithms that extract the information on a 

point target or provide calibrated intensities in a radiometrically 

calibrated scene can affect the intensities reported. In addition, 

when different programs with different sensors use different 

approaches to extracting information, the result may be a 

disarray of apparently conflicting information. For example, if 

the heat given off by a small power plant or factory is extracted 

from a complex urban scene, the manner used to perform the 

extraction can affect the values reported. If an image enhancement technique that does not preserve 

the energy in the scene is used, the result that is derived can be incorrect. Therefore, when results 

from different programs are compared, the findings may appear to be contradictory or in conflict. 

With the correct calibration of not only the sensors’ responses, but also of the impact of the 

algorithms used to perform the task, the results can come into agreement. Data fusion can then 

focus on the content of the data to address a problem at hand instead of a debate about why the 

different programs are obtaining apparently conflicting or contradictory results. The following 

example illustrates the importance of calibration in a DoD application. 

Plume Signature Models 

In the early 1980s, the intensity of 

missile plumes and the models for 

missile plumes were significantly 

different. It now appears that at least 

part of the discrepancy can be traced 

to an error in the absolute calibration 

of a constellation of space-based 

sensors. Currently, the models and 

data are in very good agreement, 

which has improved the ability to 

predict the geometrical behavior of 

the plume emission, the interaction 

of the plume signature with the 

atmosphere (the portion of the plume 

radiation that will be transmitted to 

the sensor along a given line of 

sight), and to more quickly develop 

an accurate model for a new missile.
16 

DoD Application 

The measurement geometry of the MSX satellite viewing a re-entry vehicle through the Earth 

limb. All aspects of calibration, including atmospheric effects, need to be applied in this scenario 

to accurately measure and identify the target. Information obtained from calibration is used in the 

calibration equation to put results on an absolute scale. Measurement results can then be 

compared with those of other instruments to lower uncertainty and increase accuracy of the 

knowledge about the target. 

Reprinted with permission ©The Johns Hopkins University Applied Physics Laboratory 

When performing calibrations and deriving calibration products, the environment in which the 

sensor is being tested needs to be representative of that which the sensor will experience during 

use. This “test as you fly” axiom (Datla et al., 2011; Russell, 2008) implies that instruments should 

be calibrated under the same environmental conditions as expected during operation. Similarly, 

the calibration sources, targets, and methodologies will need to be representative of the 

applications. If a modulation transfer function or point response function will be used for target 

extraction, the calibration data should be obtained in the same manner as expected during 

operation, using targets or scenes that are representative of those that will be studied in the real 

world. If signatures will be measured after transmission through the atmosphere, the RSR must 

be accurately characterized so that the calculations of atmospheric transmission effects will be 

included correctly. 

When the “test as you fly” approach is used for all the sensors involved, even though they are from 

different programs and have had varied approaches to calibration used in their manufacture, a 

coherent, consistent picture of the results can be obtained. This has been demonstrated with two 

recently launched sensors that have dramatically different filter shapes at nominally the same 

wavelengths. In spite of this, the proper application of the measured and modeled RSRs for the 

two different systems results in consistent target intensity reports. 
17 

CALIBRATION TRACEABILITY,

MEASUREMENT UNCERTAINTY, AND 

VERIFICATION AND VALIDATION

As previously described (Section 2.1.3), traceability, 

measurement uncertainty, and V&V work together to provide 

confidence in the sensor data. Traceability refers to the ability to 

track a measurement to a known standard unit of measurement 

within a given measurement uncertainty. V&V ensures that the 

instrument operates as designed and produces relevant data by 

proven processes and standards. All three are required to obtain 

reliable data that can be directly compared to model predictions 

and results from other instruments. This section further discusses 

these properties. 

3.1 TRACEABILITY 

Traceability can be defined as an unbroken record of 

documentation or an unbroken chain of measurements, and their 

associated uncertainties (www.nist.gov). The principle benefits of traceability for EO sensor 

calibration are to improve the likelihood that data products provide a quantitative description of 

the measured parameter, are invariant with time, and are sufficiently robust for regulator, policy, 

or commercial decisions (Fox, 2004). 

The definition of traceability as it applies to remote sensing has evolved over the years. In 2008, 

the International Vocabulary of Metrology – Basic and General Concepts and Associated Terms 

(VIM) reworded the term ‘traceability’ to ‘metrological traceability’, and defined it explicitly for 

metrology as the property of a measurement result whereby the result can be related to a reference 

through a documented unbroken chain of calibrations, each contributing to the measurement 

uncertainty (JCGM 200:2012). The VIM was developed by the Joint Committee for Guides in 

Metrology (JCGM), which was formed in 1997, and was comprised of metrologists from the 

world’s major standards organizations, including the International Organization for 

Standardization (ISO), the International Electrotechnical Commission (IEC), the International 

Organization of Legal Metrology (OIML), and the International Bureau of Weights and Measures 

(BIPM). In addition to the VIM, this committee also developed the Guide to the Expression of 

Uncertainty in Measurement (GUM) (JCGM 100:2008) to address metrological traceability. A 

discussion of the applicability of the VIM definition to remote sensing is provided at the end of 

this chapter. 
18 

NIST adopted the approach of GUM and in 1994, NIST Technical Note 1297 (Taylor and Kuyatt 

1994) was accepted and incorporated into the NIST administrative manual to be followed for all 

its measurements and services. In 2008 the word ‘traceability’ was changed in NIST’s 

documentation to ‘metrological traceability’. A full description of NIST’s policy on the subject 

can be found at www.nist.gov/traceability/. 

 Metrological Traceability 

Metrological traceability is a property of the result of a measurement, and not attributed to an 

instrument, the calibration report, or a laboratory. To substantiate a claim of metrological 

traceability, the provider of a measurement result must document the measurement process or 

system used to establish the claim and provide a description of the chain of calibrations that were 

used to establish a connection to a particular specified reference. 

The following elements are common to all valid statements or claims of metrological traceability 

(http://www.nist.gov/traceability/, 2013): 

 A clearly defined particular quantity that has been measured 

 A complete description of the measurement system or working standard used to perform 

the measurement 

 A stated measurement result, which includes a documented uncertainty 

 A complete specification of the reference at the time the measurement system or working 

standard was compared to it 

 An internal measurement assurance program for establishing the status of the measurement 

system, specified reference, or working standard at all times pertinent to the claim of 

metrological traceability 

The comparison of measurement results with stated references needs to be reexamined 

periodically, as well as the criteria for judging if a comparison is successful. The consideration for 

periodic recalibration and its success should include assessing the integrity of the traceability 

chain, which is dependent on many things, including the measurement requirements, the needs of 

the client, the dependability of the equipment and standards, and the environmental effects (Ehrlich 

and Rasberry 1998). 

NIST’s quality system (http://www.nist.gov/qualitysystem/index.cfm, 2013) requires the use of 

quality assurance practices to ensure the validity of calibration and reference material results and 

their uncertainties. Practices include: 

 Comparison of repeat measurements/calibrations over many time intervals 

 Comparison of results obtained using multiple reference standards 

 Use of check standards and control charts 

 Use of redundant experimental designs 

 Comparison of results obtained using two are more differing measurement approaches 

 Results of national and international comparisons, including key comparisons between 

NMIs organized by International Committee for Weights and Measures (CIPM), the parent 

body of BIPM in Paris 

 Results of proficiency tests 

 Correlation of results for different characteristics of an item 
19 

 Metrological Traceability and Remote Sensing Measurements 

Metrological issues in remote sensing radiometric applications focus on assuring that repeated 

measurements of the same quantity are metrologically comparable. Data sets collected by multiple 

sensors are often used together for various measurement functions, including studying changes in 

the Earth’s atmospheric and surface properties, and DoD applications. Use of multiple sensor data 

is rapidly increasing with the refinement in EO sensors and the need for more global data. 

Therefore, it has become extremely important that those data sets are calibrated with the same 

traceability and that differences between instruments are clearly understood. 

Measurements widely separated in time and space can be compared if they are traceable to the 

same reference, which is stable in time and space. The remote sensing community has been 

working toward this goal for the past 20 years. The experience at NASA in the measurement of 

top of atmosphere (TOA) total solar irradiance (TSI), beginning with the Earth Radiation Budget 

(ERB) instrument on the NIMBUS 6 satellite in 1975, provides an example of this work (shown 

in the following example). Early measurements of TSI resulted in discrepancies several times the 

uncertainties of the measurements involved. Improvements in TSI measurements since 1975 have 

enabled measurement results that are now consistent within the uncertainties of the measurements. 

Earth Radiation Budget (ERB) Instrument 

The ERB instrument, launched in 1975 on the NIMBUS 6 satellite, measured total solar 

irradiance (TSI) to be 1389 W/m2, a value 1.5 % higher than expected from ground 

measurements corrected for atmospheric effects. NASA employed a team of engineers and 

scientists, including metrologists from NIST, to resolve the issue. The following shows the 

team working on the calibration effort. 

(Applied Optics/ Vol. 16, No. 10/October 1977) 

Based on their recommendation, several electrical substitution-type radiometers (ESR) 

were built and flown on a rocket to TOA to measure TSI. The ESR built by Jet Propulsion 

Laboratory (JPL), which was considered to be adequately calibrated, measured a TSI value 

of 1367 W/m2, which was in the expected range (Duncan et al., 1977). NASA then flew the 

JPL ESR called the Active Cavity Radiometer Irradiance Monitor (ACRIM) in a series of TSI 

measurements from space. This study found that on orbit, the radiometers each measured 

values that differed by more than the quoted radiometer uncertainty. In addition, 

radiometers from other laboratories differed from each other. To resolve the issue, NASA 

funded the Laboratory of Atmospheric and Space Physics (LASP) to build an ESR of a 

slightly different design for TSI measurements. This radiometer, flown in 2003, measured 

lower TSI values (1361 W/m2) than the ACRIM and other radiometer measurements. 
20 

ERB Instrument (cont.) 

The TSI climate data record, shown in the following, now spans 34 years. Instrument offsets 

are unresolved calibration differences, much of which are due to internal instrument scatter 

comparison of the various missions (Kopp and Lean 2011). 

TSI Climate Data Record (http://spot.colorado.edu/~koppg/TSI/, 

Image G. Kopp, 10 Jul 2014)) 

LASP has recently acquired an absolute cryogenic radiometer and built a facility under 

NASA funding to perform SI traceable calibrations of TSI radiometers. The cryogenic 

radiometer measures the optical power in watts by comparing the optical heating of a cavity 

in cryogenic conditions with electrical power to achieve the same heating when the optical 

power is shut off. The cryogenic conditions assure that there are no other heating effects, 

and provide very high accuracy equivalence to the electrical power measured in SI units. 

The results showed various systematic effects in the legacy instruments to be corrected for 

irradiance measurements, and there is now consistency reported in the TSI measurements 

from space (Kopp et al., 2012). 

Système International (SI) Traceability 

The need for pre-launch SI traceability of space-bound sensors to establish metrological 

traceability for space-based measurements has been emphasized by workshop participants 

sponsored by National Polar-Orbiting Operational Environmental Satellite System (NPOESS), 

National Oceanic and Atmospheric Administration (NOAA), NASA, and NIST over the last 

decade (Ohring et al., 2004; Ohring, 2007; Cooksey and Datla, 2011). The National Calibration 

Center (NCC) at NOAA was established out of the recommendations of the Achieving Satellite 

Instrument Calibration for Climate Change (ASIC3) workshop held in Lansdowne, VA, May16-

18, 2006. 
21 

Various workshops also pointed out the need for on orbit SI traceability and recommended 

benchmark satellite missions with SI traceable standards. Recommendations from these 

workshops included characterization of the Moon as an SI traceable standard of high accuracy for 

satellite sensors to view for on orbit calibration (Cooksey and Datla, 2011). 

While pre-launch activities help evaluate the extent to which the instrument meets specifications, 

it is in the post-launch environment that the issue of traceability to SI units becomes critical. This 

is particularly true for post-launch calibration of satellite sensors in the visible and near infrared 

(NIR), where there are many examples of pre-launch calibration coefficients needing revision due 

to changes in the sensor caused by storage and launch into orbit (Fox, 2004). 

The CLARREO mission by NASA is currently in the research phase to carry SI traceable standards 

for calibrations of its sensors and provide bench mark measurements. This project explains SI 

traceability (for remote sensing) as a technique used for satellite observations that link a satellite’s 

measurements to internationally recognized measurement standards. Using this technique, 

measurements from different satellites may be pooled into one long-term observational record that 

is free from small drifts in measurements due to slight differences between satellites 

(http://clarreo.larc.nasa.gov/about-SITrace.html, 2014). 

Internationally, the Committee on Earth Observing Satellites (CEOS) and the Group on Earth 

Observations (GEO) organized workshops on traceability for remote sensing measurements. 

Through these workshops, GEO developed a guide to establishing quantitative evidence of 

traceability to underpin the quality assurance requirements of GEO (Fox, 2010). The World 

Meteorological Organization (WMO) and BIPM held a joint workshop in 2010 on measurement 

challenges for global observation systems for climate change monitoring, emphasizing the 

importance of traceability, stability and uncertainty in remote sensing measurements. The results 

from this workshop are reported in IOM-Report No. 105 (2010). 
22 

APPLICABILITY OF THE VIM TRACEABILITY 

DEFINITION TO REMOTE SENSING 

The document “Vocabulary for Metrology” (VIM) (JCGM 200:2012) by the International Bureau of Weights and 

Measures (Bureau International des Poids et Measures (BIPM)) reworded traceability as “metrological traceability” in 

2008 and defined it explicitly for metrology as follows: property of a measurement result whereby the result can be 

related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement 

uncertainty. 

NOTES 

1. For this definition, a 'reference' can be a definition of a measurement unit through its practical realization, or a 

measurement procedure including the measurement unit for a non-ordinal quantity, or a measurement 

standard. 

2. Metrological traceability requires an established calibration hierarchy. 

3. Specification of the reference must include the time at which this reference was used in establishing the 

calibration hierarchy, along with any other relevant metrological information about the reference, such as when 

the first calibration in the calibration hierarchy was performed. 

4. For measurements with more than one input quantity in the measurement model, each of the input quantity 

values should itself be metrologically traceable and the calibration hierarchy involved may form a branched 

structure or a network. The effort involved in establishing metrological traceability for each input quantity value 

should be commensurate with its relative contribution to the measurement result. 

5. Metrological traceability of a measurement result does not ensure that the measurement uncertainty is 

adequate for a given purpose or that there is an absence of mistakes. 

6. A comparison between two measurement standards may be viewed as a calibration if the comparison is used 

to check and, if necessary, correct the quantity value and measurement uncertainty attributed to one of the 

measurement standards. 

7. The International Laboratory Accreditation Cooperation (ILAC) considers the elements for confirming 

metrological traceability to be an unbroken metrological traceability chain to an international measurement 

standard or a national measurement standard, a documented measurement uncertainty, a documented 

measurement procedure, accredited technical competence, metrological traceability to the SI, and calibration 

intervals (see ILAC P-10:2002). 

8. The abbreviated term "traceability" is sometimes used to mean 'metrological traceability' as well as other 

concepts, such as 'sample traceability' or 'document traceability' or 'instrument traceability' or 'material 

traceability', where the history ("trace") of an item is meant. Therefore, the full term of "metrological 

traceability" is preferred if there is any risk of confusion. 

The notes above are part of the VIM (JCGM 200:2012) definition of “metrological traceability”. They are reproduced 

verbatim to discuss their applicability to remote sensing measurements. Note 1: The “reference” can be the unit of 

measurement for example, Watt/m2 for irradiance, Watt/ m2 sr for radiance and kelvin (K) for temperature. If the 

measurements of these quantities are made in absolute units (SI), those measurements become the ‘reference” for 

relating to other measurements. A measurement standard calibrated in the SI units could be used as a reference for 

establishing the measurements as SI traceable. The reference can also be a measurement procedure. For example a 

commonly accepted standard model (algorithm) for weather prediction would be a reference for evaluating other model 

predictions or for comparing with observations. This applies to derived products from level1b measurements for 

example: sea surface temperature, aerosol optical depth, vegetation index etc. Note 2: The calibration hierarchy is 

established to create the documented unbroken chain of calibrations to relate to the reference. The hierarchy for 

physical measurements starts at the NMI like NIST for USA at the top of the hierarchy and accredited reference 

laboratories as secondary in the chain of calibrations for establishing the uncertainty budget. For other references such 

as measurement procedures it becomes necessary to arrive at a commonly accepted hierarchy to establish the 

uncertainty budget. Note 3: As references may change with time the calibration hierarchy should have a time tag 

attached to help future updates. Note 4: For example, the measurement equation of a satellite sensor has many input 

quantities such as reflectance, transmittance and emittance of components, responsivity of detectors, etc., and the 

metrological traceability chain applies to all input quantities. Each input quantity contributes to the relative uncertainty 

budget with its own calibration hierarchy and propagation of uncertainties based on the measurement equation. Note 5 

and Note 6: These are self explanatory. Note 7: ILAC is the acronym for international laboratory accreditation 

cooperation which is an international body on laboratory accreditation. ILAC approved and adopted the VIM and the 

associated Guide to the Expression of Uncertainty in Measurement (GUM) for metrology (JCGM 100:2008). Note 8: 

The full term “metrological traceability” is the preferred term for usage compared to the term “traceability” itself as there 

are other concepts attributed to “traceability”. 
23 

NOAA NATIONAL CALIBRATION CENTER (NOAA NCC) 

The NOAA National Calibration Center (NCC) was established in 2011 at the NOAA Center for Satellite 

Applications and Research (STAR). Its purpose is to facilitate improved accuracy of NOAA's weather, 

climate, and ocean models through sharing of technical practices for fine tuning remotely-sensed data from 

environmental satellites among different programs and agencies. NCC's mission is to provide common 

standards and methodology for the user community as well as encourage communication though a centralized 

Calibration Knowledge Base. This practice provides support to NOAA's satellite programs by enforcing 

stricter and more widespread quality control on satellite data from the Global Earth Observation System of 

Systems (GEOSS), which will improve efficiency and reduce costs as the community strives to meet the 

growing needs for high quality satellite data. NCC is established as a virtual center accessible on line for 

exchanging information about current and past NOAA satellite data research and analysis and to provide a 

repository of publications on calibration of satellite sensors, standards and uncertainty analysis. Its web site is 

http://ncc.nesdis.noaa.gov/, with the banner shown above. 

The home page on the current website provides access to the calibration knowledge base. The structure of the 

calibration knowledge base page is as follows. It has terms of reference section that leads to key publications 

on the standards and calibration methodologies for satellite sensors from International Standards 

Organizations, NIST, NASA and NOAA. The focus of NCC will be to support the Calibration and Validation 

of the operational sensors from pre-launch to post launch and into operations. In this regard NCC has been 

supporting the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPP and JPSS satellites, as well 

as the Advanced Baseline Imager on the next generation geostationary satellite GOES-R. The calibration 

knowledge base for VIIRS and ABI have been used extensively by users worldwide for instrument 

characterization and performance related information, such as calibration/validation data, documentation, 

software, data quality, instrument event log database, lunar calibration, instrument performance anomalies, 

time series at more than 30 validation sites, and inter-satellite calibration. This page also provides choices for 

information on other satellites such as the Joint Altimetry Satellite Oceanography Network (JASON). Other 

features include frequently asked questions (FAQ) and Tools section; for example, a Planck calculator can be 

used to calculate the spectral radiance value based on the choice of temperature and wavelength in different 

units commonly used in remote sensing. 
24 

3.2 MEASUREMENT UNCERTAINTY 

As previously discussed, metrological traceability is defined as the 

property of a measurement result whereby the result can be related to 

a reference through a documented unbroken chain of calibrations, 

each contributing to the measurement uncertainty (JCGM 200:2012). 

Uncertainties associated with radiometric measurements result from 

many factors, including the effects of noise, nonlinearity, non-uniform detector array response, 

non-ideal spectral and spatial responsivity, and standard calibration source uncertainty. (Wyatt et 

al., 1998). The extent of these errors depends upon the nature of the target source and the 

background as well as the sensor characteristics. Finally, uncertainties are associated with the 

calibration procedures and reference standards themselves (Wyatt 1991). 

A comprehensive uncertainty budget should be established early in the calibration planning 

process. This uncertainty budget can be used as a tool to identify dominant sources of uncertainty 

and allow for comparison to sensor requirements. The effort required to quantify a comprehensive 

estimate of uncertainty should not be underestimated. It is not uncommon to spend more time 

estimating uncertainty than deriving calibration results. 

The uncertainty budget should be maintained up-to-date with the most recent sensor performance 

and calibration source uncertainty estimates as they become available. This allows for estimates 

of uncertainty to be tracked against sensor requirements and for independent assessment if new 

uncertainty information becomes available at a later date. Documentation of uncertainty analysis 

must provide sufficient detail to increase confidence that all sources of uncertainty were considered 

and allow the reader to make an independent assessment if new uncertainty information becomes 

available at a later date (Tansock et al., 2004; Wyatt et al., 1998). The following should be 

considered for the uncertainty analysis: 

 Identify and reduce the largest uncertainties to give the smallest overall uncertainty 

 Report results in standard units and use established guidelines for estimating 

uncertainty 

 Recognize the need for other programs to use your results 

 For all uncertainty estimates, report detailed logic and supporting background so that 

reassessment at a later date is possible 

 Be realistic when estimating uncertainty; no one ultimately benefits by providing an 

overly optimistic level of uncertainty 

 Establish multiple traceability paths to physical standards to verify or help quantify 

uncertainty estimates 

Measurement results cannot be compared with reference standards unless they are reported in 

standard units and include an estimate of uncertainty. A discussion on estimating uncertainty is 

provided at the end of this chapter. Guidelines for estimating uncertainty have been established 

(JCGM 100:2008 GUM 2008; Taylor et al., 1994; Wyatt et al., 1998; ISO 1993). When possible, 

it is recommended that calibration results be expressed in the International System of Units (SI) 

(Pollock et al., 2003) (Section 3.1). 

Calibration measurements 

must be traceable within a 

specified uncertainty. 
25 

ESTIMATING UNCERTAINTY 

The NIST policy on traceability states that measurements results are not traceable unless the measurements can be 

clearly related to the national or international standard through an unbroken chain of measurements, all having clearly 

documented uncertainties (see Section 3.1). 

The first step in any uncertainty analysis is the statement of the measurement and/or calibration equation, as described in 

Section 4.1.6. Without an equation, the uncertainties cannot be propagated since the sensitivity coefficients cannot be 

determined. A measurement or calibration equation in the most general form is: 

  1, , m y fx x   

where y is the measurand and xi is the input parameter. 

The procedure for uncertainty analysis generally consists of the following: 

 Express the functional relationship between y and xi

 Determine the values of xi (by evaluation of the sensor design, statistical analysis, or other means) 

 Evaluate the standard uncertainty u(xi) of each input xi

 Determine the value of y using the functional relationship 

 Determine the combined standard uncertainty uc(y) from the standard uncertainties associated with each value 

of xi

 Calculate the expanded standard uncertainty (U) as the combined standard uncertainty uc(y) times the 

coverage factor (k) 

 Report the value of y and specify the combined standard uncertainty uc(y) 

The combined standard uncertainty, uc(y) (NIST Technical Note 1297), is defined as: 

2 1

2 2

c

1 11

() ( ) 2 ( , )

n nn

i ij

i i ji i ij

f ff u y u x ux x

x xx



  

   

          

where the partial derivatives డ௙

డ௫೔

 are often referred to as sensitivity coefficients, ݑሺݔ௜ሻ is the standard uncertainty of xi, and 

(, ) i j ux x is the estimated covariance associated with xi and xj. The equation is based on a first-order Taylor series of

1 ( ... ) N y fx x  . 

Setting the covariance, (, ) i j ux x equal to (, )()( ) ij i j rx x ux ux where (, ) i j rx x is equal to the correlation coefficient 

gives 

2 1

2 2

c

1 11

() ( ) 2 ( , )( )( )

n nn

i ij i j

i i ji i ij

f ff u y u x rx x ux ux

x xx



  

   

          

Sometimes it is convenient to express standard uncertainties as relative standard uncertainties. The relative standard 

uncertainty for ( ) r i u x is ( )/ i i ux x where 0 i x  and the relative combined standard uncertainty for , ( ) c r u y is ( )/ c uy y

where y  0 . Additionally, if the error contributions are independent, the cross terms are zero and the combined relative 

standard uncertainty may be calculated using 

2

2 2

c,r

1

() ( )

n

r i

i i

f uy ux  x

         

 Coverage Factor (k) 

For many applications, the uncertainty in y is approximated with a normal or Gaussian distribution. For this case or others 

(note: it is important to make sure the assumptions and expected distributions are documented) it is often informative to 

consider the value of the coverage factor (k). In simple terms, from statistical theory, a coverage factor of 1 gives the 

expanded uncertainty, U(y) = (1)uc(y), or more generally for any value of k, Uk(y) = (k)uc(y). For a normal distribution, k = 1, 

k = 2, and k = 3 provides a confidence of approximately 68 %, 95 %, and 99.7 %, respectively, the actual value Y is 

within the range of y ± Uk. 
26 

 Type A and Type B Uncertainty Evaluations 

When reporting uncertainties, the type of uncertainty evaluation should be specified along with logic and assumptions that 

were made in quantifying the estimate. Type A and Type B uncertainty evaluations give information about how these 

uncertainties were estimated. When estimating uncertainties, it is recommended the type uncertainty be specified so the 

reader is able to quickly assess the source of each individual uncertainty contributor. 

Type A uncertainty evaluation is often used to estimate uncertainty by way of multiple measurements and statistical 

methods. One statistical method often used for calibration applications is realized by calculating the mean and standard 

deviation (when the data are normally distributed) of multiple samples to better estimate the individual parameter i x and 

the associated uncertainty contributor. 

An example of Type A evaluation is making multiple sensor measurements while looking at a stable calibration source 

over a specified period of time. In this case, sensor response repeatability (a measure of uncertainty due to sensor 

measurement variability), which assumes the response variation due to the source variation is either negligibly small or 

sufficiently corrected, is quantified by way of multiple measurements and the standard deviation calculation. The specified 

period of time could be seconds for short term repeatability to months or years for long term repeatability. 

Type B uncertainty evaluation is used to estimate uncertainty when multiple data samples, and consequently statistical 

methods, are not possible. Uncertainties are instead estimated using engineering insight, judgment, and available 

information. This available information may include previous measurement data, experience or general knowledge, 

manufacture specifications, data provided in reports, and handbooks and other recognized reference materials. 

The logic of a Type B uncertainty can be shown using calibration dependence on temperature sensor readout as an 

example. In determining a correction factor to minimize this variability, readout error of the temperature sensor was 

identified through propagation of errors to be a contributing uncertainty. Drift in the readout was found to be negligibly 

small; therefore, the readout uncertainty was estimated as the standard deviation of a uniform distribution which is 

ሺܾ െ ܽሻ/√12 where (b - a) is the interval of the uniform distribution. If the readout resolution is ( )1 b a   , which is true 

for this example, then the one sigma uncertainty for this uncertainty contribution (i.e., k = 1) is 1/√12. 

For both Type A and Type B uncertainty evaluations, it is important to provide sufficient detail when documenting 

uncertainty estimates. This allows the reader to further assess uncertainty estimates particularly when new information 

becomes available. 

 Uncertainty Evaluation Example (Sensor System-Level Ground Calibration) 

The radiance uncertainty for the ground calibration of the SABER instrument (Tansock et al., 2003) gives a system level 

example of an uncertainty evaluation. The calibration equation for this calibration is: 

, , , , , ,i

1 1 ( ) ( ( )) m c ch ch i ch i ch i ch i ch

ch ch

L r Y Scn L G r O

r r       

where Lm is measured radiance, rch is peak radiance responsivity, rc,ch is corrected instrument response, Ych,i( ) is off-axis 

extended source throughput correction, Scn is scan mirror pointing angle, Gch,i is gain-mode normalization, Lch,i( ) is 

linearity correction, rch,i is detector response, Och,i is sensor offset, ch is channel number, and i is gain mode. 

The peak responsivity coefficient, ch r , was periodically updated for on-orbit calibration during operations. A space look 

was used to characterize the sensor offset and an in-flight calibration source (IFC) measurement was used to characterize 

the sensor response to a known radiance. The updated responsivity coefficient was calculated using: 

c IFC ,

ch

IFC

r

r

N  

where rch is the updated peak radiance responsivity, rc,IFC is the corrected IFC response, NIFC is the IFC radiance, and ch

is channel number. 

The on-orbit scene radiance was estimated by substituting the updated peak radiance responsivity into the calibration 

equation. This substitution, along with applying propagation of errors and setting the cross terms to zero (i.e., uncertainty 

contributors are independent), yields the following equation, which estimates the radiance (k = 1) relative combined 

uncertainty: 

,sig ,IFC

,sig 2 ,IFC 2 2 2 2 2 IFC

,sig ,IFC IFC

( 100) ( 100) ( 100) c c

c c

L r r IFC

c c

r r N

rr N

  

  

     
27 

where L is uncertainty of measured scene radiance (%), rc,sig is measured signal corrected response in counts, rc,sig is 

uncertainty of signal corrected response in counts, rc,IFC is measured IFC signal corrected response in counts, rc,IFC is 

uncertainty of IFC corrected response in counts, NIFC is IFC radiance in Wcm-2sr-1, NIFC is uncertainty of IFC radiance in 

Wcm-2sr-1, ߪ೎௥,ೞ೔೒ is uncertainty of SABER response to external source in percent, ߪ೎௥,಺ಷ಴ is uncertainty of SABER response 

to IFC in percent, and IFC is uncertainty of IFC radiance in percent. 

This equation identifies three main sources of uncertainty: uncertainty of sensor corrected response to an external source, 

uncertainty of the IFC corrected response, and uncertainty of the IFC radiance. Propagation of errors was also used to 

identify individual uncertainty contributors for each of these sources of uncertainty. The following table summarizes the 

radiance uncertainties for one of the SABER channels, where the main and individual uncertainty terms are listed. The 

type of uncertainty evaluation, Type A or B, is also shown. Some of the individual uncertainty terms contain both types of 

evaluations (A, B). 

Summary of the Radiance Uncertainties for SABER (Tansock et al., 2003) 

Description Relative 

Uncertainty (%) 

Uncertainty of the Corrected Response to an External Source 

Sensor offset measurement uncertainty 

 Offset is calculated from 10 sample average (B); SNR = 100 (B) 0.32 (B) 

Medium-term uncertainty of sensor offset. (i.e., time between space looks) 

 Measured medium-term offset drift (A); SNR = 100 (B) 0.08 (A, B) 

Signal noise uncertainty 

 SNR = 100 (B) 1.00 (B) 

Linearity correction uncertainty 

 Measurement uncertainty (A) 0.23 (A) 

Gain mode normalization uncertainty 

 Measurement uncertainty, 0.083 % (A); Electronics operating temperature, 

0.016 % (B); On-orbit radiation exposure, 0.01 % (B) 

0.085 (A, B) 

Uncertainty of off-axis extended source throughput correction (i.e., throughput correction as 

function of scan angle) 

 Measurement uncertainty (A) 

0.034 (A) 

Combined Uncertainty 1.08 

Uncertainty of the IFC Corrected Response 

Sensor offset measurement uncertainty 

 Standard deviation of offset noise divided by IFC response (A) 0.035 (A) 

IFC signal noise uncertainty 

 Standard deviation of IFC response noise divided by IFC response (A) 1.09 (A) 

Linearity correction uncertainty 

 Measurement uncertainty (A) 0.23 (A) 

Gain mode normalization uncertainty 

 Measurement noise, 0.083 % (A); Electronics operating temperature, 0.016 % (B); 

On-orbit radiation exposure, 0.01 % (B) 

0.085 (A, B) 

Combined Uncertainty 1.12

Uncertainty of IFC Radiance 

Radiance uncertainty of full-aperture blackbody 

 Thermal uncertainties (A, B); Emissivity uncertainties (A, B); Blackbody temperature = 

250 K (B) 

0.3 (A, B) 

Uncertainty of IFC channel radiance due to uncertainty of instrument relative spectral response 

 Measurement uncertainty (in- and out-band), 1.56 % (A); Out-of-band noise floor - 

where noise floor is larger than spectral response, 0.1 % (A); Polarization, 0.1 % (A) 

1.57 (A) 

Uncertainty of IFC radiance (calibration transfer to IFC) 

 Measurement uncertainty (A) 0.20 (A) 

Uncertainty due to IFC radiance long-term repeatability 

 Based on design performance (B); Verified with radiometric measurements (A) 0.9 (A, B) 

Combined Uncertainty 1.85

Total Combined Standard Uncertainty (%) (3 % Goal, 5 % Requirement) 2.4 
28 

3.3 VERIFICATION AND VALIDATION (V&V) 

The terms verification and validation have different connotations 

for different organizations, groups, and individuals. For example, 

V&V can have an entirely different meaning to the program office 

(program director, program manager, project scientist, etc.), who 

have the mission objectives in mind, versus the sensor lead EO or 

thermal engineer, who are focused on specific tasks and 

responsibilities. To be meaningful, V&V must have a context or 

scope associated with the specific application. 

This publication examines the relationship between calibration and V&V, by providing V&V 

definitions, qualifying (or putting into context) V&V scope with respect to calibration, identifying 

the role of calibration within this scope, and providing an example of the role of calibration in 

V&V. 

For this publication, the following NIST definitions are used for verification and validation: 

Verification – 

Confirmation, through the provision of objective evidence, that specified requirements have 

been fulfilled (e.g., an entity’s requirements have been correctly defined, or an entity’s 

attributes have been correctly presented; or a procedure or function performs as intended and 

leads to the expected outcome). (Source: CNSSI-4009) 

Validation – 

The process of demonstrating that the system under consideration meets in all respects the 

specification of that system. (Source: Federal Information Processing Standards (FIPS) 201) 

Confirmation (through the provision of strong, sound, objective 

evidence) that requirements for a specific intended use or 

application have been fulfilled (e.g., a trustworthy credential has 

been presented, or data or information has been formatted in 

accordance with a defined set of rules, or a specific process has 

demonstrated that an entity under consideration meets, in all 

respects, its defined attributes or requirements). (Source: 

CNSSI-4009) 

Applying the NIST V&V definitions specifically to EO sensor 

calibration, these definitions can be simplified to: 

Sensor-Level Verification – Answers the question ‘does the as-built sensor performance meet 

the specifications and requirements?’ 

Mission-Level Validation – Answers the question ‘were the sensor specifications and 

requirements sufficiently defined to meet the mission objectives?’ 

Using these definitions, the role of calibration is to provide the strong, sound, and objective 

evidence required for sensor performance verification. Additionally, calibration goes beyond the 

sensor performance verification role, providing the means by which measurement data are 

converted into target radiance, such as astronomical coordinates, creating the mission data 

Calibration provides the 

strong, sound, and 

objective evidence 

required for sensor 

performance verification. 

An EO sensor calibration that 

has been thoroughly planned, 

properly executed, verified, 

and validated creates a high 

level of confidence that the 

sensor data can ultimately be 

trusted. 
29 

products and uncertainties, for the mission validation process, which ultimately leads to trusted 

results. 

To perform sensor-level and mission-level V&V roles, the calibration itself must first be verified 

and validated by answering the questions: 

 Calibration-Level Verification – Were the calibration plan and procedures successfully 

executed? 

 Calibration-Level Validation – Was the calibration plan sufficient to meet the calibration 

requirements, and ultimately, the mission objectives? 

Verification of successful execution of the ground calibration plan via the data collection 

procedures is the purpose of quicklook analysis, where calibration data is checked and preliminary 

results are evaluated. A complete quicklook assessment of the calibration procedure results allow 

the calibration team to provide a definitive ‘YES’, to the successful completion of the ground 

calibration data collection procedures. This ‘YES’ should be provided at the consent-to-break 

calibration configuration meeting. 

Validation of the calibration is a demonstration that the correct calibration tests were planned and 

executed such that the calibration data products accurately characterize sensor performance, and 

that sensor response data can be converted into calibrated values with accurate measurement 

uncertainty bounds. Validation of the calibration data collection plan and procedures is not 

complete until the final calibration analysis and report is complete; which, depending on the scope 

of the calibration, is on the order of months beyond the end of calibration data collection. 

Calibration hardware anomalies, sensor anomalies, automated data collection problems, invalid 

assumptions, and other calibration concerns can surface during this phase. Hopefully any 

unexpected issues are identified during calibration data collection (from real-time monitoring) or 

quicklook analysis, and the test plan and procedures are modified on-the-fly to accommodate any 

additions and changes required. 

The Cross-Track Infrared Sounder (CrIS) sensor onboard the Suomi National Polar-Orbiting 

Partnership (Suomi NPP) satellite (Han, et al., 2013) used various methods to validate and verify 

the data, as discussed in the following example. 

 
30 

CrIS Validation and Verification 

CrIS sensor has three levels of data products: raw data records (RDR), sensor data 

records (SDR) and environmental data records (EDR), as shown in the following figure. 

Sensor-level verification was performed as part of prelaunch calibration to assure that 

CrIS sensor is performing as expected and to validate that the computed RDRs are an 

accurate representation of scene spectral radiance. 

The EDRs, which contain sensor calibration data products that are used in data 

processing, were validated through post-launch calibration activities. The post-launch/onorbit mission calibration activities of the CrIS sensor include three phases: early on-orbit 

checkout, intensive calibration and validation, and long-term monitoring (Han et al., 2013). 

Due to the JPSS/CrIS calibration efforts, which were thoroughly planned, properly 

executed, verified and validated, the science data results created by the CrIS sensor are 

trusted with a high level of confidence. 

CrIS Data Flow and Data Certification Levels 

(Courtesy of Joint Polar Satellite System (JPSS) Program) 
31 

CALIBRATION PLANNING

When planning any calibration campaign, constraints on time, funding, 

personnel, and calibration resources such as chambers, sources, 

spectral test equipment, thermal control systems, and even data 

acquisition and processing systems must be taken into account. There 

are always trade-offs among what is ideal, what is desired, and what is 

strictly required when performing sensor calibration. While it may 

seem expedient at the time, reducing the scope of the calibration effort 

to reduce costs may in fact lead to more costly issues later in the 

program that could ultimately impact the success of the mission. The 

axiom “You only have one opportunity to collect the data” is for the most part true. Therefore, 

knowledgeable experts who can identify trade-offs among available budget, schedule, and impact 

to sensor performance/mission objectives should be included when deciding on test program 

specifics. This chapter discusses the components of calibration planning and identifies trade-offs 

that must be considered. 

4.1 EARLY CALIBRATION PLANNING 

Calibration planning should begin early during the instrument design phase 

to ensure the design can accommodate the measurement methods of an 

efficient calibration approach (Tansock et al., 2004). Calibration planning 

includes gathering and filtering information, performing analyses and 

trade-off studies, and creating a test plan that maximizes the sensor 

performance information obtained within the budget and schedule 

constraints of the program. The final product of calibration planning is a 

calibration plan that is typically presented as part of sensor design reviews. 

 Calibration Planning Trade-Off Space 

In the first step of the calibration planning trade-off space, shown in the following figure, 

requirements of the mission are flowed down to defining the instrument requirements, which are 

then used to identify parameters of the calibration equations, radiometric model, and sensor 

performance metrics (Section 4.1.6). 

These parameters flow down to calibration planning, which is essentially a trade-off study of 

calibration approach, cost, schedule, uncertainty, calibration support equipment, and risk. This 

process also includes interaction and feedback between calibration planning and instrument 

design. The goal is to determine the most efficient calibration approach that meets performance 

requirements, while minimizing calibration uncertainty, schedule, cost, and risk. This process is 

often initiated by first developing a strawman calibration plan (Section 4.2.1). 

Calibration planning for the 

lifetime of a sensor promotes 

an optimum calibration 

approach, reduces costs and 

expenditures, and minimizes 

uncertainty for the intended 

application. 

Calibration planning 

should begin during 

the instrument design 

phase. 
32 

Calibration Planning Trade-Off Space 

The calibration planning process for the SABER instrument is documented in Tansock et al. 

(2003). The detailed calibration planning process for this instrument resulted in an optimized and 

efficient calibration approach. One specific benefit from the interaction between calibration 

planning and the SABER instrument design was that scan mirror modes of operation, which 

allowed for high resolution instantaneous field of view (IFOV) measurements, were added to the 

design as a result of the various planning trade-offs. 

 Detailed Test Schedule 

Because calibration necessarily occurs after the sensor has been manufactured and toward the end 

of pre-launch preparations, there is often tremendous pressure to cut back schedule from the prelaunch calibration to make up time and to reduce costs. A detailed and accurate test schedule that 

follows the calibration plan provides credible documentation that can be used to justify the 

calibration schedule and allow for making accurate impact assessment if/when reducing the 

duration of the schedule is desired. This schedule must consider the duration of executing detailed 

data collection procedures, test equipment capabilities, measurement configurations, data 

management capabilities, test day and work week durations, and availability of the needed skilled 

human resources. The following example is a pre-launch sensor calibration schedule that illustrates 

how the large number of measurement tasks quickly add up to a significant schedule duration. The 

test schedule is sensor-specific and highly dependent on many factors, resulting in the need to 

develop a custom schedule for each sensor program. 
33 

Pre-Launch Sensor Calibration Schedule Example 

 Component- vs. System-Level Testing 

The degree of component-level versus system-level testing should also be determined during the 

calibration planning process. As discussed in Section 2.1.4, the advantage of system-level 

measurements is that all components are included in the measurement in the way they are used, as 

opposed to component-level measurements where differences in optical configuration, 

temperature, or orientation may be unavoidable. Using component-level measurements to estimate 

system-level parameters, compared to system-level measurements, increases the risk of having 

>20 % errors (Hansen et al., 2011). Therefore, when budget constraints dictate that component 

measurements must suffice for some calibration parameters, a minimum level of system-level 

measurements should be mandated in the calibration plan to verify the component-level 

measurements. 

 Pre- and Post-Launch Calibration Needs 

The best calibration planning includes thorough pre-launch calibration characterization followed 

by on-orbit validation and verification. As previously discussed (Section 2.1.5), pre-launch 

calibration provides the capability to perform tests in a controlled environment with known sources 

that cannot be duplicated on-orbit, and has the advantage of discovering and resolving anomalies 

prior to launch. Once on orbit, options to correct sensor performance are limited and expensive. 

Post-launch testing has the advantage of being performed under true flight conditions rather than 

simulated flight-like conditions. 
34 

Once the required measurement parameters are determined for an instrument, the decision must 

be made as to whether the measurement will be made pre- or post-launch. 

Post-launch calibration activities are required to 

maintain calibration throughout a sensor’s operational 

lifetime, quantify calibration uncertainty, and update 

calibration coefficients to meet measurement 

requirements. A sensor contains internal calibration 

sources that are common to all phases of calibration, 

and are used in trending the response of a sensor. 

These sources must be thoroughly characterized and 

calibrated during sensor-level ground calibration to 

establish baselines. 

For instrument calibrations that plan to use on-orbit 

calibration sources, the spacecraft or sensor payload 

may need special capability to view these sources. For 

example, low Earth orbit (LEO) satellites will often 

need to execute attitude maneuvers to view on-orbit 

sources. If addressed early in the sensor payload 

design, an alternate data acquisition procedure could 

be considered such as rotating-mirror designs, which 

can adjust the Earth-view sequence to include 

calibration source and deep space viewing. Chapter 6 

provides a thorough discussion of on-orbit calibration. 

 Sensor Performance Model 

Sensor performance is dependent on relationships between multiple responsivity domains, as 

discussed in Section 2.2, and a fundamental purpose of radiometric calibration is to identify and 

apply corrections for the interactions between domains that may impact a given measurement. 

To identify the parameters and characterization required to understand sensor performance, a 

unique sensor performance model should be compiled during the sensor design phase. Using 

mission requirements along with sensor design input, this model enables the sensor designers to 

tailor the instrument design to meet project requirements, and can be used to predict and test the 

response of the sensor to different calibration scenarios. By modeling the sensor response to scenes 

presented by different calibration sources, the designs for these sources can be adjusted and 

optimized for the calibration in parallel with the instrument design and fabrication. In addition, the 

equations used in the sensor performance model may provide insight into sensor behavior, may 

support selection and development of calibration equations and radiometric model parameters 

(Section 4.1.6), and can be a valuable tool for uncertainty analysis and budgeting (Section 3.2). 

Examples of the equations and level of detail that can be found in a sensor performance model can 

be found in Hansen et al. (2003). 

The sensor performance model concept uses standard principles of optical engineering to develop 

a mathematical model representing the end-to-end sensor performance, from flux entering the 

sensor aperture to detected signal output. The optical principles and concepts described are 

summarized in tutorial courses that were presented at the Conference on Characterization and 

Radiometric Calibration for Remote Sensing (CALCON) (Yoon 2010; Hansen et al., 2011). A 

Pre- vs. Post-Launch Calibration 

Parameters that must be measured pre-launch 

because available on-orbit sources do not 

provide this capability, or provide only limited 

capability. 

‒ RSR 

‒ Absolute radiance responsivity 

‒ Sensor nonlinearity 

‒ Polarization sensitivity 

Parameters for which an on-orbit 

measurements may be superior 

‒ Pointing and geometrical parameters 

such as distortion mapping 

‒ Pixel IFOV 

‒ Off-axis scatter 

‒ Dark noise free from ground test 

conditions 

‒ Dark background/offset with stabilized 

thermal conditions 

‒ Point response function (PRF) using stars 

as ideal point sources 
35 

good optical or system design textbook (Wyatt 1987) can be another valuable resource. Generally, 

the model will describe the sensor optically and electronically and then predict the sensor response 

to backgrounds and other sources of interest. Parameters such as sensor aperture diameter and Fnumber, mirror spectral reflectance, pixel dimensions, focal plane noise quantities, and quantum 

efficiency should be included. A number of software packages including Microsoft Excel, 

MathCad, IDL, and MATLAB are available to support sensor performance modeling. (Any 

mention of commercial products is for information only; it does not imply recommendation or 

endorsement by NIST or author organizations.) 

 Calibration Parameters and Equations 

An electro-optical sensor responds to optical or radiometric input energy (Tansock et al., 2004.) A 

sensor-specific measurement or calibration equation is used to convert the sensor output (in units 

of counts, volts, etc.) to the desired physical units. Calibration parameters that are not included in 

the measurement or calibration equations, but are required to fully calibrate or characterize the 

instrument, become part of the sensor’s radiometric model. Sensor performance metrics are those 

parameters that are also the product of calibration data analysis that are used to describe and 

quantify sensor performance. The calibration parameters are therefore grouped into three 

categories: parameters associated with the measurement or calibration equation, parameters that 

comprise the radiometric model, and sensor performance metrics (Hansen et al., 2011). The 

breakdown of the components into the three categories depends on mission requirements. 

Parameters may be considered performance metrics for one sensor, but calibration equation 

coefficients for another. 

Measurement and Calibration Equations 

The measurement equation is used to model the response of a sensor as a function of source 

configuration/parameters/settings and is needed to evaluate the sensor performance in the spatial, 

spectral, and temporal responsivity domains. The concept of measurement equations was first 

introduced by Nicodemus et al. (1976) of the National Bureau of Standards and further discussed 

by Wyatt (1978) and Wyatt et al. (1998). 

The nomenclature adopted here for the response of the sensor is digital numbers (DN), although 

other units of response, depending on the sensor design, may be more applicable (such as micro 

volts, micro amps, etc.) The general form of the measurement equation for detector i in band j is: 

   , ,,, ,,, DN G L x y t R x y t dxdyd dt i j     

where G is the instrument gain factor and L(x,y,,t) is the spectral radiance of the target source or 

calibration source. Both the radiances and the responsivities can have x- and y-spatial dependence, 

and  and t denote the spectral dependences and time dependences, respectively. R(x,y,,t) is the 

relative radiance responsivity of the instrument. The integrals are performed for each variable, 

and additional dependencies such as polarization sensitivity and linearity may also need to be 

included, depending on the sensor. 

 
36 

The calibration equation is an inverted form of the measurement equation, and is often a convenient 

form for sensor-level system calibration because it can be used to directly estimate the radiometric 

measurement (in units such as radiance or irradiance) from the sensor response. In very simple 

terms, the calibration equation is: 

, (, , ,) (, , ,) ( ) L V ij x y t R x y t dxdyd dt R F DN      

where RV is the radiant value of interest (for example, radiance) as a function of the response of 

the sensor ܦܰ௜, ௝being operated on by function F( ). The function F( ) makes all needed corrections 

such as background correction, gain and/or integration time normalization, linearity, polarization, 

and others. The form of the function F( ) is unique to the sensor design and operation. Typical 

calibration equations and parameters for imaging radiometers are shown in the following example. 

Typical Calibration Equations and Parameters for Imaging Radiometers 

Radiance 

,, , , ,, , ,,   ,

1 1 k I

M k t k t Lin k T k t Lin k O k t

L L NUC k

B G L r Fr Fr

F

             

ܮெ,௞,௧ Measured radiance (W/cm2

sr) ܨ௡௜௅,௞൫ݎ்,௞,௧൯ Nonlinearity correction function (unitless) 

Ը௅

Peak radiance responsivity 

(counts/W cm2 sr) ݎ்,௞,௧ Raw pixel response (counts 

ݎ௞,௧ Corrected pixel response (counts) ݎை,௞,௧ Raw pixel background response (counts) 

ܤ ௞Bad pixel mask function (unitless) ݐ Time – parameters vary as function of time 

ܩூ Integration time normalization (unitless) ݇ Pixel index – unique to each pixel 

ܨே௎஼,௞ Non-uniformity correction function (unitless) 

Irradiance 

,, , , ,, , ,,   ,

1 1 , , k I

M k t k t Lin k T k t Lin k O k t

E irrad E irrad NUC k

B G E P r PRF P F r F r PRF

UNF UNF F

                 

ܧெ,௞,௧ Measured irradiance (W/cm2

) ܩூ Integration time normalization (unitless) 

Ըா

Peak irradiance responsivity 

(counts/Wcm2) ܨே௎஼,௞ Nonuniformity correction function (unitless) 

ௗ௜௥௥௔ܨܷܰ

Irradiance uniformity correction 

(unitless) ܨ௡௜௅,௞൫ݎ்,௞,௧൯ Nonlinearity correction function (unitless) 

ܲൣݎ௞,௧, ܴܲܨ൧ Point source extraction operation ݎ்,௞,௧ Raw pixel response (counts) 

ݎ௞,௧ Corrected pixel response (counts) ݎை,௞,௧ Raw pixel background response (counts) 

ܴܲܨ Point response function (unitless) ݐ Time – parameters vary as function of time 

ܤ ௞Bad pixel mask function (unitless) ݇ Pixel index – unique to each pixel 

Radiometric Model and Sensor Performance Metrics 

The radiometric model and sensor performance metrics include all calibration parameters that are 

not included in the measurement or calibration equation but are required to fully calibrate or 

characterize the instrument. The radiometric model helps the analyst interpret the response of the 

sensor in terms of a scene observed by the sensor (Hansen et al., 2011). Examples of parameters 
37 

that would be part of the radiometric model include interaction of sensor spectral response with 

source temperature and emissivity, and interaction of sensor off-axis response (OAR) with source 

background. Other examples of radiometric model parameters for a generic imaging radiometer 

are given in Tansock et al. (2004). 

Sensor performance metrics include additional parameters such as sensor requirements 

verification, sensitivity (noise equivalent radiance, irradiance (NER, NEI)), saturation behavior, 

uncertainty terms, noise, and stability and repeatability (Hansen et al., 2011). Other typical 

radiometer model parameters for an imaging radiometer are given in the following example. 

Typical Radiometric Model Parameters for an Imaging Radiometer 

Source Characterization Parameters

Relative Spectral Response Effective Field of View Polarization Sensitivity 

Near Angle Scatter Focus Point Response Function 

IFOV Line-of-Sight Map Waveband Crosstalk Focal Plane Image Latency 

Sensor Performance Metrics 

Noise-Equivalent Irradiance (NEI) Noise-Equivalent Radiance (NER) 

Saturation-Equivalent Irradiance (SEI) Saturation-Equivalent Radiance (SER) 

Noise-Equivalent Flux Density (NEFD) NUC and Stability (Fixed Pattern noise) 

Response Repeatability & Response Noise Dark Offset/Background Repeatability 

(Dark Noise) 

Angle Repeatability & Jitter 1/f Noise 

Sensor Time-Stamp Characterization Sensor Frequency Response 

Dynamic Range Saturation Behavior 

On-Board Calibration Source 

Characterization 

Any other unique sensor performance 

parameters 

 Capabilities Required for Calibration Data Collection 

Certain capabilities are needed to perform a complete, system-level calibration. These capabilities 

include achieving the required angular coverage, performing both point source (irradiance) and 

extended source (radiance) calibrations, obtaining pointing and stability knowledge, and making 

system-level RSR measurements. In general, these capabilities provide the ability to meet all 

calibration data collection needs. 
38 

Achieving Required Angular Coverage 

Many of today’s sensor applications require wide field-ofview (FOV) (>1 degree) coverage, and to ensure accurate 

calibration measurements, there needs to be a plan for 

achieving full aperture and angular coverage during 

calibration measurements over the sensor’s entire FOV. If 

not fully addressed and understood, optical vignetting may 

occur, resulting in partial coverage of the sensor aperture for 

select line-of-sight (LOS) angles during calibration 

measurements. 

When formulating a calibration plan, optical ray tracing of 

the sensor and calibration optical system should be 

performed to address optical coverage of the sensor entrance 

pupil for a specified range of LOS angles and (if needed) 

field of regard (FOR) angles. To make first-order estimates, 

this can be accomplished with simple geometry calculations. 

If the optical path is complex and/or the results are marginal, 

it is recommended that ray tracing be performed with more 

advanced tools such as computer-aided design or optical 

design software. 

Point Source (Irradiance) versus Extended Source (Radiance) Calibration 

A complete calibration requires both point source (irradiance) and extended source (radiance) 

calibrations (Tansock et al., 2004; Wyatt 1978). Point source calibration quantifies the sensor 

response to a point object (like a star) and the extended source calibration quantifies the sensor 

response to an extended object (like a uniform section of the Earth’s surface). In practice, the 

measurement scenario for the intended end user application of the sensor may produce neither pure 

point nor extended source responses but somewhere between the two. Therefore, the data analyst 

must account for this difference when producing results, and requires the calibration parameters 

to make this assessment. 

Irradiance is the quantity used to describe the response of a sensor due to a distant small area source 

(Wyatt 1978; Wolfe 1998). In the case of an ideal point source like a star, the distance between the 

source and sensor is very large and the photons at the sensor entrance pupil are essentially 

collimated. These collimated photons are then focused by the sensor down to a small spot on the 

focal plane whose size is finite due to design requirements, geometric optics image quality 

limitations, and diffraction. The resulting small spot on the focal plane or the distribution of energy 

at the focal plane is often termed the point spread function (PSF). The PSF convolved with the 

spatial response of an individual sensor detector element produces a PRF. The PRF quantifies the 

response of a sensor due to a PSF. 

In the laboratory, point sources are often simulated with a collimator, which is a telescope 

configured to simulate a point source by placing a small pin hole at its focus. This pin hole is back 

illuminated by an energy source such as a blackbody or lamp. 

Radiance is the quantity used to describe the response of a sensor to a source that is larger in extent 

than the IFOV of a single pixel. An extended source fills both the sensor entrance pupil and the 

field of view, and the measurement has radiometric units of W/(cm2

sr) (energy per unit area of the 

Pixel-to-Pixel 

Non-Uniformity 

Measurement of pixel-to-pixel nonuniformity with an extended source is a 

classic example where full optical pupil 

and angle coverage is required. For 

this measurement, it is important to 

optically fill the sensor entrance pupil 

with a well understood optical angular 

divergence. This allows an analyst to 

quantify (by way of measurement) the 

pixel-to-pixel response non-uniformity. 

However, if optical vignetting is present 

as a function of LOS angle, the pixel-topixel response non-uniformity will be 

due to both real pixel-to-pixel nonuniformity and optical vignetting. These 

differences are often subtle, difficult to 

predict, and easy to overlook. 
39 

entrance pupil per solid angle). The measured configuration is can be described as a near extended 

source or distant extended source (Wyatt 1978; Wolfe 1998); radiometrically, the distance to an 

extended source is irrelevant. 

Extended source measurements must completely fill the sensor aperture and overfill, at a 

minimum, the IFOV of a specified detector without being affected by geometric properties of the 

sensor which are often associated with point source calibrations. These calibration measurements 

can be used for radiance calibration as well as pixel-to-pixel calibrations like uniformity, 

background, offset, response linearity, and integration time settings. The near extended source, 

which is the radiometric equivalent to the distant extended source (Wolfe 1998), is often used 

during ground testing to minimize test distances and source size. 

EO Sensor Pointing and Stability 

The pointing knowledge of EO sensors is critical for valid and accurate data collection for many 

applications. For a space-based EO sensor, these applications include mapping the sensor boresight 

and sensor response to an Earth centered inertial (ECI) coordinate frame (Vallado and McClain, 

2007). 

Because each sensor application is unique, the flow of coordinate transformations from the sensor 

focal plane to ECI coordinates must be individually developed. A typical flow is shown in the 

following example. Mathematical details of these coordinate transformations and spacecraft 

attitude and control can be found in various publications (Lefferts et al., 1982; Shuster 1983, 1989, 

1990; Shuster et al., 1991; Shuster and Pitone 1991). The position of a sensor relative to the radiant 

scene being observed provides an analyst with fundamental information to assess data quality, 

including mapping and/or geolocation knowledge of the data set. 

Flow Diagram of Coordinate Transformations from the Sensor Focal Plane 

to ECI Coordinates 
40 

Sensor position instability is a direct result of the host vehicle performance. A sensor has a single 

pointing axis, commonly known as the optical axis. Rotation about the optical axis is deterministic 

relative to the sensor hard mount to the host vehicle. All data collected by a sensor are relative to 

the host vehicle attitude and attitude stability, which is a fiducial vector. Post data collection, an 

analyst relies upon a sensor LOS relative to a host vehicle coordinate system. The pointing 

calibration and monitoring is consequently reduced to knowledge of the time-dependent 

relationship between the 3-axis coordinate system for the sensor data frame, and the 3-axis 

coordinate system in which the host vehicle attitude is established. These in turn are related to the 

Earth position in space and orientation in time, which is a third time-dependent 3-axis coordinate 

system. After launch, the alignment is further quantified and used to update pre-launch knowledge. 

A host vehicle carries a reference attitude sensor system, which consists of two instruments: one 

instrument produces a reference attitude to update the drift and random walk intrinsic in inertial 

reference instruments such as star cameras, and the second instrument is a host stabilization 

instrument consisting of momentum wheels, gyros, or their equivalent. The data set combined from 

these instruments provides operational information for host vehicle housekeeping and safety, and 

also makes collection of accurate science data possible. The reference attitude system provides the 

host vehicle attitude and position relative to the ECI coordinate system in space and time. 

Science data and pointing data are collected during specific time intervals related to the Earth. 

Accurate measurement of time on-board the vehicle and time-stamping of the collected data sets 

are critical features. The accuracy of on-board time measurement is coupled to the accuracy of the 

vehicle and instrument coordinate systems. The on-board time is typically maintained through 

high-precision clocks, which are monitored for drift in ground operations, and have a method for 

resynchronization. 

To create the most accurate science data with respect to pointing and stability, an on-board 

autocollimator should be used where feasible to measure the alignment of science instruments 

relative to the host vehicle attitude system to retain stable accuracy. A common optical bench 

should also be used to simultaneously support the science instrument(s) and host vehicle attitude 

system. Prior to launch, an on-board attitude coordinate system should be specified to be the 

vehicle initial, ECI pointing reference. A common coordinate system for science instruments LOS 

(boresight) can be monitored and updated when data and analysis confirm the need to do so. 

Redundant attitude instruments can be used to improve science instruments’ LOS centroid 

algorithm, and for pixel-to-pixel gain normalization across the instrument FOV. Stray light 

rejection can be improved in both instrumentation sensors and attitude sensors instruments. 

Attitude system alignment sensitivities should be fully calibrated and understood before and after 

data collections operations. 

System Level Relative Spectral Responsivity (RSR) 

All radiometric measurements depend on knowledge of the sensor's RSR since the sensor output 

is due to both the spectral distribution of the source being measured and the RSR of the sensor. 

The RSR for a spectrometer can be measured by collecting data while the sensor is looking at any 

source with a known spectral distribution. This is often accomplished by using a spectrometer to 

measure a blackbody source with high spectral emissivity where the source spectral distribution 

follows Planck’s equation. 

 
41 

For sensors other than spectrometers, it is more difficult to measure the RSR. The most common 

methods are to use the output of a spectrometer (monochromator or a Fourier transform 

spectrometer (FTS)), or to use a tunable monochromatic source (such as a laser) as the calibration 

source. For any of these approaches, care should be exercised to measure the system level RSR 

with a spectral source that has finer spectral resolution than the narrowest line source measured by 

the sensor application, as discussed in Section 5.3.2. Since none of these sources are available in 

space, it is difficult if not impossible to make an RSR calibration measurement for nonspectrometer sensors post launch. 

The general accuracy of pre-existing RSR knowledge can be roughly evaluated on orbit by making 

measurements of sources that cover the largest possible range of temperatures, such as multiple 

stars. Since hotter sources produce relatively more flux at shorter wavelengths, and cooler sources 

produce relatively more flux at longer wavelengths, radiometric response calculations using both 

types of source can reveal errors in the available RSR data. However, this type of testing does not 

provide a new measurement of the RSR, but only reveals shortcomings in existing data. 

 Environmental Conditions for Pre-Launch Calibration 

When conducting pre-launch calibration on an airborne or satellite-based EO 

sensor, it is best to follow the axiom “test as you fly” or “test like you fly” 

(TLYF) (Datla et al., 2011; Russell 2008), which states that instruments 

should be calibrated under the same environmental conditions as expected 

during operation, such as low pressure environment, sensor thermal 

environment range, sensor operational modes, solar and/or Earth heat loads, 

sensor electronics environmental conditions, and sources of scattered light. 

In practice, it may be too costly to simulate the complete operating 

environment, and this must be addressed during calibration planning. The level of replication of 

the operating environment will depend on what is expected to significantly affect the instrument. 

For example, most space instruments must be tested in a vacuum environment, and calibration 

must be performed with as much of the instrument as possible at operating temperatures. It may 

be difficult to replicate the detailed thermal environment of space because cold space and heat 

sources (Sun and Earth) differ with varying orbital geometries. Conditions can be sufficiently 

simulated with boundary environmental conditions, however. 

Most instruments will experience a range of environmental conditions during operation, and 

calibration should be performed over this range of operating conditions. As a minimum, tests 

should be performed at the nominal and expected extremes of the operating environment to bound 

calibration results. However, if a strong or unusual dependence on the environment is expected or 

observed for the instrument under calibration, additional testing over the range of environmental 

conditions may need to be performed. 

 

Test as you fly - 

Calibrate under the 

same environmental 

conditions expected 

during operation. 
42 

This may lead to testing over environmental ranges that 

somewhat exceed those expected in order to obtain 

functional dependencies of the outputs on these 

conditions. Even if the ranges are not larger than those 

expected on-orbit, the rate of change of the orbital 

environment compared to the usually static or equilibrium 

conditions used during ground calibrations may mean that 

the program does not have adequate insight into how the 

sensor will be performing on-orbit. In the extreme case, 

where the temperature distribution due to the on-orbit 

environment is not stable and/or does not match the static 

conditions during pre-launch calibrations, the transient 

behavior of the sensor will need to be derived from a 

combination of ground data as a function of the test 

conditions that were used, and data that can be acquired 

on-orbit on internal calibration sources, celestial sources, 

or Earth scenes themselves. 

 Day in the Life Tests 

When a sensor is placed into orbit, not only will the environment not necessarily be the same as 

the testing environment, but the manner in which the sensor is tasked on-orbit may lead to 

unexpected behavior of the sensor. The orientation of the spacecraft to the Sun will change, and 

consequently, thermal loading on the sensor will vary as the seasons change. In addition, the data 

acquisition and transfer system may be different from the thermal vacuum chamber operations on 

the ground. Time delays within the command system and lags in the receipt of information from 

the on-orbit spacecraft may introduce conflict for control of command lines, which can lead to 

unexpected behavior of the sensor in its actual orbital environment. Even the manner in which the 

sensor is commanded may not be exactly the same, although every attempt is made to use flight 

software and flight commands to operate the sensor during pre-launch calibrations. 

To better understand the expected behavior of the sensor in its on-orbit environment, a day-in-thelife (DITL) test can be performed pre-launch. A DITL test is usually run for a full 24-hour period 

and tries to mimic the diurnal variations of the expected on-orbit environment on the worst case 

day. This test also provides the opportunity to mimic the commanding and data loading of the 

system expected during flight operations. This test implements a TLYF philosophy and can help 

resolve any residual concerns with how the sensor will be used on-orbit. 

This DITL test is usually performed apart from the required calibration testing, and is geared to 

identify consequences of actual operation of the sensor that may not have been anticipated. The 

test is more about the use of the sensor than about the specific radiometric response of the sensor. 

DITL testing provides insight into the command, control, data acquisition, and data flow, including 

the use of stored command sequences, as well as live commanding for full-up operation of the 

sensor. 

Unexpected Environmental 

Effects 

The degree of acceptable environmental 

condition dependence on calibration must 

be evaluated based on the sensor design 

and calibration and/or instrument 

requirements, and then tested and 

verified during calibration testing. 

Environmental temperatures have been 

observed to affect radiometric 

performance of instruments when no 

effect was predicted (Esplin et al., 2010) 

due to factors such as the instrument 

unexpectedly seeing radiation from a 

baffle that varies in temperature with the

environment or detector readout 

electronics sensitivity to temperature. 
43 

4.2 CALIBRATION PLANS AND PROCEDURES 

Calibration plans and calibration data collection procedures should be prepared before the start of 

any testing. Calibration plans address the entire sensor life cycle from design, fabrication, 

assembly integration and test, pre-flight calibration, spacecraft integration and test, and all on-orbit 

operations. Step-by-step data collection procedures identify each step of the data collection process 

to ensure that the resulting data are adequate for subsequent analyses. Failure to create and execute 

an end-to-end calibration plan, including sensor performance trending over the sensor life, leaves 

gaps in the mission profile with no quantifiable traceability to NIST standards for validation and 

uncertainty estimates. 

 Strawman Calibration Plan 

A strawman calibration plan should be developed early in the program design phase that identifies 

the sensor, science, project, and mission requirements, which are then used to determine the needed 

calibration parameters. 

Consideration should be given to meeting performance requirements while minimizing calibration 

uncertainty, schedule, cost, and risk. The project life time should be addressed, including 

subsystem and system end-to-end pre-launch calibration, on-board source calibrations, calibration 

trending during integration and launch preparations, early on-orbit calibration, and calibration 

maintenance during operations. 

Once driving requirements have been identified, specific calibration equations and radiometric 

model parameters (Section 4.1.6) are developed for the sensor. An efficient calibration approach 

is then developed to identify the needed calibration measurements and measurement combinations 

to determine these parameters. An example of this process is described in Tansock et al. (2004). 

Strawman calibration plans are program specific, but should address the items listed in the 

following example: 

Strawman Calibration Plan Contents 

‒ Sensor and mission requirements that determine calibration requirements 

‒ Assumptions 

‒ Calibration equations and supporting radiometric model 

‒ Component-level calibrations required 

‒ Tests to be performed and phase during which the test will be performed 

‒ Tests used to quantify sensor performance and verify calibration requirements 

‒ Calibration monitoring requirements and concept for trending measurements 

‒ Measurement combinations for each calibration measurement 

‒ Baseline calibration schedule and human resource needs 

‒ Initial budget for calibration uncertainties 

‒ Calibration facility, sources and other hardware, and software requirements 

‒ On-orbit sources required and measurement feasibility 

‒ Concept of operations for on-orbit measurements 

‒ Availability and validity of calibration sources 

‒ Concept for data quality assessment 

‒ Concept data management approach for each phase of calibration 

‒ Projected risks of not meeting requirements 

‒ Rough order of magnitude (ROM) cost estimate 
44 

The strawman planning process for the SABER ground calibration is provided in Tansock et al. 

(2003). For this instrument, the engineering team coordinated with the project and science teams 

through technical interchange meetings. This effort resulted in consensus among the various teams 

to the overall calibration approach, measurement combinations, and budgeted estimates of 

uncertainty. 

 Comprehensive Calibration Plan 

Following the development of the strawman calibration plan, the comprehensive calibration plan, 

which is a more mature and detailed plan, is prepared. The comprehensive calibration plan revises 

the strawman plan with updated sensor design and performance information, and verification of 

the elements of the calibration approach. Any major changes or updates from the strawman plan 

should be reviewed and approved by stakeholders. The output of the comprehensive calibration 

planning process is a formal, detailed, and well organized document that has concurrence from all 

stakeholders. 

 Data Collection Procedures 

The calibration plan provides a top-level overview of calibration data 

collection, but does not provide the necessary detail to actually collect 

data. Therefore, step-by-step data collection procedures are developed 

to ensure that the resulting data are adequate for subsequent analyses. 

Testing of the data collection procedure before the final data collection 

helps to ensure that the procedure will generate the expected data. An engineering calibration prior 

to final data collection (Section 5.2) provides this opportunity. A new revision of the data 

collection procedure incorporating changes or parameter adjustments identified during the 

engineering calibration should be released before the start of final data collection. The engineering 

calibration is also a useful training opportunity for the personnel that will perform the final data 

collection. Information provided in the data collection procedure generally includes those items 

listed in the following example. 

Data Collection Procedure Contents 

‒ Test description 

‒ Preparation steps (needed hardware, configuration, etc.) 

‒ Data collection steps 

‒ Data collection time requirement estimates 

‒ Data storage and download time requirements 

‒ Reference to test plan and data products 

‒ Documentation of related command files 

‒ Data collection notes 

‒ Data collection log sheets 

‒ Quicklook analyses to be performed shortly after data collection, and before breaking the 

hardware configuration 

‒ Data collection success criteria 

Test data collection 

procedures prior to 

the final calibration. 
45 

 Human Resource Requirements 

Sufficient personnel must be available to collect calibration data, move data between the collection 

and analysis systems, and complete quicklook data analyses. The detailed data collection 

procedures and schedule help to identify and finalize these resource requirements. 

4.3 DATA COLLECTION AND DATA MANAGEMENT SYSTEM 

The data collection and data management system for the calibration effort must 1) provide the 

hardware and software tools for the test conductor to efficiently and accurately configure the sensor 

and test hardware and collect the calibration data for each test point, and 2) provide calibration 

engineers and/or analysts with the data processing hardware, software tools and management data 

structures needed to preform analysis and create calibration data products in a timely and efficient 

manner. 

The scope of data collection and data management systems is program dependent. For sensors 

producing large data volumes, the data collection and data management systems may be separate 

local area networks (LAN), working semi-independently, with separate data collection and data 

management teams assigned. For large-scale calibrations, the data management team may be 

required to work multiple shifts to keep up with the data volume produced by the test team. For 

smaller-scale calibrations, data collection and data management may reside on a single computer, 

or network, and require only one or two operators, including test team members. 

 Data Collection and Data Management Plan 

The data collection and data management plan should be developed as part of the calibration plan, 

and should include a description of the required data collection hardware, data collection software, 

data flow, expected data volume, data management hardware, and data handling and processing 

software. In addition, data quality assurance and archiving should be addressed. 

 Data Collection and Data Management Hardware 

Special test equipment is required to perform ground-based end-to-end radiometric sensor 

calibration. This equipment, referred to as ground support equipment (GSE) includes the data 

collection and management system, test chambers, calibration sources, electrical support 

equipment (ESE), and sensor ground support equipment. This section describes the data collection 

hardware, data management hardware, and ESE. The other components of the GSE are described 

in Section 5.3. 

The data collection and data management hardware must be capable of handing the data volume 

produced during calibration while providing user access. The required hardware typically includes 

a central automation control computer, control and monitor computers, data management storage 

and terminals, data analysis workstations, and ESE. The ESE controls and monitors the instrument 

under test, the test chamber, and the radiometric sources during ground operations. The readiness 

status of the data collection hardware, data management hardware, and ESE must be part of the 

test readiness review (TRR). 

A typical data collection and data management hardware system functional diagram is shown in 

the following example. This diagram was developed as part of a calibration test plan and was used 

to identify hardware requirements and assess readiness. The data collection and management 

system architecture in this diagram consists of commercial-off-the-shelf (COTS) hardware that is 
46 

used for both general and special purposes. The hardware choices were based on the requirements 

to collect, store, analyze, and archive sensor data. 

For this example, the calibration control system (CCS) is the central automation control computer. 

Commands to configure the sensor under test (SUT) and the control and monitor systems (CMS) 

computers for the test chambers are transmitted via a separate dedicated Ethernet LANS. 

Snapshots of state of health data from the SUT and configuration status from the CMS computers 

are collected and stored on an internal hard drive, along with the CCS command history. Data 

received from the SUT are stored on the sensor's electrical support equipment (ESE) and then 

transferred and stored to the data management terminal onto the protected data management store 

network-attached storage for data quality checks. Once collected and stored, data are backed up 

and archived to a taped storage system. The CMS computers collect and store configuration and 

status information on internal hard drives. Data are transferred via Ethernet TCP/IP to the data 

management store (network attached storage) for later backup and archiving. Facility data are 

collected, transferred, and stored via portable media. 

As a rule of thumb, data storage systems should be sized to accommodate a 5-times expansion of 

the data volume over the volume of the collected data to have sufficient storage space for analysis 

products, and consideration should be given to future extensibility of the system. The number of 

analyst workstations required to support the calibration effort is dependent on the quantity of data 

to be collected, the level of automation applied to the data analysis process, and the number of 

analysts reducing the data. Investment in building a dedicated workspace or facility where analysts 

have the resources (servers, workstations, software, network, etc.) required to support the 

calibration effort can minimize schedule and cost overruns. 

Sensor data can generally be collected much faster than it can be analyzed. Therefore, programs 

typically also require an in-house data repository and server system to support detailed, longerterm analysis and trending of functional and radiometric analysis of sensor performance 

throughout the duration of the program. 

Current EO imaging sensors can produce hundreds of terabytes of calibration data, which pushes 

the limits of current state-of-the-art computer hardware. Delaying hardware procurement until it 

is actually needed may result in more capable and/or less expensive hardware, but this strategy 

must be weighed against the time needed to assembly, integrate, and test the hardware. 
47 

Calibration Data Collection and Data Management System Example 

 Data Collection and Data Management Software 

Development and testing of data collection and management software is a significant part of sensor 

calibration. Tasks related to data collection and management software should be discussed in the 

calibration plan, and the software development, testing, and revision control should be reported in 

the TRR prior to the start of test. 

Each of the computer systems in the calibration data collection and data management system 

requires software to accomplish the expected task. Data collection and data handling software play 

a large role in the quality, efficiency, and flow of the calibration. Data collection and data 

processing software are best developed by a calibration engineer with software engineering 

capabilities, or a software engineer with a strong understanding of EO sensor calibration hardware, 

processes, and objectives. Typical functions of a data collection and management software are 

shown in the following table. 
48 

Potential Data Collection and Data Management Software Functions 

Function Description and Details 

Facility data logging Environmental conditions logging 

Individual systems Command, control, and monitoring of test chambers, test 

hardware and subsystems 

Sensor command and 

data system 

1. Configure sensor under test 

2. Real-time monitoring of sensor response 

3. Sensor data collection 

Master controller 

Manual and/or scripted communication and commanding 

1. Control and monitoring of subsystem configurations 

2. Control sensor under test configuration 

3. Initiate sensor data collections 

4. Log all measurement configuration information 

Quality assurance Check for data anomalies, operational bounds, etc. 

Data base Gather all information and populate the database for each test 

point or data collection 

Data archival Structure and copy all data to processing storage and long-term 

archive 

Preprocessing Extract statistics and informational quantities from the raw data 

Test point construct Gather sensor data and all information associated with a test point 

for analysis 

Processing and analysis Turn test-point constructs into calibration parameters, 

uncertainties, and data products using specialized software tools 

 Automated Data Collection 

Automated data collection is a must for any modern sensor calibration. Automated data collection 

provides efficiency, accuracy, and consistency that starts with data collection and continues 

through data processing and analysis. Computer and software scripts are often used to perform 

these repetitive tasks. The initial investment required to implement automated data collections and 

remove the human element results in a high return on investment, with overall savings and a better 

calibration product. This is especially true since automated data collection development takes place 

off the program critical path, and sensor calibration data collection is typically on the critical path. 

Implementation of automated data collection requires specialized software to execute control script 

file(s) to set sensor and chamber conditions, and to command the sensor data collection system. 

At the same time, chamber, calibrator, and sensor conditions, along with ancillary information, are 

logged over a distribution of computer systems, and integrated into a time-tagged database. All of 

this information is passed to the data management system for processing and analysis. 

Coordination between the automated test controller, the sensor data collection system, and the 

database is important for the efficient hand-off between the data collection system and data 

management system. Personnel should be assigned to automated data collection, database, and 

data management tasks with sufficient time and resources prior to the start of calibration to ensure 

that the data collection, processing, and analysis system is prepared and tested prior to the start of 

calibration. 
49 

 Real-Time Display and Monitoring 

The ability to display EO sensor data in real time is critical to successful ground testing and 

calibration. Real-time data display assists in verification of sensor and ground support equipment 

(GSE) performance, and provides feedback for data collection configuration settings such as 

centering a point source on a specific location while avoiding bad pixels and checking for 

saturation. Real-time data display capability is typically built into the sensor data collection GSE, 

while allowing both data collection and real-time display to be accomplished simultaneously 

without impeding data collection. Real-time display of data during the collection period is the first 

verification that the data were properly collected, and provides immediate feedback of sensor 

response. 

While real-time display features are sensor dependent, all displays have similar features in 

common. One of the most useful features is the ability to display both numeric response values 

and plots such as current values, statistical values, and coordinates. Plots allow the user to visualize 

large amounts of information such as full images, zoom images, image profiles (column or row 

plots), image projections (all column or row values plotted onto a two-dimensional image), time 

series plot, and histograms. The ability to pan and zoom, select plot type, set plot limits, and autoscale are examples of features that should be included in a graphical user interface. 

The following image is a screen-capture of the Geosynchronous Imaging Fourier Transform 

Spectrometer (GIFTS) real-time display viewing the Moon from the ground. This figure contains 

information about the data collection system, measurement configuration, and measurement 

results. The upper-left corner of the figure shows metadata related to the data collection request 

and actual data collected. The upper-right corner shows that the target (Moon) is fully contained 

within the sensor FOV. Directly below this image is an oscilloscope projection of all pixels in the 

image, showing that the response levels are within the desired dynamic range. A single-pixel 

response interferogram and spectrum are show in the lower left quadrant of this figure, along with 

meta-data showing the interferogram maximum and minimum values and their locations, as well 

as other features. 
50 

Real-Time Display Example 

GIFTS viewing the Moon during instrument checkout (units are typically omitted on space-limited 

real-time displays because test conductors are familiar with the expected data output) 
51 

PRE-LAUNCH CALIBRATION 

Pre-launch calibration, or ground calibration, provides the capability 

to perform tests in a controlled environment with known sources that 

cannot be duplicated on-orbit, and has the advantage of discovering 

and resolving anomalies prior to launch. These measurements include 

component-level or bench-top subsystem tests, as well as systemlevel tests. Measurements made during pre-launch calibration are 

used to verify proper instrument operation, to quantify calibration 

equation and radiometric model parameters, and to estimate measurement uncertainties. Prelaunch calibration is essential to understanding sensor performance nuances so that they can be 

addressed and understood before launch. Options to correct sensor performance on orbit are 

limited and expensive. 

5.1 PREPARATIONS 

Before the pre-launch calibration effort can begin, the calibration plan and data collection 

procedures must be finalized, and because calibration is generally on the critical path of the 

program schedule, a detailed test schedule must be developed and followed. In addition, the GSE 

planned for use must be available and ready for testing. If possible, an engineering test should be 

conducted prior to the start of the pre-launch calibration data collection effort. 

5.2 ENGINEERING TESTING 

To mitigate the problem of identifying calibration shortcomings after the opportunity for corrective 

action has passed, it is recommended that an engineering test (or engineering calibration) be 

performed prior to the final calibration so that anomalies can be discovered and corrected prior to 

the final calibration. 

Engineering testing usually consists of collecting data from subsets of the planned measurement 

combinations and collecting data that can be used to formalize the content and timing needs for 

the calibration tests. The data obtained can be used to verify operation of the calibration hardware 

and test configurations, verify and refine the calibration test procedures, optimize the calibration 

measurement combinations, and verify the combined operation of the sensor and supporting 

hardware/software for the calibration test environment. Engineering testing can also help identify 

additional tests that may be needed to further quantify the sensor performance. In addition, sensor 

performance dependences on sensor operational environmental conditions can be determined. 

Performing these tests several weeks prior to the final calibration allows time to make appropriate 

procedural or hardware updates based on results and/or lessons learned (Tansock et al., 2004). 

Collecting data during the engineering test also provides an opportunity to assess the functionality 

of the data collection and data management systems, the data analysis tools, and the quicklook 

display, and also allows verification that the calibration data can be made available to analysts. 

Pre-launch calibration 

identifies sensor 

performance nuances that 

they can be addressed and 

understood before launch. 
52 

Data collection scripts for acquisition of calibration data can be refined through this process. The 

results of engineering testing can help calibration personnel prioritized the calibration schedule. 

5.3 GROUND SUPPORT EQUIPMENT (GSE) 

Special test equipment is required to perform ground-based end-to-end radiometric sensor 

calibration. While sensor component and subsystem measurements often occur under ambient 

laboratory conditions, they may require simulated space environment conditions. System-level 

measurements require the sensor to be operated under vacuum inside a cryogenic space simulation 

chamber. Therefore, the test equipment must be compatible with simulated space environmental 

factors such as vacuum, temperature, and contamination, and must provide the various calibration 

sources required for individual calibration tests. 

Sufficient time and resources must be allocated during the calibration 

planning phase to prepare existing equipment or develop and acquire 

the equipment needed to perform pre-launch calibration 

measurements. This equipment, typically referred to as GSE, includes 

test chambers, calibration sources, ESE, data collection and 

management system (discussed in Section 4.3.2), and sensor GSE. 

 Test Chambers 

Special test hardware must be used during calibration to simulate on-orbit conditions and to present 

specific scenes to the unit under test. Thermal-vacuum (TVAC) chambers provide the mechanical, 

electrical, and thermal configurations required by the sensor. The Space Dynamics Laboratory’s 

(SDL) Thermal & Optical Research Chamber (THOR) (SDL/10-242), is shown in the following 

photograph. 

SDL's Thermal & Optical Research Chamber (THOR) 

The calibration GSE 

should be tested and 

characterized prior 

to sensor testing. 
53 

Testing to achieve the desired parameters usually requires a variety of different calibration source 

configurations. One approach is to use a single TVAC chamber that holds multiple source 

assemblies. This type of chamber minimizes calibration time by reducing or preventing repeated 

cycle times associated with pump, cool-down, warm-up, and configuration times. This is 

particularly important for time-constrained projects where calibration is on the critical path 

(Tansock et al., 2004). The following example describes the single calibration facility used to 

successfully calibrate the SABER instrument. 

SABER Calibration Facility 

The calibration facility used for the SABER instrument calibration (Tansock et al., 2003) 

consisted of a test chamber interfaced with a collimator, and provided all the required 

radiometric calibration testing in a single test setup. SABER was mounted and operated in the 

test chamber, which also provided blackbody and knife edge calibration sources. The collimator 

provided source configurations for spectral, spatial, linearity, and temporal calibrations. 

(reprinted by permission of the publisher (Taylor & Francis Ltd, http://www.tandfonline.com)) 

 Calibration Sources 

Ground-based calibration sources provide well understood and/or repeatable flux levels as optical 

input to the sensor being calibrated. There are many commonly used ground calibration sources 

including spectral, spatial, linearity, radiance, irradiance, temporal, and scene generation sources. 

Spectral Sources 

Spectral sources include a variety of source types that produce an output with a content distribution 

that is known as a function of wavelength. Spectral sources can provide very narrow molecular 

emission or absorption lines, or can use spectroscopic techniques to separate the spectral content 

of a broadband source. Another option is to use a tunable laser to produce a very narrow spectral 

source that can be varied in wavelength. Different types of spectral sources are discussed in the 

following paragraphs. 
54 

Emission Sources There are many different types of emission sources, such as gas discharge 

lamps, that have emission lines with peaks at well-known wavelengths that can be used for 

wavelength calibration. Gases can also be used as emission sources under the right conditions. 

Fundamental atomic or molecular emission lines can provide well known wavelength references. 

These sources are often used to perform wavelength calibrations for spectrometers, spectrographs, 

or hyperspectral sensors, as well as for laboratory monochromators and spectrometers, which are 

also used to perform spectral responsivity calibration of broadband sensors. Well known gases in 

the earth’s atmosphere produce emission spectra that can be used for wavelength calibration of 

spectrometers, spectrographs, or hyperspectral sensors viewing the atmosphere from space. Uplooking space-based sensors can view novae, planetary nebulae, ionized hydrogen (H II) regions, 

certain galactic nuclei, and even some emission line stars and molecular clouds. These sources 

come in a variety of angular extents from true point sources to quite extended emission regions. 

The lines may be very narrow to quite broad. In some cases they can be used as an absolute spectral 

irradiance or radiance calibration source, especially if combined with high quality ground-based 

concomitant observations. 

Absorption Sources Different materials and gases can be used to absorb some of the energy from 

a broadband radiance source to provide wavelength calibrations. Fundamental atomic or molecular 

absorption lines can provide well known wavelength references. Absorption materials or gases in 

the optical path can be used to perform wavelength calibrations for spectrometer sensors, as well 

as for monochromators and spectrometers, which are used to perform spectral responsivity 

calibration of broadband sensors. NIST provides a number of Standard Reference Materials such 

as SRM 1921b – IR Transmission Wavelength (Polystyrene Film). NIST special publication 260-

146 shows an example of an absorption cell that is used to contain a well-known gas that can be 

used to provide wavelength calibration. The absorption of the atmosphere can be used to provide 

wavelength calibration when the uncertainties due to pressure and temperature are acceptable. 

Monochromator Monochromators use either a diffraction grating or a dispersion prism to 

separate the optical energy of a broadband source by wavelength. Monochromators produce output 

in a narrow spectral band defined by the monochromator slit. The monochromator output is 

typically coupled to a sensor through an optical system such as a collimator or re-imaging optics 

system. The spectral resolution and spectral range are a function of the source and the optical 

properties of the grating or prism and order sorting filter. A range of wavelengths can typically be 

scanned through automated control of the monochromator. Monochromators use lamps, 

blackbodies, LEDs, or other input sources. 

Fourier Transform Spectrometer (FTS) FTS systems such as standard commercial Michelson 

interferometers use interference between the different wavelengths of light, as a function of the 

difference in the length of two optical paths, to create an 

interferogram. The interferogram is measured by a detector and 

processed using a Fourier transform to produce spectra. For 

spectral calibration, the output of the FTS is typically coupled 

to a sensor through an optical system, such as a collimator or 

re-imaging optics system, and the interferogram is sampled by 

the sensor under test. The spectral range is a function of the 

source and optical properties of the beamsplitter and other 

optical elements in the interferometer. The spectral resolution Fourier Transform Spectrometer 
55 

is a function of the maximum optical path difference between the two optical paths. FTS systems 

use lamps, blackbodies, LEDs, or other input sources. 

Tunable Lasers Tunable laser systems, such as the NIST Spectral Irradiance and Radiance 

Responsivity Calibrations using Uniform Sources (SIRCUS) system (Brown et al., 2006), use an 

integrating sphere to convert a tunable laser output beam to a spatially uniform beam large enough 

to cover a sensor entrance aperture or to be coupled to a sensor through an optical system such as 

a collimator. The wavelength of the laser output is typically monitored during the testing with a 

monochromator or FTS. The output radiance or irradiance is typically monitored using a calibrated 

detector or transfer radiometer. The wavelength can be scanned by tuning the laser(s) over the 

desired spectral range. Information on the NIST SIRCUS is available from 

http://www.nist.gov/pml/div685/grp06/sircus_facility.cfm. 

Advantages and disadvantages of the various spectral sources are listed in the following table: 

Spectral Source Comparison and Summary 

Source Type Advantages Disadvantages 

Emission 

Sources 

‒ Repeatable peak wavelengths 

‒ Simple to use 

‒ Some emission lines have high 

intensity 

‒ Wavelengths are not tunable 

‒ Many emission lines are lower intensity than 

desirable 

‒ Wavelengths are limited by emission lines 

Absorption 

Sources 

‒ Repeatable peak wavelengths ‒ Wavelengths are not tunable 

‒ Wavelengths are limited by the absorption 

material 

Monochromator ‒ Simplicity of a single spectral line 

source scanned over the desired 

spectral range 

‒ Limited spectral resolution and throughput 

compared with other spectral calibration 

sources 

‒ Wavelength steps (i.e., spectral measurement 

resolution) must meet spectral measurement 

resolution requirements and be comparable with 

the spectral line width of the monochromator 

setting. This will limit throughput and may result 

in lengthy measurement duration 

Fourier 

Transform 

Spectrometer 

‒ Higher throughput and spectral 

resolution than monochromator 

‒ Spectral resolution can be easily 

selected for each test 

‒ Selection of OPD sampling 

interval allows test time to be 

optimized for the spectral band 

being tested 

‒ More complex data processing than other 

options 

‒ Requires sampling the sensor output in 

synchronization with uniform optical path 

differences 

‒ Typically provides a relative spectral 

responsivity measurement of the sensor 

Tunable Laser ‒ Higher output power levels are 

available than the other options 

‒ Can provide absolute spectral 

responsivity measurement of the 

sensor 

‒ Limited availability of tunable lasers at some 

wavelengths 

‒ Lack of automated tuning in certain spectral 

regions 

‒ Wavelength steps (i.e., spectral measurement 

resolution) must meet spectral measurement 

resolution requirements and be comparable with 

the spectral line width of the tunable laser. This 

may result in lengthy measurement duration. 
56 

Spatial Sources 

Many sources are used for spatial testing of sensors. Spatial testing includes measurement of a 

sensor's modulation transfer function (MTF), direct measurement of sensor imaging performance 

as a function of spatial frequency in a scene, and measurement of other sensor calibration 

parameters such as optical distortion, FOV, FOR, and scatter. 

The MTF of a sensor can be determined by measuring the edge response 

function (ERF) using a knife-edge source, by measuring the line spread 

function (LSF) using a slit source, or by measuring the PRF using a point 

source. Fourier transform techniques are used to derive the MTF from the 

measured image data. 

Measurements of sensor imaging performance as a function of spatial 

frequency can also be made by observing bar patterns. Image contrast 

between light and dark portions of each bar pattern is plotted as a function 

of the spatial frequency of each pattern. The resulting data is essentially a 

direct measurement of sensor MTF. 

Many sensor spatial tests such as optical distortion, FOV, FOR, and nearfield scatter can be measured using a point source or an array of point 

sources. Many of these spatial tests are performed with calibration 

equipment with the capability to direct the collimated beam into the sensor 

at different angles by directing the beam or rotating the sensor in the 

collimated beam. Spatial sources need to have high enough output power 

levels to enable high signal-to-noise ratio (SNR) measurements, preferably 

using short integration times to reduce the effects of jitter in the test system. 

Some spatial tests such as FOV mapping require many data sets with 

different point source locations to measure the relative response versus 

source location. These tests require a stable amplitude source. Some spatial 

tests such as optical distortion measurement can require many hours to 

complete. These tests require the calibration optical system to have very 

little drift over many hours. Drift can be measured during these extended 

duration tests by periodically returning the point source to a fixed or defined 

position, which allows the sensor being calibrated to measure the angular 

drift in the calibration optical system. 

Linearity Sources 

Linearity of sensor response over the full dynamic range can be measured in a number of different 

ways. Each measurement method requires different types of sources with significantly different 

requirements. Three popular response linearity measurement techniques are briefly described here. 

Linearity can be measured directly by looking at numerous absolute sources that span the full 

dynamic range of the sensor. In the IR spectral range, this can be achieved by absolute blackbody 

sources that span a range of temperatures that cover the dynamic range of the sensor. However, it 

is difficult to get absolute blackbody sources with the desired accuracy. Even with ideal sources, 

the change is spectral distribution resulting from changing blackbody temperatures can couple with 

RSR errors and uncertainties to confuse the linearity test results. 

Slit Source 

Spatial Frequency 

Response Bar Patterns 

Pinhole Aperture Array 
57 

Another linearity method commonly used is the small-signal 

linearity method (Kintner 2007; Bird 2002 and 2006; Larsen 

and Sargent 1997). The small signal linearity method requires 

a small signal source that can be modulated with very good 

repeatability coupled with a large signal source that can 

provide adequate output to operate anywhere over the full 

sensor dynamic range. The data are analyzed to look for 

changes in the response of the sensor to the small signal which 

defines the slope of the sensor responsivity across the 

dynamic range. This method does not require any knowledge 

about the small signal or large signal radiance. 

Another linearity method is based on a set of precision 

apertures that are used to change the flux entering a sensor. 

The aperture image must underfill the sensor IFOV, but while 

the source remains constant the flux falling on the detector 

depends only on the aperture size. Multiple overlapping data 

sets are taken using different source temperatures or power 

levels. The data sets are merged using the sensor response, 

which does not require any knowledge of the absolute source output. This method only requires 

that the sources are stable during the time period required to collect sensor data with each aperture 

in place. The method also allows the data to be used to verify the knowledge of the area of each 

aperture; since each aperture is used multiple times across the sensor dynamic range, any 

discrepancy in aperture area will show up as a systematic error. 

Additional linearity measurement approaches are described in Self-Study Manual on Optical 

Radiation Measurements (http://www.nist.gov/pml/div685/pub/studymanual.cfm 2010) and 

White et al. (2008). 

Radiance Sources 

Radiance sources, also referred to as full-field, flood, or extended area sources, are used for nonuniformity correction testing that requires a very uniform scene illumination. These sources are 

also used for absolute radiance responsivity calibration, which requires low uncertainty. Low 

uncertainty can be achieved be either having a source that is approaching an ideal source, or by 

using a sensor such as a transfer radiometer to measure the critical characteristics of a non-ideal 

source. Both types of sources need to have spatially uniform radiance output, or it becomes very 

difficult to characterize and correct for non-uniformities. 

An ideal blackbody source would have emissivity approaching one, small thermal gradients, and 

low temperature uncertainty. For blackbodies, cavity configurations provide higher emissivity and 

reduce the thermal gradient across surface treatments such as paint. Integrating sphere and diffuse 

surface based radiance sources can also provide very good uniformity. However, the spectral 

radiance must be calibrated using a sensor such as a transfer radiometer to achieve low uncertainty. 

Irradiance Sources 

Irradiance sources, also referred to as far-field, distant small area, or point sources, are used to 

simulate input to a sensor under test from a point source. Generally an optical collimator is used 

with a pinhole at the focus of the collimator that defines the spatial divergence of the source. The 

pinhole is back-illuminated by a radiance source such as a blackbody or integrating sphere. The 

An assembly of 8 small signal linearity 

source devices reflected in a beam 

combiner in front of an extended source 

that provides the required large signal. 
58 

intensity of a minimum divergence point source that can be simulated with a collimator system 

depends on the size of pinhole aperture used and the focal length of the collimator. Longer focal 

length collimators achieve the same output beam divergence and irradiance level with a larger 

pinhole. The diffraction losses for small apertures at long wavelengths can become significant, and 

should be included in calculating the output irradiance. The image quality of the collimator optics 

at the operational temperature can limit the minimum divergence that can be achieved. The image 

quality and focal length of the collimator should be measured at the operational temperature to 

characterize the system. 

Temporal Sources 

Temporal sources are used to produce a time varying output to measure temporal response effects 

in a sensor under test. Temporal sources may be implemented using a chopper combined with a 

lamp or blackbody at the input of a collimator system, pulsed sources such as LEDs or other 

devices, or a fast shutter. 

Scene Generation Sources 

Scene generation sources are used to produce a spatially structured input to a projection system to 

present a simulated real-world view to a sensor under test. Scene generation sources often include 

the capability to simulate motion in the projected scene for operational testing of tactical scenarios. 

Devices used to implement scene generation sources include resistor and LED arrays (Rice et al., 

2006), structured transmissive plates, and multiple sources with a beam combiner to generate a 

composite output. 

 Sensor GSE 

The GSE required for the sensor is provided by the sensor vendor, and may include computers, 

electrical interfaces, thermal interfaces, mechanical interfaces, software, and special tools for 

installation and removal. 

The sensor GSE is also typically used during sensor assembly, integration, and test (AI&T), 

spacecraft AI&T, and flight operations. Therefore, it must meet ground operation requirements as 

well as calibration requirements, which may include the ability to display real-time sensorresponse, the ability to receive commands from the calibration control computer for calibration 

automation, and the capability to collect a given number of data points/frames on command or 

hardware trigger. 

 GSE Preparation 

The quality of a calibration is only as good as the tools and references used to perform the 

calibration; therefore, the GSE used in the calibration must be well-characterized, stable, and 

accurate. The calibration hardware and software should be tested and characterized prior to the 

actual calibration to minimize schedule delays and to prevent a possible degradation in the 

calibration due to equipment issues. This process can take considerable time at a significant cost, 

and should be considered during the calibration planning. 

Calibration equipment anomalies during testing and problems with interfaces and/or automation 

of the testing hardware can all result in schedule delays. Delays can also result from incomplete 

characterization of test and calibration systems. If something questionable shows up during testing, 

it is critical to understand whether it is due to sensor performance or to an issue with the sources 

or test equipment. 
59 

The key requirements on the test equipment will be unique for each sensor, and must be identified 

and verified prior to calibration. As previously discussed (Section 4.1.8), the testing should be 

performed under the same environmental conditions that will be used during calibration. For 

example, if a collimator system will be used at cryogenic temperatures during the sensor 

calibration, the image quality and focal length of the collimator system should be measured at the 

same cryogenic temperatures. 

The repeatability of key calibration characteristics to temperature cycles should be measured and 

compared to other variables that will be changing during sensor calibration testing. There are many 

examples of calibration equipment characteristics changing due to thermal cycling, such as largerthan-acceptable focus shifts in collimators due to thermal loading from the temperature of the test 

chamber. Once a specific issue is understood, the anomalies can often be mitigated. 

Validating the specifications for commercial calibration equipment is also suggested. For example, 

a commercial blackbody manufacturer claimed their blackbody emissivity was greater than 0.98 

at all wavelengths. When requesting data for this specification, the vendor replied that the 

emissivity value was based on a reference that claimed that the average spectral reflectance of the 

type of material used in their blackbody cavity was below a certain value. Further investigation by 

the calibration team found additional data showing that the spectral reflectance of the material 

significantly degraded at the longest wavelengths tested. The calibration team then performed an 

independent measurement on samples of the blackbody cavity material and incorporated the results 

into a cavity enhancement model. This model showed that the spectral emissivity was not 

uniformly acceptable at all wavelengths, which the manufacturer had claimed. In fact, the model 

showed that the spectral emissivity was a function of the area of the blackbody cone. This 

information allowed the calibration team to tailor their test configuration when the higher spectral 

emissivity was required. 

It is also recommended to test calibration equipment against available NIST standards. It is 

common to have an unexpected effect in a calibration system, such as a straylight path, which 

would result in higher radiance through an aperture, or vignetting that would result in lower 

radiance than expected. Unexpected effects do not show up in uncertainty estimates, yet can 

dominate the accuracy of calibration systems. One study showed a comparison between the 

radiance temperature measured by the NIST Low Background Infrared (LBIR) Facility and the 

expected temperature from the platinum resistance thermometer (PRT) temperature sensors 

designed in the blackbodies (Carter, et al., 2006). The temperature errors range from -2 K to over 

10 K. This paper states that the “radiance temperature error usually exceeded temperature sensor 

uncertainty.” The paper describes work that was performed to calibrate the PRT temperature 

sensors mounted into the blackbody to reduce the temperature errors from strain effects on the 

PRTs and their electrical leads used to mount them into the blackbodies. In some cases, this 

increased the temperature error. The paper concluded that “using calibrated PRTs or calibrating 

PRTs after installation is no guarantee of radiometric accuracy, especially in cryogenic 

environments,” and “bath calibrations of the blackbody-mounted PRTs combined with radiometric 

calibrations of the blackbodies provide a useful tool for separating PRT strain issues from thermal 

gradient issues in blackbody temperature control efforts.” One of the points made in the paper was 

“blackbodies of the same design did not show the same temperature error,” which suggests that it 

would have been impossible to model or predict the temperature error from the blackbody design. 

Another study showed a comparison of the spectral irradiance between six calibrations chambers 

in use throughout the country (Carter et al., 2003). The results showed relative irradiance errors 
60 

that ranged from -10 % to 10 %. These appeared to be systematic errors because most of the results 

from the same chamber have a similar relative error for all of the different irradiance levels tested. 

When aware of a systematic error in a calibration source, it is usually possible to identify the source 

of the systematic error and improve the accuracy of the source. 

This discussion illustrates the benefits of using calibration equipment that has previous calibration 

heritage. The more time spent testing and operating calibration equipment, the less uncertainty 

arises from the calibration equipment itself. It is time consuming and expensive to perform 

numerous tests on a piece of new calibration equipment and repeat these tests over numerous 

thermal cycles of the equipment. 

 GSE Operation and Traceability Maintenance 

Individual pieces of equipment used for calibration often has a maintenance schedule to ensure 

accurate and proper operation. For example, the Air Force requires its contractors that use 

calibration tools such as volt-ohm-current meters to calibrate the tools annually against known 

reference standards. These meters are then distributed to the appropriate laboratories and are used 

to test and calibrate higher-level sensors. 

As the level of complexity of a calibration tool or reference increases, the level of calibration 

complexity is correspondingly elevated. For items that essentially must be calibrated through a 

laboratory operation rather than simply by comparison to a standard, a procedure must be written 

to ensure that the calibration being performed will adequately meet program requirements. Thus, 

while a square wave generator can be calibrated with an oscilloscope, a sensor that will be used to 

measure the thermal infrared radiance emitted by the surface of the Earth from space will need to 

be calibrated in a vacuum and at space-based operational temperatures. It will need to measure a 

radiance standard, with spectral calibrations being performed to obtain the RSR. 

Considering this example in more detail, the response of the sensor can be measured by using it in 

measurements of a radiance standard. The radiance standard can be established through knowledge 

of the emissivity and temperature of the surface. To show that the radiance standard will be 

accurate enough to perform the radiance responsivity calibration, the temperature sensor must, in 

turn, be calibrated. This can be accomplished through comparison to standard platinum resistance 

thermometers provided by NIST (with their own history and methodology of calibration) and used 

in a thermally uniform bath. Following that, either the surface emissivity versus wavelength or that 

of a witness sample coated at the same time as the blackbody must be measured. Using these 

parameters and data, the emitted radiance of the surface can be calculated. 

Unfortunately, there are issues with this path. For example, although the temperature sensor may 

be accurately calibrated and stable, if it is not in good thermal contact with the surface on which it 

is mounted, it cannot provide the correct temperature to be used in the radiance calculation. 

Similarly, if the blackbody material is not in good thermal equilibrium, the temperature measured 

by the sensor may not be representative of the material under the paint layer. This can happen if 

the blackbody is being heated well beyond ambient temperature, but there is no boundary shield 

around it to prevent the radiation from “leaking” out the sides. Furthermore, if the temperature 

sensor is a PRT that is cycled to cold temperature, the calibration may shift due to the thermal 

cycling, rendering the calculated radiance incorrect. 

In addition, the emissivity of the coating of the blackbody may change under various conditions 

such as temperature cycling and contamination. As a consequence, some programs have adopted 

a transfer radiometer as the standard calibration tool for radiometric transfer and traceability to 
61 

NIST standards. NIST has developed tools for radiometric calibrations of various types of 

calibration sources (http://www.nist.gov/pml/div685/grp04/transfer.cfm). Additional tools used 

for maintaining and trending changes in calibration are internal stimulation sources or other types 

of internal calibration units to trend the performance of the transfer radiometer, and thus promote 

an understanding of the calibration accuracy. In one instance, using a transfer radiometer and 

internal sources for trending resulted in the system staying in calibration for over 6 years. 

When the calibration accuracy of a piece of equipment is established by trending its performance 

over several years, it is sometimes acceptable to a funding agency to not recalibrate every year. 

Particularly for specialized calibration equipment requiring expensive and time consuming 

calibrations, such as a transfer radiometer or cryogenic blackbody, the challenge is to determine 

when recalibration is required. This can be done by establishing a plan for monitoring and trending 

calibration, comparing these results to requirements, and defining trending threshold(s) to indicate 

when recalibration is required. In this way, the frequency of recalibration is driven by the 

monitoring of calibration performance rather than by pre-defined recalibration intervals. NIST 

offers laboratory guidelines in their National Voluntary Laboratory Accreditation Program 

(NVLAP) for establishing laboratory procedures and general requirements (http://www.nist.gov/ 

nvlap/). Depending on the primary calibration role and the type of calibration equipment, an 

approach of monitoring and trending calibration will be unique to each laboratory. This 

emphasizes the importance for establishing a monitoring plan, identifying performance thresholds, 

implementing procedures, and reporting of trending results. 

5.4 PRE-LAUNCH CALIBRATION DATA COLLECTION AND DATA QUALITY 

ASSESSMENT 

During calibration testing, the data collection engineer will follow the procedure, fill in log entries, 

and make note of events or conditions that may affect the data. Real-time displays will provide 

feedback to verify proper instrument configuration, GSE configuration, and response levels. The 

completed data collection procedures, including any red-lined procedures and notes, should be 

archived as as-run procedures. 

Data quality checks should be performed throughout the data collection period. The goal of data 

quality assessment is to obtain confidence that the data can be used for the intended calibration 

analyses. In addition, information gained from the data quality checks can be used to help make 

informed decisions when changing calibration configurations, which can be a time consuming 

effort. 

Various methods can be used for data quality assessment, such as counting files and frames, and 

checking housekeeping and other important parameters. Quicklook analyses of subsets of the 

calibration data can also help evaluate data quality and can provide additional guidance to the 

remainder of the calibration campaign. It is best to use a combination of these data quality checks 

to provide confidence in the collected data. The data quality assessment approach is unique to each 

payload/sensor and should be addressed in the calibration plan. 

Data quality assessment can be performed both during and shortly after data collection. During 

data collection, the real-time display can be used to show that sensor response is consistent with 

the intended goals of each procedure. Observations of sensor and source configuration can be 

obtained from the ESE computer displays. Shortly after data collection the data can be processed 

through the data analysis software and examined for errors such as missing bytes, frame-to-frame 
62 

discontinuities, sensor and source configuration errors, and pixel and array response. The response 

statistics can be used to identify unexpected response variations. It may be necessary to add 

additional sensor-specific error checking to this process. In addition, the sensor response to the 

calibration source can be verified by performing an abbreviated analysis to show that the data are 

adequate to generate calibration parameters identified in the data collection procedure and to 

provide preliminary results. 

Daily status meetings are usually held during the course of calibration data collection to discuss 

issues found during data collection and data quality verification. Data quality assessment is used 

to determine when a calibration configuration can be changed (consent-to-break configuration), as 

well as determining the end of the calibration effort. 

5.5 QUICKLOOK ANALYSES 

Quicklook analyses are performed during testing shortly after data are collected for each test. 

These results will often be presented in the form of graphs and tables in a similar format to the 

intended final analyses. Sometimes assumptions are needed or corrections are preliminary or not 

available, but these analyses will still provide preliminary instrument performance results, and will 

provide confidence that the intended, more detailed analyses (usually performed post calibration 

testing) can be successfully completed. These results allow project leaders to make educated path 

forward decisions. An example of a quicklook analysis is shown in the following figure. 

Quicklook Analysis Example SABER Ground Calibration 

This example is an IFOV contour plot showing relative angular detector positions. The data 

were obtained by scanning a point source using the SABER scan mirror at each of 31 crossscan positions. These data were used to generate in-scan and cross-scan IFOV intensity 

profiles for each detector on the focal plane (Tansock et al., 2003). For this case, the quicklook 

results verified the sensor detector positional response requirements. 

(Tansock et al., 2000)
63 

POST-LAUNCH CALIBRATION

The goals of post-launch calibration are to verify and validate the calibration parameters 

determined pre-launch, characterize or update parameters that are more successfully characterized 

from on-orbit measurements, quantify on-orbit calibration uncertainty, and trend sensor 

performance and update calibration coefficients if necessary to meet measurement requirements. 

Phases of post-launch calibration include early on-orbit calibration operations, intensive 

calibration and validation (Cal/Val), and sensor performance trending during planned operations. 

Each of these phases has criteria and goals required to move on to the next phase. 

On-orbit calibration measurements are implemented using available resources to meet calibration 

goals, including on-board devices, stars, Moon, asteroids, and other celestial objects, crosscalibration, and vicarious calibrations. 

6.1 EARLY OPERATIONS 

Early calibration operations begin once the instrument reaches orbit, completes its initial bake out 

and checkouts, and is deemed functional. The goal of this phase is to verify that the response 

measured on the ground is similar to that measured on-orbit. Parameters that can be up-loaded and 

updated are completed. Gains and settings can be adjusted for optimal operation. Measured results 

can be incorporated into the temporal trending for 

mission life. An initial benchmark or test to determine 

initial performance values is usually conducted to 

determine if the sensor response is similar to ground 

testing. 

Early operations are also used to determine and 

investigate anomalous results. It is beneficial to the 

end users if experienced personnel have an 

opportunity to review a portion of the data to 

determine if the results look as expected. Experienced 

personnel can then perform studies and analyses on 

questionable results to resolve these issues. 

6.2 INTENSIVE CALIBRATION AND VALIDATION 

The intensive Cal/Val period begins when the sensor has been set up and is ready to collect data. 

The main goal of the intensive Cal/Val portion is to verify that the sensor is ready for operations. 

Testing during this phase includes deriving/verifying parameters that were not measured during 

ground testing or updating those parameters that can be determined during both pre- and postlaunch. Data collected during this phase can be used to determine parameter trending and result 

statistics. 

Early post-launch calibration 

operations testing activities: 

‒ Sensor optimization 

‒ In-flight calibration source calibration 

‒ Amplifier gain check and adjustment 

‒ Bit trim and impulse mask checks 

‒ NER difference (NEdN) check 

‒ Initial on-orbit trending 

‒ Anomaly investigation 
64 

Intensive Cal/Val testing may also include tests to investigate anomalies, such as changes in the 

responsivity or noise, and to check the spectral calibration using atmospheric lines or celestial 

emission line sources. The linearity of the detectors can be checked and adjusted, if required. 

Other common activities during the intensive Cal/Val period include: 

 Performing pointing calibration 

 Assessing and fine tuning the in-flight calibration source radiance model 

 Characterizing responsivity and sensitivity 

 Analyzing for spikes and ice contamination 

 Characterizing correlated/uncorrelated noise 

 Tuning and analyzing residual uncertainty 

 Performing cross comparisons with other sensors 

 Establishing on orbit trending baseline 

 Updating and finalizing calibration monitoring plans for mission operations 

The duration of intensive calibration varies with the mission objectives, planning, mission lifetime, 

and orbit. For example, sensors in low Earth orbit, which are used for atmospheric research, may 

take over one year to complete characterization testing due to the requirement for access to cloudfree ground targets. Small satellites with short lifetimes may be forced to perform only a limited 

amount of characterization testing due to time constraints. 

6.3 SENSOR PERFORMANCE TRENDING 

Sensor performance trending tracks long-term changes in sensor 

behavior due to component aging and/or sensor contamination, and 

provides a means for their correction, if necessary. This allows sensor 

calibration to be maintained throughout the mission life. The 

objective of sensor performance trending is to demonstrate that 

measurements collected continue to meet the standards required for 

the sensor, and that no changes have occurred that invalidate the 

measurement results. Sensor performance trending begins during pre-launch calibration and 

continues throughout on-orbit operations until the end of sensor life. 

Several major events occur between ground-based calibration and on-orbit operations that have 

the potential to change sensor performance, including sensor handling during spacecraft AI&T, 

launch-pad operations, launch, and on-orbit operations. Subtle sensor performance changes may 

also occur due to changes in thermal operating conditions, the electromagnetic 

interference/compatibility (EMI/EMC) environment, solar loading, mirror contamination, orbital 

procession, and radiation exposure, and random events such as cosmic ray hits and solar storms. 

On-orbit sensor performance trending measurements should be initiated during pre-launch 

calibration and continue throughout the sensor’s life time, including spacecraft integration/test and 

post-launch operations. Because of the significant environmental changes after launch and sensor 

deployment, the first on-orbit trending measurements should be performed as soon as possible to 

establish the on-orbit sensor performance baseline. This provides a performance snapshot that can 

be compared to AI&T trending, ground calibration, and spacecraft I&T trending. These early 

measurements quantify performance changes that may have occurred due to launch and provide a 

baseline for continued on-orbit trending. 

Sensor performance 

trending allows sensor 

calibration to be 

maintained throughout 

the mission life. 
65 

Sensor performance trending involves making repeated measurements of stable source(s) and 

deriving long-term sensor-response changes. Potential on-orbit sources include on-board sources; 

ejected sources; the stars, Moon, and other celestial objects; and solar diffusers. Information 

obtained from vicarious, cross-calibration, and pseudo-invariant calibration sites (PICS) can also 

be used for sensor performance trending. These on-orbit sources are described in Section 6.4. 

Dark measurements may also be used in sensor performance trending. Sensor models and 

performance trending data can be compared to identify changes in specific components, thus 

allowing appropriate changes to be made in the sensor calibration and/or sensor model to correct 

and/or compensate for the performance trend. 

Long-term performance changes can occur in the sensor spectral, spatial, radiometric, and/or 

polarimetric responsivity, and these changes may be interrelated. The impacts of sensor 

performance changes are specific to the sensor, mission objectives, and the ability to detect and 

make corrections. For example, a contaminant generated by spacecraft thrusters can have a larger 

impact on a mid- to long-wave infrared (MWIR, LWIR) spectrometer used to monitor atmospheric 

changes than on a visible Earth-imaging system. 

In an imaging system, long-term sensor performance trending must be capable of trending changes 

due to both global and localized events. Global events such as changes in the focal plane array 

(FPA) operating temperature or contamination of the primary mirror will have a similar impact on 

all pixels in the FPA, while localized events, such as cosmic ray hits, may impact a single pixel or 

small group of pixels. 

Regardless of the approach and specific details, sensor performance trending is critical to 

maintaining traceability to NIST calibration standards and deriving target measurement 

uncertainties. Sensor performance trending should be included in the calibration plan, incorporated 

into the sensor design, and included in on-orbit operations. 

6.4 ON-ORBIT CALIBRATION SOURCES 

On-orbit calibration measurements are implemented using whatever observable sources may be 

available to serve as a calibration source. These sources, summarized in the following table, 

include on-board devices, stars, lunar observations, other celestial objects, and the Earth's limb and 

surface (land and water). 
66 

On-Orbit Calibration Sources 

Name Description 

On-board sources 

Radiance and/or spectral reference sources that are contained 

within an EO sensor’s optical path, are moved in or out of the 

sensor’s optical path, or are viewed by means of a scan mirror 

while on orbit 

Ejected sources 

Sources that are ejected from the payload (Price et al. (2004) 

provides a discussion on the ejected reference spheres during 

the MSX mission.) 

Stars 

A limited number of stars that are in the IR spectral region and

also have stable intensity with proven/measured stability of ≤ 3 

% (Russell et al., 2012) 

Moon 

Natural Earth satellite with stable surface reflectance and no 

atmosphere (spatially and temporally variable, modeled at 

shorter wavelengths – USGS robotic Lunar Observatory (ROLO)

project (Kieffer and Stone 2005)

Other celestial objects Sun, planets, galaxies, dark space scenes 

Vicarious Natural or artificial sites on the surface of the Earth (CzaplaMyers, 2011; Blonski et al., 2012; Schiller and Silny 2010) 

Cross-calibration of onorbit instruments 

Comparison to a calibrated sensor in another orbit viewing the 

same Earth scene at the same time 

Solar diffusers On-board reflective surface that attenuates solar radiance to 

match sensor dynamic range (Xiong 2012; Guenther 2012) 

Pseudo-invariant 

calibration sites (PICS) 

Sites on Earth’s surface (typically desert regions) that have 

repeatable radiant properties 

 On-Board Calibration Sources 

On-board calibration sources provide the capability of periodically stimulating the sensor response 

with known and/or repeatable flux levels. These sources are generic terms for radiance and/or 

spectral reference sources that are contained within an EO sensor’s optical path, are moved in or 

out of the sensor’s optical path, or are viewed by means of a scan mirror while on orbit. In some 

cases an on-board calibration source may not qualify as a true calibration source in that it does not 

have inherently known calibration properties, but is highly repeatable and can be linked to a true 

calibration source during pre-launch calibration. 

On-board calibration sources provide a critical link between AI&T, ground calibration, early onorbit operations, and on-orbit operations over the life of the sensor. This link, known as long-term 

trending (LTT), is typically quantified initially via benchmark tests during ground or pre-launch 

calibration. Periodically throughout the sensor lifetime (including operations), these sources are 

used to monitor and trend the sensor's response. The LTT may result in a long-term repeatability 

uncertainty estimate or provide a means for responsivity trending and correction as sensor 

performance degrades or changes with time. 
67 

Often required to meet mission/sensor long-term repeatability requirements, on-board calibration 

sources are application specific and included as part of the sensor design process. The sensors are 

uniquely named for a specific program, and are therefore called various names, including internal 

calibration units (ICU), on-board calibrators (OBC), flight calibration sources, and internal 

calibration targets (ICT). 

Operational considerations, requirements, and design trade-offs for these sensors include: 

 Volume, mass, and power 

 Long-term source output repeatability 

 Magnitude, dynamic range, and stability 

 Temporal properties (such as time required for source to be considered stable and 

repeatable) 

 Radiation sensitivity to the on-orbit environment 

 Spectral content 

 Sensor response uniformity and repeatability over the sensor FOV 

 Absolute traceability to standards 

 Ability to exercise elements of system that have been identified to have potential 

degradation properties due to long term changes in optical transmissive properties and/or 

contamination 

 The extent of making internal calibration source measurements simulate on-orbit 

measurement (overfill sensor entrance pupil, the number of optical elements illuminated 

by internal calibration source, component angular considerations, etc.) 

Types of on-board calibration sources include grain-of-wheat lamps, near-field source (sometimes 

referred to as a Jones' source lamps), blackbodies, solar diffusers, and spectroradiometric 

calibration assemblies. Because the implementations of on-board calibration sources cover a broad 

range of sensor/mission applications, detailed designs and requirements are of little generalpurpose value and are not included in this publication. The following examples are provided, along 

with the associated publication references, for further information on a specific design or 

implementation. 

TIMED/SABER 

The SABER instrument used two approaches 

for the in-flight calibration (IFC) source designs: 

‒ Full aperture flat plate cavity enhanced 

blackbody for MWIR and LWIR bands 

‒ Near-field source lamps integrated into 

the full aperture blackbody for the near 

infrared (NIR) and short-wave infrared 

(SWIR) bands 

Calibration benchmarks were performed 

throughout the mission by rotating the scan 

mirror to view the blackbody and near-field 

sources (Tansock 2003). 
68 

MSX/SPIRIT III 

The IR SPIRIT III radiometer used grain-of-wheat lamps (i.e., tungsten filament) for the 

internal calibration sources and were extensively characterized before being integrated 

into the sensor (Miles 1991). These lamps provided relative on-board calibration 

sources that were used for long-term trending and repeatability of changes in sensor 

response. 

Aqua/Terra/MODIS 

MODIS is one of the key instruments for NASA’s Earth Observing System (EOS) (Xiong 

2003). The MODIS scan cavity and on-board calibrator (OBC) is shown in the following 

image. A description of the calibration and characterization approach of the OBCs can also 

be found in (Xiong 2003). The MODIS OBCs include a solar diffuser, a blackbody, space 

view, and a spectroradiometric calibration assembly. The performance of the MODIS onboard 

blackbody is described in Xiong et al. (2009). The spectroradiometric calibration assembly is 

used for the instrument spatial and spectral characterization, along with monitoring for MODIS 

on the Terra spacecraft (Xiong 2006). The on-orbit degradation of the solar diffuser is 

monitored by the solar diffuser stability monitor. 

MODIS Scan Cavity and On-board Calibrators (Xiong 2003) 

Suomi-NPP/CrIS 

CrIS is a Fourier transform Michelson interferometer instrument launched on board the 

Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite on 28 October 2011. 

CrIS uses an ICT and a deep space (DS) view for radiometric calibration. The ICT is an 

internal high precision blackbody that is not temperature controlled but allowed to 

remain in thermal equilibrium with the rest of the instrument environment. The ICT 

temperature is measured with two high-precision platinum resistance thermometers 

(Han et al., 2013). Tobin et al. (2013) provides a description of blackbody calibrations 

(including the ICT). The sensor data records (SDR) preprocessing module also 

computes laser metrology wavelength from neon calibration data (Han et al., 2013), 

which is used to monitor and identify changes in the spectral calibration. 
69 

CLARREO IR Spectrometer 

CLARREO aims to provide highly accurate and SI-traceable decadal change 

observations sensitive to the most critical but least understood climate forcings, 

responses, and feedbacks (Wielicki, et al., 2013). The CLARREO design is intended to 

provide SI-traceable reference standards on orbit for assessing the Earth’s climate 

change and providing reference intercalibration for other satellite instruments. With 

such critical and stringent standards of accuracy and SI traceability, the on-board 

calibration sources rely on multiple technologies to meet the improved SI-traceable 

absolute accuracy on orbit. The following figure shows the concept for the on-board 

calibration design for the IR spectrometer on CLARREO. The IR component of the 

sensor relies on phase change cells at -39 °C, 0 °C, and 30 °C to verify thermistor 

accuracy, quantum cascade laser and heated halos to verify blackbody emissivity, 

optics design to verify polarization sensitivity, and the quantum cascade laser with 

integrating sphere to verify instrument spectral response (Wielicki et al., 2013). An 

approach for obtaining absolute temperature calibration on-orbit is described in Best et 

al. (2008). The use of phase change references for in-flight recalibration of orbital 

thermometry is described in Topham (2011 and 2013). 

CLARREO concept for improved SI-traceable absolute accuracy in orbit (Wielicki et al., 

2013; ©American Meteorological Society; Used with permission) 
70 

AIRS 

The AIRS instrument uses a scan cavity that allows the instrument to view multiple onboard calibration sources, and includes an OBC blackbody (nominally 308 K) for 

infrared channels, a spectral reference source (Parylene) and a photometric calibrator 

for the visible/NIR channels (http://airs.jpl.nasa.gov/instrument/hardware/scanning/). 

The following figure is an image of the scan concept and the OBC blackbody calibrator. 

AIRS Scan Concept and Calibrator 

(http://airs.jpl.nasa.gov/instrument/hardware/scanning/) 

Quantum Cascade Laser (QCL) 

An emerging technology for on-orbit calibration sources is the quantum cascade laser 

(QCL), which, if used, could reduce the power requirements of the satellites. While this 

technology has not yet been flight qualified, recent studies at Pacific Northwest National 

Laboratories (Myers et al., 2007) show promising results for the use of QCLs as 

calibration sources, and studies at Utah State University (Hansen 2014) show that they 

can be used in space flight applications. Additional information on QCLs can be found 

at http://www.boselec.com/products/documents/AlpesQCLlit11-5-13.pdf. 

 Stars 

Stars provide true point sources that have been used for on-orbit calibrations for over 40 years. 

During this time, the absolute and spectral calibrations of the spectral energy distributions of the 

stars have improved dramatically in the IR, and have been shown to be excellent in the visible. 

On-going efforts to further refine these 

calibrations continue, but research 

shows that the visible and NIR 

calibration intensity are measured with 

relative standard uncertainty of about 1 

% or less, and the IR calibration (~ 2 

μm out to 14 μm) is good to 5 % or 

slightly better. The relative spectral 

uncertainty is believed to be <1 % in the 

visible and ~1 % in the infrared 

(Russell et al., 2007). 

Star 

Irradiance (W/cm2)*10-14 Location 

K Band 

(2.0 μm – 2.4 μm) 

L Band 

(3.0 μm – 4.0 μm) 

M Band 

(4.6 μm – 5.0 μm)

RA 

(deg) 

DEC 

(deg)

 Boo 27.4 13.2 1.25 213.33 19.43

 CMa 5.96 2.66 0.296 100.70 -16.63

 Lyra 1.64 0.744 0.0837 279.23 38.78

 Tau 24.0 11.7 1.05 68.29 16.39

 Gem 3.01 2.18 0.221 116.33 28.03

Selected stars have been characterized and provide 

known IR irradiance (Russell et al., 2012).
71 

Typically, an IR sensor will be tasked to view the brighter IR stars to maximize SNR for the 

observation and to minimize the length of time required to perform the calibration. For the IR, this 

often means using a variable star, but it has been shown that near simultaneous ground-based 

observing equipment, techniques, and data analysis methods are adequate to provide the high 

quality spectral energy distributions (SED) required for even the most ambitious current program 

calibrations (Russell et al., 2008). 

The SED is the brightness of the star at each wavelength over a span of wavelengths. The groundbased measurements can be used to normalize the theoretical models for the brightness of the star 

as a function of wavelength, allowing the in-band calculation of the brightness of the star even in 

regions where the Earth’s atmosphere is opaque, such as the short-wave infrared band near 3 μm. 

Ground-Based Data Set Compared to a Theoretical Model 

The following figure shows an example of a ground-based data set taken with The Aerospace 

Corporation’s Broadband Array Spectrograph System (BASS) on the Air Force 3.6 m Advanced 

EO System telescope on Haleakala, Maui (Russell et al., 2012), compared to a theoretical model. 

The theoretical model was normalized to measurements obtained by the Cosmic Background 

Explorer (COBE) spacecraft to provide an independent comparison with the measured spectrum. 

The dominate shape of the spectrum in the figure is that of a blackbody. The noise seen in the 6 

μm region is due to water vapor in the Earth’s atmosphere severely attenuating the signal. Effects 

due to terrestrial atmospheric CO2 (at 4.3 μm and > 13 μm) and ozone (O3) at 9.7 μm would be 

expected, but in this data set there is excellent atmospheric correction, and the only increased 

noise due to CO2 can be observed at > 13 μm and a few missing points near 4.3 μm. Expected 

absorption features due to the atmosphere of the star are almost nonexistent in these data, 

including features expected at 4.5 to 5.5 μm due to CO and 7.9 to 9.4 μm due to SiO. 

While the theoretical model for the stellar spectrum would usually have to be normalized to the 

data, it was not required in this case because of the strong agreement in the data from the COBE 

sensor and the current data, indicating that the intensity of the star has been quite stable. The 

higher spectral resolution model can be convolved with the bandpass of a sensor to obtain the 

truth value for in-band irradiance, even in regions that cannot be observed from the ground, such 

as in the 4.3 μm CO2 band. When multiple stars are used, they can be averaged to provide an 

accurate update to the point source (irradiance) responsivity on-orbit. 

Ground-Based Measured SED of Beta Oph (Russell et al., 2012)
72 

The truth value can also be used with the point source extraction algorithm to calibrate the effects 

of the algorithm on the point source intensity results. This can be particularly critical when the 

shape of the point response function changes between ground calibrations and the on-orbit use of 

the sensor, or if there is a temporal behavior of the imaging by the optics in the orbital environment. 

Due to their true point source nature, stars are ideal for determining the PRF of a sensor, especially 

those that look into space for such applications as space situational awareness (SSA). The only 

drawback in the use of stars for PRF determination is when a down-looking (nadir-viewing) sensor 

must slew away from the Earth to view a star and the observing conditions are necessarily different 

from those used during normal Earth-viewing operations. In this case, the state vector of the sensor 

may need to be considered. For example, if the heating of the sensor’s optics during Earth viewing 

is suddenly taken away when viewing a star against the cold background of space, there may be a 

change in the PSF of the optics, which may affect the measured PRF. This can usually be mitigated 

by viewing a star in close proximity to the Earth's limb, and minimizing the time used for each 

stellar observation. 

Closely associated with the PRF calibration is the characterization of energy on detector (EOD). 

As the EOD directly impacts the ability to perform image enhancement and to separate closely 

spaced objects (CSO), it is often a key performance parameter for a system. Isolated bright stars 

are ideal calibration targets for determining this property of a system, even if their absolute 

intensities are not well known. 

Omicron Ceti 

The very bright IR star Omicron Ceti (also known as Mira) is one example of a star that 

can be used to good advantage for PRF or EOD characterization, even though it varies 

by more than a factor of two in intensity in the IR on a time scale of months. 

Mira as seen by Hubble (NASA Image)

"Cetus IAU" by IAU and Sky & Telescope magazine (Roger 

Sinnott & Rick Fienberg). Licensed under Creative 

Commons Attribution 3.0 via Wikimedia Commons - 

http://commons.wikimedia.org/wiki/File:Cetus_IAU.svg#med

iaviewer/File:Cetus_IAU.svg
73 

In addition to the radiometric calibration of the sensors, stars can be used for line-of-sight (LOS) 

(pointing or goniometric) calibrations. While star trackers, inertial measurement units (IMUs), and 

gyros all play key roles in attitude determination and ephemeris generation, the use of in-scene 

stars for real-time pointing information can be the most straightforward and accurate method of 

establishing a target’s position and track. Having multiple stars in the FOV of the sensor at one 

time can provide a check on the focal plane vector table calibration obtained prior to launch, as 

well as the distortion map with the (sometimes unique) temperature distribution achieved by the 

optical train in the on-orbit environment. 

Star pairs (real binaries or apparent LOS binaries) can be used to assess performance against CSOs 

and to provide data sets for the development of point source extraction algorithms. These 

observations can also provide diagnostic information about the condition of the optics over time 

in the on-orbit environment. The simplest metric may be the focus of the optics, which can be 

measured on either isolated stars or on pairs. Similarly, assessing potential contamination buildup can be done through a comparison of the best obtainable PRF and the degree of near angle 

scatter (NAS). The NAS can be assessed through observations of the PRF on a single star or the 

performance of the system against CSOs. 

Stars are not optimal for calibrating the non-linearity of a sensor because the signal is usually 

spread over more than one pixel, but not uniformly over many pixels. However, the extracted 

intensity can be used to check the linearity calibration performed prior to launch for a few selected 

pixels. Similarly, the non-uniformity correction (NUC) can be checked by moving any bright, high 

SNR star to a number of locations on the focal plane and assessing the repeatability of the measured 

intensity. The NUC could in theory be recalibrated through the tedious and time consuming 

process of sub-pixel scans of a bright star across the FPA. 

Finally, while an on-orbit tunable calibrated spectral source for performing end-to-end RSR 

calibrations does not exist, the extreme range of stellar temperatures, from >30,000 K for earlytype stars to <800 K for dust-enshrouded late-type stars, does permit at least a crude assessment 

of the validity of the pre-launch or component-based modeled RSR in the on-orbit environment. 

If there are out-of-band spectral leaks, they can be strongly responsive to one temperature range 

of stellar radiation, while being almost completely non-responsive to the other end of the stellar 

temperature distribution. If the calibration coefficients derived from both hot and cool stars are 

consistent, the RSR being used is most likely to be fairly accurate. 

 Lunar Calibration Source 

Several distinctive properties of the Moon make it an attractive and useful 

source for on-orbit radiometric calibration. At reflected solar wavelengths 

the Moon behaves as a solar diffuser with an exceptionally stable surface 

reflectance, considered photometrically invariant to under one part in 108

per year (Kieffer 1997), the result of eons of exposure to the space 

environment. The sunlit Moon presents a spatially extended source with an 

overall brightness level similar to that of clear land surfaces. Lunar viewing 

is accessible from any Earth orbit, although many spacecraft instruments 

must have a capability for off-Earth viewing angles to observe it. A 

particular advantage of the Moon is the absence of intervening atmosphere 

between the source and the sensor. 

NASA image 
74 

The inherent stability of the lunar surface reflectance means calibration measurements of the Moon 

made at any time can be cross-compared. This enables sensor response trending with very high 

precision given a series of lunar observations acquired with sufficient frequency, and different 

instruments that have viewed the Moon can be cross-calibrated against the common lunar 

reference. Calibration against the Moon can be realized long after the observations have been 

made, including past the operational lifetime of the sensor. 

Earth-observing spacecraft instruments should acquire calibration measurements of the Moon 

regularly throughout their missions, at a minimum frequency of monthly. Multiple observations 

should be acquired at each lunar view opportunity, if practical. 

The observed brightness of the Moon is strongly dependent on the Sun–Moon–Observer geometry, 

primarily the phase angle but also the separation distances. Since observations from orbit can have 

any geometric configuration, the lunar radiometric reference is provided by an analytic model that 

is capable of continuous prediction of the lunar brightness with geometry. To date the most 

successful applications of lunar calibration have used the model for lunar spectral irradiance 

developed by the U.S. Geological Survey (USGS) Robotic Lunar Observatory (ROLO) project 

(Kieffer & Stone, 2005). The USGS model covers wavelengths from 350 nm to 2450 nm and phase 

angles from eclipse to 90 degrees before and after full Moon. 

Practical Considerations for Observing the Moon with Spacecraft Instruments 

Observing techniques for acquiring lunar calibration measurements vary depending on how the 

Moon is to be used, the sensor type, and the spacecraft orbit. To use the lunar spectral irradiance, 

the entire disk of the Moon must be captured in some manner, with quantitative accounting for any 

oversampling. The size and geometric shape of a sensor FOV can influence the scan sequence for 

acquiring a complete lunar disk and evaluating the oversampling factor. 

Because the Moon is a relatively dark target, there is an advantage to observing it at low phase 

angles due to the higher available signal from its increased brightness. However, the strong 

backscatter enhancement below 7 degrees known as the "opposition effect" leads to larger 

uncertainties in the lunar reference in this regime, and thus it is best avoided. 

The signal level registered by a sensor viewing the Moon also depends on the IFOV of the detector 

elements. Because the brightest features on the Moon have reflectances of about 0.2 at solar 

wavelengths, saturation typically is not an issue for sensors designed for Earth observations. But 

low SNRs can be encountered at the shortest wavelengths due to the combination of diminished 

solar irradiance and lower lunar reflectance. The expected radiance for a specified detector IFOV, 

wavelength, and phase angle can be predicted for lunar view planning. 

Low Earth Orbit An instrument that normally observes the Earth in nadir view from low Earth 

orbit must view the Moon either through an alternative optical path or by executing a spacecraft 

attitude maneuver. Accommodating lunar views with a space-look optical configuration is a 

common option for whiskbroom imagers. This requires accurate knowledge of the differences in 

sensor response due to the different optical angles and any additional optical components. 

Response characterizations from pre-launch instrument testing may not be reliable once on orbit, 

or after a substantial time in space. The MODIS instruments on the Terra and Aqua spacecraft are 

examples of instruments that view the Moon through a space-view port. 

The physical placement of the space-view port constrains the availability and phase angle of lunar 

observations, and a consideration for instrument design is that the look direction intersects the 
75 

Moon's orbit with sufficient frequency to develop a viable time series of lunar calibration 

measurements. The configuration used for viewing the Moon with NPP–VIIRS, which has rotating 

mirror fore optics, shifts the normal Earth-viewing imaging sector to view deep space while the 

Moon drifts through the view field in the down-track direction. 

Using spacecraft attitude maneuvers to view the Moon is advantageous in that the calibration 

measurements can be made using the same optical configuration as for normal data acquisition. 

The cost of this is increased complexity in flight operations. Because Sun avoidance is usually an 

important viewing constraint, lunar view maneuvers typically are conducted in the shadowed 

portions of low Earth orbits. Pushbroom imagers may need to turn and scan the Moon with a raster 

sequence. Turning a normal nadir-view line of sight toward the Moon imposes considerations for 

spacecraft thermal loading and radiative cooling systems. By executing pitch-over maneuvers, 

SeaWiFS acquired more than 200 lunar views during its 13-year lifetime, scanning the Moon 

through the view field using the spacecraft attitude motion. 

MODIS and SeaWiFS Moon Views 

The following figure shows two lunar images, one captured in space view by 

MODIS-Terra, and one acquired by SeaWiFS through its nadir-viewing optics. 

The MODIS-Terra image was taken on 29 November 2007 23:37:03 UTC in 

band 2 (858 nm) with 250 km ground resolution, resulting in a lunar disk image 

about 40 pixels in diameter. The SeaWiFS image was acquired on 14 November 

1997 22:50:09 UTC by scanning the Moon through the view field using the 

spacecraft attitude motion. The SeaWiFS lunar image is constructed from 

multiple time delay and integration (TDI) scans with 1.1 km ground resolution, 

and is about 8 pixels across track and about 20 pixels along track, where the 

along-track oversampling is determined by the spacecraft pitch rate. 

MODIS SeaWiFS 

(Courtesy of NASA) 

Geostationary Orbit From a position in geostationary orbit the Moon appears regularly behind 

the Earth several times in a month, traversing the Earth disk diameter (17.4 degrees) in about 80 

minutes. With the raster or spin-scanning techniques used by meteorological satellite imagers, the 

Moon is captured periodically by chance with normal imaging operations. 

The frequency of these chance captures is dependent on the imaging schedule. For example, the 

3-hour full disk interval used for most GOES operations results in only a few captures per year, 
76 

while 20-30 Moon images are found each year for the Meteosats, which operate on a 15-minute 

repeat cycle. A moon image captured by GOES-13 is shown in the following example. 

GOES–13 Visible Channel Image that Captured the Moon by Chance 

This figure shows an image from the Geostationary Operational Environmental 

Satellite (GOES)–13 visible channel that captured the Moon by chance (upper 

right corner). This operational Northern Hemisphere sector image was acquired 

21 Jan 2011 19:15:19 UTC. 

(Courtesy of NOAA) 

If the operational schedules for a geostationary imager results in too infrequent (i.e., less than 

monthly) captures of the Moon by chance, then dedicated lunar observations are warranted. Some 

next-generation geostationary sensors currently operational (e.g., Multifunctional Transport 

Satellite (MTSAT)–1R) or under development (e.g., GOES–R) have scanning sequences that 

acquire full disk images with a near-circular FOR, overscanning the Earth disk by a few degrees. 

In such cases, capturing the Moon's disk completely within a narrow overscan margin runs a risk 

of contaminating the lunar calibration data with stray light from the Earth's limb and atmosphere. 

Scanner gimballing constraints should allow slewing away from the Earth and scanning the Moon 

with dedicated lunar observations. This can impact normal imaging operations, however.

Infrared Sensors Extending lunar calibration to infrared wavelengths requires characterizing the 

Moon's thermal behavior with sufficient detail for modeling. This is a substantial challenge. Lunar 

IR isophotes display complex spatial structures at any given phase angle (Saari and Shorthill 1972), 

and there are non-uniformities over the surface and strong gradients from the meridian to the 

terminator (Shorthill and Saari 1965). Characterization measurements acquired from ground-based 

instruments require correction for the thermal signatures originating in the Earth's atmosphere, 

which can be highly variable and a significant source of measurement uncertainty. Close to the 
77 

sub-solar point the brightness temperature of the lunar surface reaches ~400 K, which can saturate 

IR sensors designed for Earth observations. 

Radiance Measurements Using the Moon as a spatially resolved target (i.e., as a radiance source) 

adds complexity to the lunar calibration task. Images of the Moon taken by an instrument must be 

spatially co-registered with the radiance model reference. This typically involves spatial scaling, 

and possibly also corrections for distortions, typically employing a camera model for the imager. 

Development of a calibration reference for lunar radiance requires obtaining characterization 

measurements of the lunar surface reflectance that cover wide ranges of all three photometric 

angles (incidence, emission, and phase angle) at the spatial and spectral resolutions desired for the 

resulting model. At finer spatial scales, local surface topography and shading influence the 

radiance measured by both the instruments being calibrated and the characterization observations 

acquired for reference model development. Several spacecraft missions to the Moon over the past 

decade have acquired substantial volumes of high-resolution image data from lunar orbit (e.g., 

Lunar Reconnaissance Orbiter (Chin et al., 2007); Moon Mineralogy Mapper (Pieters et al., 2009); 

SELENE/Kaguya, (Haruyama et al., 2008)). These datasets can provide the basis for a detailed 

lunar radiance model if their radiometric calibrations can be validated. 

Calibration of Imaging Sensors Against the Moon

Realization of a lunar calibration consists of comparing radiometric measurements of the Moon 

derived from instrument observations against the corresponding reference quantity provided by a 

lunar model. To use the lunar spectral irradiance with an imaging instrument, a complete image 

of the Moon's disk must be obtained. If the image is clipped, the missing portion must be accounted 

for, and achieving high precision requires detailed image analysis with reference to a lunar albedo 

map. Irradiance measurements from images involve summing pixels on the lunar disk after 

correcting for detector artifacts and converting the pixels to radiance, as shown in the following 

equation: 

 N E  

pix i i L 

where Ωpix is the solid angle subtended by one pixel, Li is the radiance measure of pixel i, and the 

summation covers all pixels on the Moon N. The process of summing pixels provides a signal-tonoise advantage. The particular selection of on-Moon pixels can affect the accuracy of the 

irradiance measurements. Often it is desirable to expand the area enclosing the lunar disk to include 

some of the surrounding deep space region to capture the wings of the sensor spatial response 

function. For scanning instruments, it is typical and desirable to oversample the Moon. 

Oversampling can be adequately corrected by simple scaling if the oversampling is uniform over 

the image. 

Development of the Lunar Radiometric Reference 

Using the Moon as a calibration source requires quantitative and accurate knowledge of its 

brightness for the particular conditions of instrument observations. The complications of 

developing such a reference arise from the strong dependence of the lunar surface reflectance on 

illumination and viewing angles, and also the dependence of the scattering phase function on the 

composition of the surface materials, which governs the local albedo. However, the geometric 

brightness variations are smooth and periodic, and thus are predictable and can be modeled. A 

practical lunar radiometric reference is derived from an extensive set of characterization 

measurements of the Moon that cover a wide range of illumination and viewing geometries. These 
78 

basis measurements are rendered into an analytic model that provides a continuous predictive 

capability to accommodate the geometries of any instrument observations. The lunar reference, 

or lunar radiometric standard, is then provided by the model. 

The accuracy of any lunar model is governed by the quality of the basis measurements and the 

suitability of the model formulation to describe the Moon's radiometric behaviors. The inherent 

stability of the Moon's reflectance exceeds current instrumentation capabilities for measuring it. 

The lunar radiometric reference currently used most widely for calibration purposes operates with 

spatially integrated spectral irradiance. Using this quantity adds the complication that the model 

must account for the brightness variations due to the particular hemispheres of the Moon that are 

illuminated and viewed (i.e., the lunar librations). The USGS spectral irradiance model (Kieffer 

and Stone 2005) covers the full range of librations viewable from the Earth's surface 

simultaneously with its phase angle coverage. This model is capable of predicting the geometrydependent variations in lunar brightness with a relative precision <1 % over the full range of phase 

angles and librations. The uncertainty in its absolute scale is about 5 % to 10 %. 

 Other Celestial Object Calibration Sources 

In addition to the more obvious celestial sources, asteroids and planets can also be used as 

calibration sources. Asteroids are bright, near room temperature targets that can be used as point 

sources for all but the highest spatial resolution sensors. Mars can also be a point target or “fat 

spot” target, depending upon its distance from the sensor and the spatial resolution of the sensor, 

and has a temperature around 245 K. 

It is advantageous to use calibration sources in the same temperature range as a class of targets to 

allow any spectral peculiarities of the sensor, such as out of band (OOB) spectral leakage, to be 

included in the calibration and to minimize the impact on the quality of the result. 

The use of these calibration sources has some disadvantages, however. Almost all asteroids rotate 

with a time scale of hours, and some of them exhibit spectral variations as a function of what part 

of the surface is being measured. In addition, asteroids are in orbits that change their distance from 

the sensor, and at times could be too far away to be used at all. This temporal variability issue can 

be addressed through simultaneous ground-based observations, or the selection of a particular 

asteroid that does not exhibit large changes in spectral intensity or shape. Most of the asteroids, 

as well as Mars, can be fairly well represented by a Planck function, but the deviations can be 

characterized by spectral measurements from the ground. Mars is also known to exhibit dust storms 

and a phase angle effect. Dr. Ned Wright of UCLA has developed a model for the emission from 

Mars that has been applied in the use of Mars as a calibration source with good results (Wright 

1976; Wright and Odenwald 1980; Wright 2007). 

Another type of celestial source is a line emitting region, such as a planetary nebula, ionized 

hydrogen (H II) region, nova, Seyfert galaxy, or even a planetary atmosphere (Neptune has 

multiple molecular bands in the LWIR, for example). These line emitting regions may be quite 

stable or very transient in nature. Some of them exhibit isolated strong lines, such as the 10830A 

(He I) line in a nova, or the forbidden lines due to Ne II, S IV, and other ions in the IR spectra of 

planetary nebulae. Some exhibit line pairs, such as the Na or O lines in planetary nebulae. Neptune 

has already been cited for the vibration-rotation bands in the LWIR. These can be used to check 

the spectral calibration, the spectral resolving power, the instrument function, and dispersion in 

general of spectral sensors. 
79 

 Vicarious Calibration 

Vicarious calibration is a well-developed method for EO sensor calibration that has been used 

since the early 1980s. It is a fairly generic term indicating a calibration performed in a manner not 

directly connected to the satellite system under consideration. Thus, it could include the concept 

of using processes largely external to the satellite, such as viewing the Moon or other celestial 

objects, as opposed to using on-board calibration devices such as lamps. Over the years, though, 

the term ‘vicarious calibration’ has come to be closely associated with a particular calibration 

methodology that involves viewing the Earth and deploying a team to make ground level 

measurements of the test site at the time of satellite overpass (Helder, et al., 2012). This is the 

loose definition that will be used in these guidelines. 

Vicarious calibration has several advantages including availability of acceptable sites at multiple 

locations on the Earth, calibration based on actual Earth scenes, normal operation of the satellite 

sensor, and improving methodologies. Disadvantages include the expense and time of transporting 

people and equipment to the test site, susceptibility to cloud cover, and site maintenance. 

Vicarious calibration typically denotes making measurements of the Earth’s surface to obtain 

estimates of the surface reflectance at a particular location. Simultaneous measurements are made 

of the atmosphere so that the propagation of the electromagnetic radiation through the atmosphere 

is adequately understood. These two sets of measurements allow characterization of both the 

Earth’s surface reflectance and the atmosphere so that, in combination with a model for solar 

radiation, a prediction can be made of the radiance present at the top of the atmosphere and at the 

aperture of an orbiting satellite. The last element needed in this modeling effort is a radiative 

transfer code that uses the surface reflectance and atmospheric measurements as inputs and 

predicts top of atmosphere radiance (Moran et al., 1992). Calibration then occurs by comparing 

the measurement made by the satellite to the prediction based on the surface measurements and 

calculating a correction factor that is essentially just a ratio of the predicted and measured value 

(Schott 2007; Thome et al., 2004; Naughton et al., 2011). 

Site selection is the first step for vicarious calibration. Preferred locations are those where the 

surface and the atmosphere are stable and relatively easy to characterize. Bright surfaces are often 

preferred simply because of the higher SNR that they offer, which leads to better precision in the 

calibration estimates. However, two drawbacks of bright 

surfaces are that they may not be similar to the targets 

that are most often of interest to users of the satellite 

data, and they only characterize a small part of the 

dynamic range of the sensor. Thus, darker sites are also 

valuable to provide multiple calibration points along the 

dynamic range. Darker sites are also more typical of the 

types of targets that users of the satellite data monitor. 

Spatial homogeneity is preferred because it makes 

characterization of the surface reflectance easier to 

accomplish. Some of the well-known bright calibration 

sites include Railroad Valley and Ivanpah Playa in 

Nevada/California, and White Sands, New Mexico 

(Bannari et al., 2004). An example of a well-used darker 

vegetative site is located at Brookings, South Dakota 

(Thome et al., 2004). 

Portable spectrometer for collecting surface 

reflectance measurements at a vegetated site 
80 

Surface reflectance characterization is often considered the element of vicarious calibration that 

has the largest uncertainty (Thome and Fox 2011). Thus, it is important to carefully consider how 

this measurement should be performed. Since no site is completely spatially uniform, it is 

necessary to sample the surface reflectance at multiple locations across the site. The sampling 

technique is determined by the site itself and the equipment and human resources available for the 

measurement. The most typical approach is for an operator to traverse the site with well-calibrated 

portable equipment; often this is done simply by walking the site with a broad range 

spectroradiometer to measure radiance levels from both the ground and from a calibrated reference 

panel to produce a hyperspectral ground level reflectance (Thome, 2001). For large sites 

appropriate for where the IFOV of the sensor may be on the order of hundreds of meters, it may 

be necessary to use some type of vehicle to cover the entire site in a short enough time period such 

that the atmosphere has not changed significantly during the course of the measurement and the 

satellite overpass. Automated surface reflectance measurement approaches are being developed at 

sites where the number of samples needed is low. 

Schiller and Silny (2010) have pioneered an alternative vicarious calibration technique using 

spherical reflectors. This method does not require ground characterization but instead uses 

measured solar illumination to derive the atmospheric transmission. This method can be used in 

many different locations. 

Measurement of the optical properties of the atmosphere needs to be performed 

at the time of satellite overpass so that energy propagation through the 

atmosphere can be accurately modeled (Schott 2007). Various types of 

instruments have been used for this purpose; perhaps the most common is a 

sun photometer that tracks the sun and directly measures solar downwelling 

radiance. However, other instruments are available that measure both the direct 

and diffuse solar irradiance. Wavelengths for measurements are carefully 

chosen to be within regions that can be optimally used with the radiative 

transfer code for accurate atmospheric modeling. Typically, these 

measurements are performed at the time of satellite overpass. However, in 

many situations, measurements are taken throughout the entire day to monitor 

the stability of the atmosphere, detect the presence of clouds, or perform 

Langley analyses both for optical depth measurements as well as for 

instrument calibration. 

Ancillary measurements are often necessary as inputs to the radiative transfer code (RTC). 

Examples include meteorological measurements such as air temperature, air pressure, wind speed, 

and surface elevation. Once these measurements have been collected and reduced to overall 

average values (for surface reflectance) or appropriate point values (for temporal atmospheric 

measurements), they are used as inputs to the RTC. Commonly used models for the visible through 

thermal wavelength region include MODTRAN and 6S (Kotchenova et al., 2008). The purpose 

of the RTC is to couple the surface, atmospheric, and ancillary measurements together to form an 

overall optical model for the electromagnetic radiation propagation from the sun through the 

atmosphere, reflecting off the surface, and propagating back up through the atmosphere to the 

sensor. Often this is an iterative process involving human interaction, but efforts are underway to 

automate it as much as possible. The output from the RTC generally is the spectral radiance at the 

top of atmosphere as a function of wavelength. By selecting the wavelength range corresponding 

with the precise spectral response of the sensor, a process generally termed as ‘banding’, estimates 

Commonly used Sun 

photometer for 

vicarious calibration 
81 

can be developed of what the sensor should be receiving, and compared with actual measurements, 

to perform a calibration. This can be done in units of radiance or in terms of apparent reflectance. 

Continuous improvements in sampling and measurement techniques and equipment, in 

conjunction with improved RTCs, have allowed uncertainties to decrease from 10 % in the 1980s 

down to the 3 % (k = 1) level in the visible and infrared regions today. Vicarious calibration 

remains an excellent independent complement to other forms of on-board calibration. 

 Pseudo Invariant Calibration Sites (PICS) 

Stable sites on the Earth's surface, known as pseudo invariant calibration sites (PICS), have been 

used in a variety of ways for the calibration of EO instruments for remote sensing over the past 

two decades. Common PICS include dry lakebeds, salt flats, and desert sand sites in arid regions 

that have low probability of cloud cover, are spatially homogeneous, and have relatively constant 

surface spectral reflectance and bi-directional reflectance distribution function (BRDF) over long 

periods of time. They are inexpensive to use because no target maintenance or team deployment 

is required. They have shown excellent stability and accuracy and can be used with a wide variety 

of sensor types. 

Key to the usefulness of PICS is the concept that they 

don’t change radiometrically as a function of time. 

While in theory this is impossible, in practice it has 

been found to be a good approximation with an 

uncertainty in the range of 1 % to 3 % (k = 1), which 

is significant given that the atmosphere is included in 

the overall PICS concept. It is no surprise that the 

most stable sites in the world are found in the Sahara 

Desert (Cosnefroy et al., 1996; Helder et al., 2010; 

Mishra et al., 2014). Other sites have been identified 

throughout the world in major desert regions. An 

excellent listing of PICS is maintained at the USGS 

Remote Sensing Technologies website: 

http://calval.cr.usgs.gov/rstresources/sites_catalog/radiometric-sites/test-sitegallery/. Most of the PICS are located in large 

uniform areas and are suitable for moderate-to-coarse 

resolution sensors with IFOVs in the range of 30 m 

to 1 km. High resolution sensors (IFOVs in the range 

of 1 m to 10 m) have also been calibrated using PICS 

by aggregating pixels over larger areas. 

Unfortunately, it can be difficult to obtain ground 

truth at PICS sites due to the harsh environment and 

the political unrest often present in the areas. 

Long-term trending of EO sensor radiometric calibration was the first use of PICS (Cosnefroy et 

al., 1996; Barsi et al., 2012; Chander et al., 2010). This is easily achieved by simply acquiring 

imagery over the site repeatedly to determine if any trends exist. The variability that is inherent in 

such measurements must be minimized for optimal trending purposes. This can be accomplished 

by imaging at the same time of year to minimize seasonal variations, imaging with the same 

PICS Landsat 7 image 

This well-known PICS, Libya 4, is located in the 

Libyan desert. Improvements of an empirical 

absolute calibration model have been achieved 

using the NASA Terra MODIS (Mishra et al., 2014) 

The red rectangle indicates the CEOS suggested 

region of interest. Sand dune features are present 

throughout the image but are particularly apparent 

in the lower right hand corner. 
82 

viewing angle to minimize BRDF effects, imaging the same location exactly to maximize spatial 

homogeneity, and choosing an optimal location to minimize loss of data due to cloud cover. An 

example of long term trending of the Landsat and MODIS sensors is shown in the following figure. 

This plot clearly shows the stability of the Libya 4 site, but also indicates the uncertainties present 

in the measurements. Stability for this particular site and these two sensors is 0.87 % for the 

Landsat 7 ETM+ and 1.1 % for the MODIS Aqua instruments. When trending sensors that have 

been operational for a decade or more, changes in sensor gain as small as 0.2 % per year are 

detectable. 

An example of long-term trending of the Landsat and MODIS Sensors 

This figure shows the temporal trending of Landsat 7 ETM+ and MODIS Aqua 

over their lifetimes using the Libya 4 PICS. 

Another use of PICS has been in the cross-calibration of satellite sensors. An optimal method of 

cross-calibration is the use of simultaneous nadir overpass (SNO) observations, which allows two 

sensors to view a target at essentially the same time (Lacherade et al., 2013; Helder et al., 2012; 

Chander et al., 2013). However, this can be difficult to do and is dependent on sensor orbital 

characteristics. Fortunately, for PICS, the constraint of simultaneous observation can be relaxed. 

Because of the stability of the surface, as well as the atmosphere, observations can be several days 

apart with minimal differences caused mostly by small atmospheric changes. The figure in the 

above example illustrates this by comparing the temporal trend in the red band for ETM+ and 

Aqua. Cross-calibration for a wide variety of government and commercial sensors has been 

accomplished with the Saharan PICS. Sensors that are known to be well calibrated and stable such 

as Landsat and MODIS are often used as the reference sensors for cross-calibration. 

More recently, efforts have been under way to use PICS for absolute radiometric calibration. Two 

methods have been developed: one basically uses the concept of a calibrated detector, and the other 

exploits the idea of a calibrated source. In the case of the former method, a known well-calibrated 

satellite sensor can be used as the reference radiometer (Govaerts et al., 2012; Helder et al., 2013; 

Mishra et al., 2014). Using data from this sensor, as well as hyperspectral data available either 

from the surface or a second sensor, a reasonably simple model for predicting TOA reflectance 

can be developed for a range of illumination and viewing angles that is valid throughout the visible 
83 

and infrared regions of the spectrum. Models such as this have been shown to be accurate to 3 % 

or better, but are ultimately based on the accuracy and traceability of the reference sensor. The 

second approach for absolute calibration relies on using the Sun as a calibrated source. In this 

method, a solar model is used in conjunction with a surface BRDF model and an atmospheric 

model. These models are combined to predict top of atmosphere spectral radiance or reflectance 

as a function of date/time, viewing and illumination angles, and spectral band. Although more 

complicated, this approach has the advantage of not being dependent on the calibration of a 

reference EO instrument. 

 On-Orbit Cross-Calibration 

On-orbit instruments must be cross-calibrated to assess the consistency of observations across 

satellites. This, in turn, affects the inter-operability in global Earth observations, as well as climate 

change detection. While many methods have been used for cross-calibration, this section focuses 

on the direct comparisons of satellite radiometer measurements on-orbit, for which one particular 

method, the SNO method, has gained popularity in the past decade (Cao and Heidinger 2002; 

Heidinger et al., 2002; Cao et al., 2004, 2005; Zou et al., 2006). 

The SNO method is an extension of a similar method used in 

laboratories to perform cross-calibration of on-orbit satellite sensors. 

Radiometer cross-calibration in laboratories relies on viewing the 

same calibration target (such as a blackbody or integrating sphere) in 

near identical conditions. In contrast to the laboratory environment, 

identical viewing conditions of Earth targets are far more difficult to 

recreate due to the dynamics of the atmosphere, Earth surface, and 

the solar illumination. Therefore, SNO observations become 

necessary for cross-calibration of radiometers on different satellites. 

The SNO method is simple and robust, and is based on the fact that 

any pair of polar-orbiting satellites flying at different altitudes 

regularly observe the Earth at their orbital intersections at nearly the 

same time. The frequency of occurrence is a function of the orbital 

period differences driven by the altitudes. Observations from the two 

satellites at the SNOs can then be collocated pixel by pixel and the 

biases between them can be quantified. A time series of the biases at 

the SNOs further reduce the uncertainties and allow for the long-term 

trends to be studied. 

Since the SNO method was introduced more than a decade ago, it has been accepted for intersatellite calibration for a number of applications. The method was first used for the operational 

monitoring of intersatellite biases at NOAA. It was then introduced to the climate community for 

constructing time series for decadal climate change detection (Zou et al., 2006; Zhao et al., 2008; 

Cao et al., 2005b). In 2005, the World Meteorological Organization initiated the Global Spacebased Inter-Calibration System (GSICS) (Goldberg et al., 2011) and adopted the SNO as one of 

the key methods, which greatly facilitated its use across countries and agencies. While the SNO 

method was initially developed for polar-orbiting satellite pairs, the GSICS community further 

expanded the method for inter-calibration between polar and geostationary satellites, which are 

commonly owned by the meteorological agencies of a number of countries. 

Simultaneous Nadir 

Overpass (SNO) 

(Courtesy of NOAA) 
84 

The SNO method has been applied to all major categories of instruments, including microwave, 

visible/near-infrared, thermal infrared, and ultraviolet. The successful use of the SNO method for 

microwave instruments inspired a number of studies. The launch of hyperspectral sounders in 

recent years greatly reduced the uncertainties in spectrally induced biases due to different spectral 

response functions, which also makes the SNO method more useful than ever for atmospheric 

sounders (Wang et al., 2007, 2008, 2009, 2011; Chen and Cao 2011). 

With the launch of the Suomi-National Polar-orbiting Partnership satellite (S-NPP) in 2011, the 

SNO method has been used for all Earth observing instruments in post-launch Cal/Val. For 

example, cross-calibration between S-NPP/VIIRS (http://npp.gsfc.nasa.gov/viirs.html) and 

Aqua/MODIS (http://modis.gsfc.nasa.gov/about/) using the SNO method helped identify the 

calibration bias in the MODIS blue band in collection 5, and confirmed the correction in MODIS 

collection 6 (Blonski et al., 2012). SNO time series between VIIRS and MODIS helped identify 

issues in the VIIRS lookup table updates and deficiencies (Uprety et al., 2013). 

The SNO method can also be used in the low latitudes between satellites in similar orbits (Cao et 

al., 2005b; Uprety et al., 2013). This has been demonstrated in the post-launch Cal/Val between 

VIIRS and Aqua/MODIS, which led to the more rigorous analysis of the biases at different scene 

types and dynamic ranges, which further reduces uncertainties. 

The SNO method is expected to be continuously used for on-orbit cross-calibration. It is 

envisioned that with the launch of GOES-R in 2015 and similar instruments in other countries, 

such as Japan, the VIIRS instrument will be cross-calibrated with those in the geostationary orbit 

with reduced uncertainties due to the similar spectral response functions in many of the channels. 

A coordinated effort across countries and agencies will ensure the consistency and reliability of 

the global Earth observations. 

One weakness of the SNO method is the fact that it relies on the assumption that a stable reference 

satellite can be used in the cross-calibration. Other methods, such as the vicarious method, can 

determine absolute values more accurately. Therefore, the SNO method should be used in 

conjunction with other methods for cross-calibrating on-orbit instruments. It is worth mentioning 

that the CLARREO team has carefully studied the opportunities for cross-calibration and intend 

to have CLARREO serve as an on-orbit reference (Wielicki et al., 2013), although its realization 

may still be many years away. 

Aircraft underflight and ground-based Cal/Val provide important feedback to the cross-calibration 

of satellite instruments, especially for geophysical variables where the truth relies on 

measurements near the surface, such as the Marine Optical Buoy for ocean color, and sea surface 

temperature. Airborne instruments have the advantage of performing frequent calibration that can 

be made traceable to an absolute standard before and after the flight. Ground-based systems also 

have the advantage of sustainability. However, both airborne and ground-based systems have 

large differences in spatial sampling compared to satellite observations, which also introduce 

uncertainties. 

 Solar Diffusers 

Solar diffusers have been used as on-orbit calibration targets in a number of Earth-observing 

sensors in the reflective solar spectral range, from UV to SWIR. There are different types of solar 

diffusers depending on their calibration requirements and design considerations, such as aluminum 

plates painted with YB71 white paint (used in SeaWiFS and Landsat ETM+), space-grade 

spectralon panels (used in MODIS and VIIRS), and ground aluminum diffusers and volume 
85 

diffusers (used in the Ozone Monitoring Instrument (OMI)). The solar diffusers can be used for 

absolute or relative radiometric calibration purposes. In both cases, the solar diffuser (or its BRDF) 

should be fully characterized at the illumination and viewing geometries to be used on-orbit. 

There are more stringent calibration and characterization requirements if the solar diffuser is used 

as an absolute calibration device. The foremost is to assure its BFDF measurements are traceable 

to a national/international reflectance standard with calibration uncertainties meeting the design 

requirements. The second is to have on-orbit monitoring 

capability, such that any changes of the solar diffuser BRDF can 

be accurately determined and corrected when deriving and 

updating sensor on-orbit calibration parameters. Ideally, the sensor 

should have a full aperture view of the solar diffuser panel with 

the same optical path as used for its Earth view observations. 

Otherwise, the viewing geometry differences between the sensor’s 

Earth observations and solar diffuser calibration also need to be 

accurately characterized. 

Both MODIS and VIIRS use an on-board solar diffuser stability 

monitor (SDSM) to track the solar diffuser on-orbit degradation. 

The SDSM is a ratioing device that makes alternate measurements 

of the direct sunlight and the sunlight reflected from the solar 

diffuser on a regular basis. The solar diffuser degradation is 

determined from the ratios of the SDSM solar diffuser view 

response to its Sun view response. More details on the SDSM 

design function and its on-orbit calibration can be found from 

Xiong et al. (2008). An alternative approach for solar diffuser 

stability monitoring is to deploy a second diffuser to track the BRDF changes of the first 

(calibration) diffuser. For this purpose, the second diffuser is used much less frequently than the 

first one. For sensors that do not have an on-board solar diffuser monitoring device, frequent 

measurements over stable targets, such as well-characterized desert sites or the Moon, could be 

used to determine the solar diffuser degradation. 

A solar attenuator is often used in front of the solar diffuser because some of the spectral bands, 

depending on their applications and design specifications, could saturate when viewing the direct 

sunlight reflected from the solar diffuser. The attenuator is a mechanical device with fixed 

transmission or attenuation. A typical attenuator is made of a metal plate with small pinholes. It 

could be permanently fixed in front of the solar diffuser as used in VIIRS or deployable as operated 

in MODIS. As part of the solar calibration system, the transmission or vignetting function of the 

attenuator also needs to be fully characterized as a function of solar illumination angles. 

Although solar diffusers have been successfully used for sensor on-orbit calibration in the 

reflective solar spectral range, their absolute calibration uncertainties are limited by the pre-launch 

BRDF characterization uncertainties and on-orbit degradation characterization uncertainties. 

6.5 FREQUENCY OF ON-ORBIT CALIBRATION MEASUREMENTS 

On orbit calibration measurements typically cannot be performed simultaneously with mission 

measurements and must therefore be interwoven in the mission timeline. While most programs 

work to minimize the time taken away from the mission for calibration, they also recognized that 

Solar Diffuser 

This solar diffuser design was used 

to maintain calibration traceability 

for the MODIS instruments on both 

the NASA Terra and Aqua Earth 

Observing System (EOS) satellites, 

as well as the S-NPP/JPSS VIIRS 

reflective solar bands.

(Courtesy of NASA) 
86 

the data collected for the mission must meet program uncertainty requirements, and that without 

verification of the radiometric and goniometric performance of the sensor, the utility of the mission 

data may be compromised. 

The purpose of calibration is to provide the information needed to convert the raw sensor data into 

data products that meet the mission requirements. If the sensor can be placed into repeatable 

operating states, viewing a constant source will provide identical data (within the noise and 

statistics of photon detection) for each of those states. If there is no allowance for changes in the 

operating conditions (i.e., the program allows only one set of calibration coefficients) then the 

sensor must be maintained within a specific operational envelope, and uncertainties in the 

radiometric and goniometric data produced by the sensor will be determined by changes in sensor 

response as the operating state evolves. 

Since all variables that can influence the sensor response are not typically known during ground 

calibration or measured during the mission, there becomes a requirement for periodic on-orbit 

measurements of constant or reference sources whose statistical bounds are accepted as accurate 

for radiometric and goniometric performance verification. These reference sources may be either 

internal or external to the sensor. Internal radiometric reference sources have the advantage of 

always being available, and the calibration measurements using them can be scheduled for minimal 

interference with the mission. The internal reference sources should use the entire optical train of 

the sensor without precluding the sensor from returning to the primary mission, and should be 

monitored to track any drift in their output. Internal reference sources for goniometric performance 

are typically located outside of the primary sensor FOV used for operational measurements, and 

therefore are useful only as a measure of the effective focal length, but not for corrections to the 

optical distortion coefficients. 

External radiometric calibration sources such as specific locations on the Earth, the Moon, planets, 

asteroids, and stars have been used in multiple programs. In general, the advantages of external 

sources include the fact that the entire optical train is used, measurements within the operational 

FOV provide confirmation of, or updates to, the optical distortion coefficients, and the sources can 

be either modeled or simultaneously observed by reference sensors to verify their position and 

intensity. Their disadvantages include their availability, scheduling conflicts between the mission 

and calibration measurements, and the fact that multiple sources are required to probe the entire 

dynamic range of the sensor because they are at fixed radiance levels. In addition, because they 

may be measured in wavebands that are different from those of the operational sensor, there is a 

need to model their radiometric output with sufficient accuracy to meet the program requirements. 

The frequency of on-orbit calibration is thus dependent upon the knowledge of the operating 

conditions, the sensitivity to operating conditions of the sensor response, the relationship between 

the current performance of the instrument and the programmatic performance requirements, the 

availability of external calibration sources, and the availability and performance of the internal 

calibration sources. If the sensor performance has been shown to be stable within specific 

operational condition boundaries, and the on-board measurement of the operational state indicates 

that the sensor is within those boundaries, there is no requirement to perform additional calibration 

measurements. If, on the other hand, the sensor performance is not stable or the operational 

envelope boundaries have been exceeded, or the accuracy of the on-board monitoring systems for 

the sensor operating condition is not sufficient to determine the operating state, then an in-situ 

calibration measurement is required. 
87 

DATA ANALYSIS AND REPORTING

Analyzing calibration and sensor performance data requires experienced analysts and a variety of 

software tools that are frequently developed, or modified, from existing tools, specifically for the 

particular sensor program. 

7.1 DATA ANALYSIS SOFTWARE 

Basic data analysis software should be ready and tested prior to the start of 

calibration testing. A detailed understanding of the software requirements 

is needed prior to initiating any development activities. From a program 

perspective, this means that the calibration and test plans must be 

developed early in the program to allow sufficient time for any software 

development and testing to take place prior to beginning the sensor 

calibration. Development of suitable data analysis software may require an additional software 

development team for large programs, or could be accomplished by engineers and analysts already 

working on the calibration for small programs. In general, software development efforts tend to be 

more costly and take longer to complete than anticipated, and this should be accounted for in 

program budgets and schedules. 

The size and scope of the calibration effort are primary considerations when devising a software 

development approach. For small programs, if analysts are available to process specific data sets 

in their area of expertise, it may be unnecessary to develop calibration software with enough 

fidelity to be used by a wider group of analysts. Large programs, however, require many analysts, 

and it may be more important to provide a suite of software routines for the multiple users. 

Regardless of the size and scope of the calibration effort, the software tools must be sufficiently 

documented and robust to avoid misinterpretation of the results, and they should be under strict 

revision control so that analyses and results can be replicated after the test program is completed. 

7.2 DATA AUTHENTICATION 

Before the calibration data collection event is identified as complete, the data collection and data 

verification engineers are required to sign off on the results of data quality authentication. The 

calibration engineer will perform data quality authentication to verify data readability, instrument 

and GSE configuration, and instrument response to the calibration source. A unique data quality 

authentication check list should be created for each data collection procedure. This quicklook 

analysis is intended to demonstrate that the data obtained are adequate to generate the applicable 

calibration parameters. 

7.3 CALIBRATION REPORT 

The results of the calibration effort are usually documented in a detailed calibration report. The 

contents of the calibration report will be unique to each project, sensor design, and application, but 

Data analysis software 

should be prepared 

and tested during 

calibration planning. 
88 

common features exist. The report is often used for future reference to assist in finding answers to 

critical performance/technical and pragmatic questions, and should therefore be as thorough and 

complete as possible. 

A typical calibration report provides a brief description of the high-level mission and sensor 

objectives, and shows how these objectives drive the calibration requirements. An overview of the 

instrument is given to provide context to the calibration approach, data analysis, and results 

presented in the document. The report may also include discussions on on-orbit operations that 

impose limitations and define operating conditions, a typical ‘day in the life’ of the sensor, power 

and/or measurement cycles, thermal stability and range requirements, and data storage and 

downlink limitations. 

The overall calibration approach taken to characterize 

the instrument should be described, including 

potential impacts to the ground calibration results as 

the sensor becomes operational in the field or on 

orbit. A detailed presentation of the measurement 

equations and other parameters that describe the 

sensor function will help to illustrate the logic behind 

the calibration approach, and also forms a basis for 

the uncertainty analysis and budgeting. 

Calibration equipment and sources used in the testing 

should be identified, including specialized test 

hardware such as test chambers, along with the 

calibration sources that were used. The test 

configurations and algorithms for each calibration 

measurement/parameter should be identified to 

provide traceability, reference values, and 

uncertainty estimates for reference sources. The 

report should specify sensor settings, modes of 

operation, and environmental bounding conditions of 

the calibration measurements. 

The data management approach should be described, including relevant results of data quality 

assessment and formats for any electronic data products. In addition, the contamination control 

approach should be addressed, along with a discussion of events that affected contamination, and 

the results of contamination monitoring. 

The calibration report should give an overall picture of the calibration data collection by including 

information such as a detailed as-run schedule and copies of as-run data collection procedures. 

Data sets that were used for the purpose of verifying calibration should be identified, along with 

the corresponding analysis performed. 

A full review of the final data analysis should make up a large part of the calibration report. This 

should describe the data used, the algorithms and software processes involved, and any 

intermediate results used to produce the final results. The results should summarize the completion 

of specific calibrations for the different measurement combinations given in the comprehensive 

calibration plan (Section 4.2.2). Results for all of the calibration parameters should be reported, 

with an emphasis on those parameters that are considered high priority for the intended application. 

Common Features in a 

Calibration Report 

‒ High-level mission and sensor objectives 

‒ Overview of the instrument 

‒ Calibration approach and calibration 

requirements 

‒ As-run test schedule 

‒ Data collection procedures 

‒ Calibration equipment, test configurations, 

and environmental test conditions 

‒ Data management system 

‒ Calibration equations 

‒ Testing details 

‒ Data analysis and results 

‒ Uncertainty analyses 

‒ Summary of contamination control issues 

‒ Unique sensor findings 
89 

The numerical values reported for each parameter should be justified by a detailed description of 

the analysis used to produce each result. 

Sensor response dependences, such as FPA row and column sensor response dependencies, 

integration time and/or gain settings dependencies, should be reported, along with sensor 

environmental conditions, and background and dynamic range response as a function of sensor 

settings. Unique sensor features discovered and anomalies experienced during calibration should 

be described, along with reasons for their underlying causes and resolutions. Any specialized 

calibration algorithms used in quantifying calibration parameters (for example, point source 

extraction algorithm to quantify point source response) should be discussed. 

A calibration report is not complete without detailed analyses and documentation of uncertainties. 

The overall propagated measurement uncertainty should be addressed for specified measurement 

scenarios (Section 3.2), and confidence provided that all sources of uncertainty were considered. 

Calibration consistency and estimated uncertainties should be verified, and the results of crosschecks and paths to traceability (Section 3.1) should be documented. 

7.4 CROSS-CHECKS AND TRACEABILITY 

Cross-checks of the calibration effort can be performed by using multiple paths to calculate 

selected parameters. Obtaining equivalent results when using multiple paths validates the 

calibration and increases confidence that the data are accurate to within the calculated 

uncertainties. 

One method of cross-checking is to compare radiance and irradiance calibrations (Section 4.1.7), 

where peak irradiance responsivity (PIRR (W/cm2

)) is related to peak radiance responsivity (PRR 

(W/(cm2 sr)) through an independent calibration of a geometric parameter known as the effective 

field of view (EFOV (sr)) solid angle. This relationship is shown in the following equation: 

PRR EFOV

PIRR  

For this equation, it is assumed that EFOV is derived using data over the entire FOV, and includes 

the response due to NAS. If these three terms are measured independently using different traceable 

sources and test configurations, this cross-check verifies calibration consistency among these 

parameters. This cross-check is appealing because it can be performed during the analysis phase 

of the calibration campaign without having to make additional calibration measurements. 

A successful method of cross-checking the RSR of a sensor is to compare the estimated systemlevel RSR predicted from the sensor performance model, and based on individual component 

spectral dependences, to the system-level RSR obtained during calibration. This not only provides 

a better understanding of the sensor but also provides a cross-check that gives confidence in the 

RSR calibration. This is especially important for space-based sensors where RSR calibration 

cannot be characterized after they are deployed on orbit. 

Other examples of cross-checking include repeated measurements with multiple sources where 

each source is independent, sensor response comparisons between ground and on-board calibration 

sources, cross-calibration of on-orbit sensors (Tobin et al., 2006), and interlaboratory round-robin 

calibrations (typically at the component or subsystem level) (Wilthan and Hanssen 2011). 
90 

There may be additional opportunities to perform cross-checks that are unique to each sensor and 

application. An evaluation of potential cross-checks, along with schedule, cost, uncertainty, and 

risk should be addressed during the calibration planning trade-off studies. 

7.5 LONG-TERM REPOSITORY OF CALIBRATION DATA 

Pre-launch testing, performance characterization, and calibration of EO sensors generate large 

amounts of data under test conditions that generally approach but often do not precisely duplicate 

the on-orbit operational environments. Although absolute control of the test conditions is achieved 

during pre-launch calibration, the sources and sensor on-orbit operating state may not be exactly 

realized. However, the importance of these prelaunch data should not be underestimated. 

Pre-launch calibration data provide the basis for the sensor calibration and instrument sell-off, and 

produce a lasting resource that can allow the users of the instrument to compare the response during 

the mission to similar data collected during testing. These data provide the opportunity to 

potentially revise calibration coefficients during normal operations or to assist in anomaly 

resolution during the mission. 

To be useful, calibration data must be archived in such a way that they are available for the analyst 

at all times during the sensor lifetime. If possible, the same data analysis software that was 

developed for test and calibration should be used during the mission. 

Calibration data continues to be useful even after mission completion. The knowledge gained from 

analyzing the ensemble of data from all phases of the mission, together with well understood and 

documented lesson learned, can benefit the next generation of sensors. 
91 

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103 

COMMON EO SENSOR CALIBRATION REFERENCES 

Achieving Satellite Instrument Calibration for Climate Change (ASIC3), Report of Workshop at 

the National Conference Center, Lansdowne, VA, George Ohring (ed.), May 16-18, 

2006. 

Art of Radiometry, J.M. Palmer and B.G. Grant, SPIE Press Monograph Vol. PM184, 2009. 

Data Reduction and Error Analysis for the Physical Sciences, Bevington, P.R and D. K. 

Robinson, McGraw-Hill, 1992. 

Electro-Optical Imaging System Performance and Modeling, Lucien M. Biberman (ed.), SPIE 

Press Monograph, Vol. PM96, 2001. 

Electro-Optical System Design for Information Processing, Clair Wyatt, McGraw-Hill; 2nd 

edition, April 1991. 

Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results, Barry 

N. Taylor and Chris E. Kuyatt, NIST Technical Note 1297, 1994. 

Handbook of Optics, Vol. I, Fundamentals, Techniques, & Design, and Vol. 2, Devices, 

Measurements, & Properties, Second edition, McGraw-Hill Professional, 1994. 

JCGM 100:2008 Evaluation of measurement data - Guide to the expression of uncertainty in 

measurement (GUM), BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, OIML, 2008. 

MSX Bibliography, Version 3.3.2, Cleared for Public Release, SRE Log #0006-2601 

https://dcp.mda.mil/. 

Optical Radiometry, Edited by A. C. Parr, R.U. Datla, and J. L. Gardner, Elsevier Academic 

Press, 2005. 

Radiometric Calibration: Theory and Methods, Clair L. Wyatt, New York: Academic Press, 

1978. 

Recommended Practice; Symbols, Terms, Units and Uncertainty Analysis for Radiometric 

Sensor Calibration (NIST Handbook152) Clair L. Wyatt, Victor Privalsky, and Raju 

Datla, Washington, DC: US Government Printing Office, 1998 

Reliable Spectroradiometry, Henry J. Kostkowski, Spectroradiometry Consulting, 1997. 

SDL/09-576B, MSX data application for future MDA Overhead Persistent Infrared (OPIR) 

efforts, Final Report, Space Dynamics Laboratory, Logan, UT, April. 2010 

Self-study manual on optical radiation measurements: NBS 910-1 through 910-8, NIST, series of 

8 documents from 1977 to 1985, http://www.nist.gov/pml/div685/pub/studymanual.cfm 

Satellite Meteorology: An Introduction (International Geophysics), Stanley Q. Kidder, Academic 

Press, 1995. 

The Infrared Handbook, William L. Wolfe and George J. Zissis, General Dynamics, Revised 

Edition, 1985. 

The Infrared and Electro-Optical Systems Handbook [8 volume set], Joseph S. Accetta and 

David L Shumaker, ERIM, SPIE Press, 1993. 

Traceable Temperatures, An Introduction to Temperature Measurement and Calibration, J.V. 

Nicholas and D.R. White, Wiley, 2001. 

 
104 

ACRONYM LIST 

ABIR Airborne Infrared Sensor 

ACRIM Active Cavity Radiometer Irradiance Monitor 

AERI Atmospheric Emitted Radiance Interferometer 

AI&T Assembly, Integration, and Test 

ASIC3 Achieving Satellite Instrument Calibration for Climate Change 

BASS Broadband Array Spectrograph System (Aerospace) 

BIPM International Bureau of Weights and Measures (French: Bureau International 

des Poids et Measures) 

BRDF Bi-Directional Reflectance Distribution Function 

Cal/Val Calibration and Validation 

CCS Calibration Control System 

CEOS Committee on Earth Observing Satellites 

CERES Clouds and the Earth’s Radiant Energy System 

CIPM International Committee for Weights and Measures 

CLARREO Climate Absolute Radiance and Refractivity Observatory 

CMS Control and Monitor System 

COBE Cosmic Background Explorer 

COTS Commercial Off The Shelf 

CrIS Cross-Track Infrared Sounder 

CSO Closely Spaced Objects 

DITL Day In The Life 

DN Digital Number 

DO Dark Offset 

DoD Department of Defense 

DS Deep Space 

ECI Earth Centered Inertial 

EFOV Effective Field if View (Solid Angle) 

EMI Electromagnetic Interference 

EMC Electromagnetic Compatibility 

EO Electro-Optical 

EOD Energy On Detector 

ERF Edge Response Function 

ERB Earth Radiation Budget 

ESE Electronic Support Equipment 

ESR Electrical Substitution-Type Radiometers 

FIRST Far-Infrared Spectroscopy of the Troposphere 

FPA Focal Plane Array 

FOR Field Of Regard 

FOV Field Of View 

FTI Frontier Technology, Inc. 

FTS Fourier Transform Spectrometer 

GEO Group on Earth Observations 

GIFTS Geosynchronous Imaging Fourier Transform Spectrometer 

GOES Geostationary Operational Environmental Satellites 

GSE Ground Support Equipment 

GSICS Global Space-based Inter-Calibration System 
105 

GUM Guide to the Expression of Uncertainty in Measurement 

HIRS High Resolution Infrared Radiation Sounder 

IASI Infrared Atmospheric Sounding Interferometer 

ICT Internal Calibration Target 

ICU Internal Calibration Unit 

IEC International Electrotechnical Commission 

IFC In-Flight Calibration Source 

IFOV Instantaneous Field of View 

IR Infrared 

ISO International Organization for Standardization 

JCGM Joint Committee for Guides in Metrology 

JPL Jet Propulsion Laboratory 

JPSS Joint Polar Satellite System 

K Kelvin 

LASP Laboratory of Atmospheric and Space Physics, Univ. of Colorado, Boulder 

LBIR Low-Background Infrared Facility, NIST 

LEO Low Earth Orbit 

LOS Line of Sight 

LSF Line Spread Function 

LTT Long-Term Trending 

LWIR Long-Wave Infrared 

MKS Meter-Kilogram-Second 

MODIS Moderate Resolution Imaging Spectroradiometer 

MSX Midcourse Space Experiment 

MTF Modulation Transfer Function 

MTSAT Multifunctional Transport Satellite 

MWIR Mid-Wave Infrared 

NAS Near Angle Scatter 

NASA National Aeronautics and Space Administration 

NCC National Calibration Center at NOAA 

NDVI Normalized Difference Vegetation Index 

NEdN Noise Equivalent Radiance Difference 

NEdT Noise Equivalent Delta Temperature 

NEI Noise Equivalent Irradiance 

NEP Noise Equivalent Power 

NER Noise Equivalent Radiance 

NGC Northrop Grumman Corporation 

NIR Near Infrared 

NIST National Institute of Standards and Technology 

NMI National Metrology Institutes 

NOAA National Oceanic and Atmospheric Administration 

NUC Non-Uniformity Correction 

NVLAP National Voluntary Laboratory Accreditation Program 

OAR Off-Axis Response 

OBC On-Board Calibrator 

OIML International Organization of Legal Metrology 

OMI Ozone Monitoring Instrument 

OOB Out of Band 
106 

PICS Pseudo-Invariant Calibration Sites 

PIRR Peak Irradiance Responsivity 

PRF Point response function 

PRR Peak Radiance Responsivity 

PRT Platinum Resistance Thermometer 

PSF Point Spread Function 

QCL Quantum Cascade Laser 

RDR Raw Data Record 

RSR Relative Spectral Response 

ROLO Robotic Lunar Observatory 

RTC Radiative Transfer Code 

SABER Sounding of the Atmosphere using Broadband Emission Radiometry 

SDL Space Dynamics Laboratory 

SDR Sensor Data Records 

SDSM Solar Diffuser Stability Monitor 

SeaWiFS Sea-Viewing Wide Field-of-View Sensor 

SED Spectral Energy Distributions 

SI International System of Units (French - Système International)

SIRCUS NIST Spectral Irradiance and Radiance Responsivity Calibrations Using 

Uniform Sources 

SNO Simultaneous Nadir Overpass 

S-NPP Suomi-National Polar-orbiting Partnership 

SNR Signal-to-Noise Ratio 

SPIRIT (III) Spatial Infrared Imaging Telescope 

SSA Space Situational Awareness 

SUT Sensor Under Test 

SWIR Short-Wave Infrared 

TDI Time Delay and Integration 

THOR Thermal and Optical Research Chamber 

TI Technical Intelligence 

TIMED Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics 

TLYF Test Like You Fly 

TOA Top of Atmosphere 

TRR Test Readiness Review 

TSI Total Solar Irradiance 

TVAC Thermal Vacuum 

UAH University of Alabama in Huntsville 

USGS U.S. Geological Survey 

V&V Verification and Validation 

VIIRS Visible Infrared Imager Radiometer Suite 

VIM International Vocabulary of Metrology – Basic and General Concepts and 

Associated Terms 

WMO World Meteorological Organization 

WORM Write Only Read Many 

 
107 

GLOSSARY OF TERMS 

The terms included in this glossary are defined as they apply to EO sensors, radiometric 

calibration, and remote sensing. 

Absolute Calibration 

Absolute calibration is defined in terms of a sensor's response on a physical, real-world scale, 

measured in internationally defined and accepted units (SI units, for example). 

Alignment/Co-Alignment 

Alignment and co-alignment refer to the degree of co-location of one sensor or array FOV with 

another, or with any defined baseline angle. Misalignments can include both rotation and offset 

components relative to the baseline optical axis. 

Sensor Alignment Matrix 

The sensor alignment matrix is a unitary rotation matrix relating sensor coordinates to spacecraft 

coordinates. The sensor alignment matrix is unique for each sensor. 

Atmospheric Transmission 

Atmospheric transmission typically refers to the transmission of light as a function of 

wavelength along the line of sight. As light (photons) passes through the atmosphere, it interacts 

with the molecular species that make up the atmosphere (H20, CO2, O3, CO, CH4, N2O, etc.), 

with difference molecular species absorbing, scatterings and re-emitting different wavelengths of 

light. At some wavelengths, the atmosphere is very transmissive, while at other wavelengths it is 

very opaque. Atmospheric transmission is highly dependent on atmospheric concentrations, 

temperature, and pressure (all of which vary with altitude) along the LOS. Computer codes such 

as MODTRAN and HITRAN are very good at accurately deriving atmospheric transmission 

based on user input information. 

Background 

Background is a term that is used loosely in EO sensor terminology and can have any the 

following meanings: 

Sensor Background/Sensor Dark Offset (DO): A sensor’s DO is its response with zero 

input radiance. If an IR sensor has a warm optical system (or warm optical components), 

the sensor’s DO will contain self-emission from the optical system. 

Background Source/Dark Background: An ideal background source/dark background 

is a target or scene that reflects and emits zero radiance. In practice, a background source 

reflects and emits radiance that is below the sensor’s NER/NESR. For most IR sensors 

the background source must be both light tight and cold enough such that the total 

reflected and emitted radiance is below the NER/NESR or self-emission from the 

sensor’s optical system.

Background Clutter: Unwanted radiometric intensity and/or spatial structure in a target 

scene. 

Bakeout 

Hardware bakeout is part of the contamination control process. All flight and test hardware must 

meet cleanliness and material outgassing requirements prior to TVAC testing and flight. For 

hardware bakeout, components are placed into a TVAC chamber at specific temperature and 

pressure levels for a specified period of time to insure cleanliness prior to TVAC testing. 
108 

Blackbody 

Blackbody sources are used as radiometric standards in IR sensor calibration. An ideal 

blackbody source perfectly matches Planck’s emissivity law and has zero reflectance. In practice, 

a blackbody source approximates Planck’s emissivity law, has an accurately known surface 

temperature, is highly uniform spatially and has very low reflectance. All non-ideal performance 

characteristics must be quantified in the blackbody’s radiometric uncertainty. 

Calibration Coefficients 

See Calibration Equation / Calibration Coefficients 

Calibration Data Products 

Calibration data products are the numeric values that are produced during calibration and 

quantify an EO sensor’s performance. Calibration data products include calibration coefficients 

and other quantifiable calibration parameters such as IFOV, RSR, NER, etc. Calibration data 

products are typically delivered as computer data files and are documented in the calibration 

report. 

 Calibration Equation / Calibration Coefficients 

A calibration equation is a mathematic model of a sensor response, converting the sensor output 

response to SI radiance units. The calibration coefficients are the calibration equation parameter 

values that are quantified during calibration. 

Calibration Parameters 

Calibration parameters refer to information generated (or that will be generated) during the 

course of a calibration that quantifies and/or describes the EO sensor performance. 

Calibration Plan 

The calibration plan is a written document that lists top-level assumptions, parameters, 

measurement equipment and methods, and data collection and analysis methods that will be 

implemented to achieve calibration of a specific sensor. The data collection plan typically 

identifies a list of data collection procedures and refers to them for specific details for a given 

calibration measurement. 

Calibration Procedures 

A calibration procedure is a written document that lists a specific sequence of events that 

implement a given calibration measurement. The procedure includes logsheets and/or checklists 

for recording and documenting the measurement for reference during data analysis. 

See Data Collection Procedure 

Co-Alignment 

See Alignment/Co-Alignment 

Collimator 

A collimator is piece of optical test hardware that creates a simulated point source. 

Component-Level Testing 

Component-level testing refers to tests performed on the components or subassemblies of an EO 

sensor prior to being integrated into the EO sensor assembly. 

Contamination Control 

Contamination control is the process of keeping an EO sensor payload clean enough to 

accomplish its mission. Contamination control is addressed at the spacecraft and payload level, 
109 

with requirements flowing down to the EO sensor handling and environmental exposure during 

calibration testing. Contamination control must be included in the calibration plan or addressed 

in a separate contamination control plan. 

Counts 

See Data Number / Digital Number 

Coverage Factor (k) 

Coverage factor is the number of standard deviations included in the reported uncertainty. For a 

normal distribution, k = 1, k = 2, and k = 3 provide confidence levels of approximately 68 %, 95 

%, and 99.7 %. 

Cross-Calibration 

The process of deriving or updating the calibration parameters (typically radiometric 

responsivity) of the sensor being calibrated, by comparing the response of the sensor being 

calibrated to the known and trusted response of a calibrated sensor viewing the same earth scene 

at the same time.

Cryogenic 

Cryogenic refers to the very low temperatures required to test some EO sensors, especially focal 

plane arrays in the LWIR wavelengths. Cryogenic systems require high-vacuum thermal control 

equipment, and typically reach extremely low temperatures through cooling provided by 

liquefied noble gasses (helium, neon, argon, or xenon), liquefied or frozen nitrogen, and/or 

frozen hydrogen. Cryogenic temperatures can also be achieved with specialized mechanical 

cryocoolers. 

Dark Noise 

See Noise - Dark Noise 

Data Collection Procedure 

The data collection procedure, a subset of the overall test procedure, contains test objectives, 

configuration requirements, test entry criteria, configuration steps, data collection steps and other 

pertinent information specific to a given calibration test. The test conductor should be familiar 

with the test objectives, hardware and data collection procedure prior to starting the test. During 

testing, initials and signature blocks should be completed, and any exceptions, deviations and 

anomalies should be recorded in the data collection procedure, creating an as-run data collection 

procedure for archival. 

Data Management 

Data management refers to the task of collecting, quality checking, organizing, cataloging, 

archiving, processing and distributing all test data, including: sensor response and metadata, 

environmental data, source configuration information and reading values, and any other ancillary 

data. 

Data Numbers / Digital Numbers (DN) 

The response of most modern EO sensors is captured and stored in computer data formats that 

are generically referred to as Data Numbers (DN) or Digital Numbers (DN). EO sensor output is 

often referred to as ‘response counts’ or simply ‘counts'. 

Data Products 

See Calibration Data Products 
110 

Distortion / Optical Distortion 

Distortion occurs when image magnification in an EO sensor optical systems varies with position 

within the image, causing straight lines (generally conceptual rather than literal) in the object to 

be mapped into curved lines in the image. 

Distortion Map / Distortion Correction 

The distortion map provides the equations needed to manipulate digitized EO sensor images to 

correct for the optical distortion introduced in the sensor. The distortion map is unique to each 

EO sensor. Based on the sensor design, the distortion correction equation may take the form of 

Zernike polynomials, or it could also be a high order (typically 3rd or 5th order) polynomial. 

Some sensor configurations may require a more complex equation. 

Dynamic Range 

The dynamic range is the response range over which sensor values are valid. It is typically 

defined as the response range between dark noise and hard saturation. 

Earth's Limb 

Limb is defined as the outer edge of the apparent disk of a celestial body (Merriam-Webster). EO 

sensors can view the Earth limb from Earth orbit. It is typical when making Earth limb 

measurements to specify a ‘tangent height’ within Earth limb - defined as minimum distance 

between the sensor LOS and the hard Earth, which occurs at the tangent point. 

Earth’s Energy Balance / Earth’s Energy Budget 

Earth's energy balance/budget is the net difference between the total energy received by the Earth 

vs. the total lost, resulting in net global temperature change. This process is driven by radiative 

heat transfer, creating a quantitative measurement role for EO sensor technology. 

Earth-Observing Sensor (EOS) 

An EOS is sensor making quantitative measurements of the Earth or Earth's limb from Earth 

orbit. In the more restrictive sense, EOS may apply only to sensors involved in missions related 

to global climate change. 

Effective Bandpass 

Effective bandpass is a figure of merit, representing an EO sensor’s spectral bandwidth in units 

of wavelength, frequency or wavenumber. Effective bandpass is the bandwidth an ideal EO 

sensor would have given a spectral responsivity of 1 within the spectral bandpass and 0 outside. 

Effective Field of View (EFOV) 

EFOV solid angle is an optical sensor spatial figure of merit that represents the field-of-view 

solid angle in units of steradians. Given an EFOV, an ideal sensor would have a spatial 

responsivity of 1 within the solid angle and 0 outside. 

Emissivity 

Emissivity is the ratio of the energy radiated by an emissive surface relative to that of an ideal 

blackbody source at the same temperature. It is generally related as a function of wavelength or 

frequency, emissivity values range from 0 to 1. 

End-to-End Calibration 

End-to-end calibration refers to the calibration of a complete system, from input aperture to data 

products, to capture all possible effects that may be features of a sensor and data system. 
111 

Ensquared Energy / Energy on Detector (EOD) 

EOD is the fraction of the total energy in a point source that falls on a single pixel when centered 

on said pixel. 

Electro-Optical (EO) Sensor 

An EO sensor is a system designed to use optical and electronic phenomena to characterize a 

remote scene or source for information extraction purposes. 

Extended Source 

An extended source is a source of electromagnetic radiation that is not resolved to a single point 

or direction in space, and has a greater extent than the IFOV of the sensor involved. In the 

laboratory, an extended source is often implemented by a flat plate with a surface treatment to 

maximize emissivity and uniformity. 

Field of Regard (FOR) 

See Field of View 

Field of View (FOV) 

The FOV of a sensor is a numeric value representing the angular extent of an EO sensor’s 

response, generally expressed in radians, arc-degrees, or arc-minutes. 

IFOV: Individual FOV or instantaneous FOV; FOV of a single pixel in an EO sensor 

containing an imaging FPA.

FOR: Field-of-regard; The full range of FOV that may be seen by an instrument 

including the range of all arrays, zoom modes, and pointing mirrors in the instrument; 

also includes EO sensor pointing mechanisms such as gimbals and turrets. 

Filter 

In EO sensor parlance, filter refers to an optical filter(s) that is placed in the optical path of an 

EO sensor to select, restrict, reject, limit, or adjust an EO sensor response to specific 

wavelengths/frequencies of light. 

Bandpass: The range of desired wavelengths/frequencies to be passed by an optical filter. 

Generally defined by the cut-on and cut-off wavelengths/frequencies of the optical filter. 

In-Band Response: EO sensor response to the optical wavelengths/frequencies within the 

desired optical filter bandpass. 

OOB Blocking: The ability of an optical filter (or optical system) to reject optical energy 

outside the desired bandpass. May also refer to filter design specifications regarding the 

ability to reject optical energy outside the desired filter bandpass. 

OOB Leakage: Undesired optical energy that passes through an optical filter (or optical 

system) that has a spectral location outside the desired spectral bandpass. 

OOB Response: An EO sensor’s response to OOB leakage. 

Transmittance: Ratio of the open-path throughput of an optical path with and without the 

filter. Generally expressed as a function of wavelength or optical frequency, 

transmittance values range from 0 to 1, or 0 to 100 % if expressed in percent 

transmittance. 
112 

Focus 

The focus, also called an image point, is the point where light rays originating from a point on an 

object converge to a minimum. Although the focus is conceptually a point, physically the focus 

has a spatial extent, called the blur circle, which is caused by optical aberrations. In the absence 

of significant aberrations, the smallest possible blur circle is the Airy disc, which is caused by 

diffraction from the optical system's aperture. Aberrations tend to get worse as the aperture 

diameter increases, while the Airy circle is smallest for large apertures. 

Best Focus / Optimum Focus 

Verifying sensor focus is part of sensor calibration. Some sensors are equipped with 

mechanisms to adjust the focus while the instrument is in operation. For these sensor the 

optimum focus is found by adjusting the focus when the sensor is viewing a point source. 

For sensors not equipped with a focus adjustment, the calibration hardware presents a 

point source to the sensor and then changes the focus of the source with the calibration 

hardware allowing the optimum focus to be found. 

Focus Requirements 

For imaging sensor systems, a focus requirement is generally included in the sensor 

performance specifications, which can be specified in terms such as ensquared energy. 

The sensor is generally considered to be within focus when the ensquared energy is 

maximized and/or greater than a specified threshold requirement. 

Goniometric 

Goniometric measurements are those that involve angular positions or angular distances. In EO 

sensor calibration, goniometric parameters include: alignment, PRF, distortion, pointing 

accuracy, near angle scatter (NAS), and large angle scatter. 

Ground Calibration 

See Pre-Launch Calibration 

Ground Support Equipment (GSE) 

GSE is the collection of equipment used to operate a remote sensing instrument during ground 

testing, operations, and calibration. GSE is typically very sensor specific/custom, and includes: 

monitoring equipment/software, mechanical support hardware, electrical power, thermal control 

hardware/software, communications hardware/software, data management hardware/software, 

and so forth. 

Sensor GSE: Hardware and software that directly supports sensor operation either 

independently or as part of a larger ground calibration test campaign. 

Electrical GSE (ESE): Hardware and software that extends capabilities to support 

extensive pre-launch ground calibration and testing. ESE generally includes a robust data 

management system to accommodate large data volumes generated during ground 

testing. 

Internal Calibration Unit (ICU) 

ICU is a generic term for a radiance and/or spectral reference source that is contained within the 

EO sensor’s optical path. In some cases the ICU may not qualify as a true calibration source in 

that it does not have inherently known calibration properties. Rather, the ICU is highly 

repeatable and can be a link to a true calibration source during pre-launch calibration by 
113 

sequentially viewing the ICU and ground calibration source. ICUs may also be known by the 

synonyms internal stimulator (stim) sources or internal reference sources. 

IFOV 

See Field of View → IFOV 

In-Band Response 

See Filter → In-Band Response 

Integration Time / Integration Modes 

Integration time / integration modes refers to the length of time a detector signal is 

accumulated/integrated between resets. Multiple integration times are implemented in detectors 

to extend the response dynamic range. In snapshot read/charge transfer detectors, integration 

time is reset when the detectors are read. In up-the-ramp sampling, detectors are sampled 

multiple times between resets. A set of discrete integration times can also be known as 

integration modes. 

Integration Time Normalization / Integration Mode Normalization 

Integration time (mode) normalization is a calibration data product that is applied as part of the 

calibration equation (after EO sensor measurements are performed) to rescale data collected at 

multiple integration times to a common reference (generally relative to the responsivity of the 

shortest integration time). 

Inter-Satellite Calibration 

See Cross-calibration 

Irradiance / Spectral Irradiance 

Irradiance is an entity of flux that describes a point source or a source of a fixed size and distance 

such as the Sun when viewed from Earth. When irradiance includes wavelength dependence it is 

called spectral irradiance. Generalized units of spectral radiance are Watts/(cm2

 μm) or 

Photons/sec/(cm2

 μm). 

Jitter 

Jitter is unwanted mechanical vibrations in an EO sensor’s internal optical path or between the 

EO sensor and the calibration source/measurement target, or within the optical path of the 

calibration source (e.g., collimator), resulting in blurring of the energy at the EO sensor’s focal 

plane. Jitter can be especially problematic when performing point source measurements during 

pre-launch calibration. 

Jones’ Source 

Jones’ source refers to a near field small area source, sometimes referred to as a Jones’ method 

source (Wyatt 1978). It is a small area source that underfills the sensor entrance aperture, while 

filling the sensor field stop. 

Large-Angle Scatter / Off-Axis Scatter 

Large-angle or off-axis scatter is unwanted signal that originates from sources that are outside of 

a sensor’s direct FOV. 

See Off-Axis Rejection 

Linearity / Non-Linearity 

Linearity and non-linearity are terms used to describe the proportional relationship between an 

incremental change in target radiance and the incremental change in an EO sensor’s output 
114 

response, over the EO sensor’s dynamic range. In a perfect EO sensor system, an incremental 

percent change in the input radiance of a source should produce the same incremental percent 

change in the EO sensor output response, regardless of where in the sensor dynamic range the 

measurement occurs. Measuring an EO sensor’s linearity, creating a linearity correction function, 

and creating a linearity correction uncertainty estimate are key components of most EO sensor 

calibrations. 

Line-of-Sight (LOS) 

LOS is the direct optical path between an EO sensor and a measurement target. LOS may include 

atmospheric species and/or obscurations along the path. 

Long Term Repeatability 

See Repeatability – Long-Term 

Measurement Accuracy (See JCGM 200:2012, 2.13) 

Measurement accuracy is the closeness of agreement between a measured quantity value and a 

true quantity value of a measurand. The concept ‘measurement accuracy’ is not a quantity and is 

not given a numerical quantity value. A measurement is said to be more accurate when it offers a 

smaller measurement error. Measurement accuracy is also referred as ‘accuracy of measurement’ 

or ‘accuracy’. However in remote sensing literature ‘accuracy’ is often used to represent ‘bias’. 

Bias is an estimate of a systematic measurement error that is the component of measurement 

error in replicate measurements that remains constant or varies in a predictable manner. 

Measurement Requirements 

Measurement requirements are the set of specifications that define the performance to be 

achieved by a remote sensing instrument. Measurement requirements must be defined in 

consultation and collaboration with program management, mission scientists, and instrument 

engineers to reconcile program and science needs and desires with technological realities, and to 

ensure that all parties understand the resulting measurement requirements in the same way. 

Measurement Uncertainty (See JCGM 200:2012, 2.26) 

Measurement uncertainty is the dispersion of the values being attributed to a quantifiable 

physical property; measurement uncertainty defines an interval that is likely to enclose the true 

value of that quantity. Measurement uncertainty contains components arising from both the 

systematic and the random effects. The more limited the accuracy and precision of a 

measurement instrument, the larger the measurement uncertainty. Measurement uncertainty is 

also referred to as uncertainty of measurement or, in short, uncertainty. 

Modulation Transfer Function (MTF) 

MTF is a measure of an EO sensor’s spatial resolving capability, which is represented by the 

normalized Fourier transform of the PSF. MTF is a combined representation of the spatial 

resolution and image quality of an imaging system, represented in terms of the normalized 

spatial frequency response. 

Near Angle Scatter (NAS) 

NAS (or small-angle scatter) is unwanted signal that originates from sources that are inside the 

FOR and more generally in the local neighborhood. NAS typically originates from filters, 

beamsplitters, and baffle edges located near or within the optical path of the sensor. 
115 

Noise 

Noise is unwanted variability in electronic signals and/or digitally sampled measurement values. 

In most measurement scenarios, measurement noise limitations challenge measurement 

objectives and are a major contributor to overall measurement uncertainty. 

Noise – Dark Noise 

Dark noise is the EO sensor noise observed when the sensor has no measureable input 

radiance. Dark noise can be expressed in counts or in terms of either noise equivalent 

radiance or irradiance. Noise equivalent radiance (NER) of a sensor is its dark noise 

expressed in units of radiance. Noise equivalent irradiance (NEI) of a sensor is its dark 

noise expressed in units of irradiance. Noise equivalent spectral radiance and noise 

equivalent spectral irradiance are referred to as NESR and NESI, respectively. 

Noise – Photon Noise 

Photon noise is caused by the random arrival of photons and signal conversion at the 

detector. It is generally considered as a Poisson statistical distribution, which means that 

the signal variance is proportional to the mean value. 

Noise – Noise Equivalent Delta Temperature (NEdT) 

NEdT is the target-to-background temperature difference that produces a signal-to-noise 

ratio of unity. 

Noise – Noise Equivalent Irradiance (NEI) 

NEI is the flux density at the entrance pupil of the optical system that produces an output 

signal equal to the system’s noise. It is used to characterize the response of a system to a 

point source target. 

Noise – Noise Equivalent Radiance (NER) 

NER is the entity of radiance that is most appropriate for the description of radiant flux 

from an extended area source. The NER is the amount of radiant flux that produces a 

signal equal to the system’s noise when viewing an extended source. 

Non-Linearity 

See Linearity 

Non-Uniformity Correction (NUC) 

NUC quantifies each pixel’s responsivity relative to the array mean (or median) responsivity 

when illuminated by a uniform source. NUC is typically computed for each FPA configuration 

(integration time, bias voltage, gain, etc.) combination. 

Off-Axis Rejection 

Off-axis rejection is the ability of a telescope to reject signal that originates from sources outside 

of the telescope's direct FOV, such as signal from the surface of the Earth when viewing the 

Earth's limb. 

Operational Envelope 

The operational envelope is the range of values for operational parameters, such as temperature, 

solar illumination, or radiation exposure, over which a remote sensing instrument is expected to 

provide results consistent with the overall calibration uncertainty. 
116 

Operational Environment 

The operational environment describes the conditions under which a remote sensing instrument 

is expected to function. These conditions must be simulated during ground testing and 

calibration. For example, an instrument to be operated in orbit must be operated in a thermalvacuum space simulation chamber for ground testing to identify and characterize all behaviors 

that may be expected to arise during operation. 

Out-of-Band (OOB) Blocking 

See Filter → OOB Blocking 

Out-of-Band Leakage 

See Filter → OOB Leakage 

Out-of-Band Response 

See Filter → OOB Response 

Optical Density 

Optical density is the log (base 10) of the attenuation of a filter, typically used when referring to 

neutral density or spectrally flat filters used to attenuate a beam. For example, a neutral density 

filter of 1 % transmission has an optical density of 2. 

On-Orbit Calibration 

See Post-Launch Calibration 

Peak Irradiance Responsivity (PIRR) 

PIRR is a calibration data product that converts corrected EO sensor response values (in 

corrected response units, such as counts) into irradiance units, such as W/cm2

 or Jansky’s. PIR is 

dependent on sensor response corrections and produces a final result that is accurate for a 

monochromatic source located at the RSR peak wavelength. 

Peak Radiance Responsivity (PRR) 

PRR is a calibration data product that converts corrected EO sensor response values (in corrected 

response units, such as counts) into radiance units, such as W/(cm2·sr). PRR is dependent on 

sensor response corrections and produces a final result that is accurate for a monochromatic 

source located at the RSR peak wavelength. 

Photon Noise 

See Noise – Photon Noise 

Planck’s Equation 

The equation describing spectral radiance emitted by an ideal blackbody. In fundamental units 

the equation is: 

2

5 2

2 1

1 hc kT

hc Watts

e m sr m

 

      

where h is Planck's constant, c is the speed of light, k is Boltzmanns constant, T is 

temperature in kelvin, and  is wavelength in meters. The units have been written to emphasize 

that the result is a flux density (per area and solid angle) per wavelength. 
117 

Point Response Function (PRF) 

The PRF describes the sensor’s normalized response to a point source. The shape of the PRF is 

determined from optical diffraction, geometric image quality, detector spatial response, optical 

scatter, detector-to-detector electrical crosstalk, and jitter. The PRF is the PSF of the sensor as it 

is sampled by the FPA. 

Point Spread Function (PSF) 

The PSF is the two dimensional profile of the flux density produced by a point source on the 

image plane of a remote sensing instrument. The shape of the PSF is determined from optical 

diffraction, geometric image quality, optical scatter, and jitter. In the case of diffraction-limited 

performance, the PSF is the cross section of the airy disk or optical blur. 

Point Source 

A point source is a source of electromagnetic radiation that is resolved in the ideal case to a 

single point or direction in space. For example, a natural star is an ideal point source. In the 

laboratory on the ground, a point source is simulated using an optical collimator. 

Pointing and Goniometric Parameters 

Pointing and goniometric parameters are coefficients and characterizations that describe the 

relationship between the position of an object observed by a remote sensing instrument and the 

true position in object space. They include parameters that describe telescope distortion and 

distortion correction, conversion of focal-plane position to object-space angle, IFOV, and optical 

image quality. 

Polarimeter 

A polarimeter is a remote sensing instrument designed to have the capability to measure and 

characterize polarization. 

Polarization 

Polarization is the degree of alignment of the electric field in a beam of electromagnetic 

radiation. Polarization is described in terms of two polarization components rotated 90 degrees 

from each other (horizontal and vertical polarization components). In general, an electromagnetic 

beam is a combination of the following three possibilities: 

Unpolarized: A beam of electromagnetic radiation in which the electric field vector 

changes constantly and randomly. 

Linear Polarization: A beam of electromagnetic radiation in which the horizontal and 

vertical polarization components are fixed and in phase, resulting in an electric field that 

oscillates in one angle. 

Circular Polarization: A beam of electromagnetic radiation in which the horizontal and 

vertical polarization components are fixed but shifted in phase, resulting in a rotating 

electric field angle. 

See Responsivity Domains 

Polarization Sensitivity 

Polarization sensitivity is the degree to which a remote sensing instrument is sensitive to 

radiation of one polarization state over another. 
118 

Post-Launch Calibration 

A general term that encapsulates any and all calibration activities that occur after a satellitebased EO sensor is on-orbit (thus post-launch calibration may also be referred to as on-orbit 

calibration). The scope of the post-launch calibration varies from program-to-program and 

sensor-to-sensor, and includes considerations such as mission objectives, measurement 

requirements, mission operations capabilities, sensor data collection capabilities, and the ability 

to downlink low-level sensor response data to the ground. Post-launch calibration activities are 

included in the calibration plan and are executed according to the post-launch calibration 

procedures.

Pre-Launch Calibration 

Pre-launch calibration (or ground calibration) is the sequence of measurement and 

characterization that takes place during and after instrument assembly and integration, prior to 

launch. Pre-launch calibration provides the best or only chance to measure some parameters such 

as spectral response, linearity, and polarization sensitivity, and also provides an important quality 

control and validation function to prevent unpleasant surprises and disappointment after launch. 

Quicklook Analysis 

A quicklook analysis is a preliminary look at the data and is used to verify dataset completeness 

and integrity before moving on from a data collection task. 

Radiance / Spectral Radiance 

Radiance is an entity of flux that describes an extended source. When radiance includes 

wavelength dependence it is called spectral radiance. Generalized units of spectral radiance are 

watts/(cm2

sr μm) or photons/(sec cm2sr μm). 

Radiometer 

A radiometer is a remote sensing instrument designed to measure the total quantity of radiation 

in a limited bandwidth. A radiometer may be more specifically described by the wavelength 

region in which it operates, such as infrared radiometer or microwave radiometer. 

Broadband Radiometer: A radiometer that integrates radiation across a wide 

wavelength range, as opposed to a spectrometer, which measures radiation at specific 

wavelengths or within very narrow wavelength bands. 

Radiometric Calibration 

Radiometric calibration is the process of deriving coefficients, identifying and describing 

behaviors, and characterizing all aspects of a remote sensing instrument to relate the response of 

the sensor to a known quantity of flux entering the system. A system that has undergone this 

process can then infer the value of an unknown quantity of flux based on the response of the 

instrument. 

Radiometric Model 

A radiometric model is a mathematical model of response of a sensor to observed scenes and 

sources, typically implemented on a computer, to facilitate rapid calculation of multiple 

parameter cases. A detailed radiometric model is essential to successful calibration planning to 

validate calibration source configurations and equipment designs. 

See Sensor Performance Model 
119 

Real-Time 

Real-time refers to providing immediate feedback, without delay for observation, processing, or 

analysis. A monitor of sensor output that shows instantaneous changes in response as sources are 

adjusted during test setup is often called a real-time display. 

Relative Spectral Responsivity (RSR) 

A radiometer’s RSR is its peak-normalized response to radiation at different wavelengths both 

within (in-band) and outside (out-of-band) its spectral bandpass. The RSR is used to calculate the 

effective flux for absolute responsivity calibrations, and to interpret flight data. 

Remote Sensing 

Remote sensing is the technique of measuring the reflected or emitted radiation from an object to 

infer characteristics of the object that can not be measured directly. Remote sensing 

measurements are performed passively. 

Repeatability 

Repeatability refers to the stability of the response of a remote sensing instrument over time. 

Repeatability is defined over short, medium, and long time intervals as follows: 

Short-Term: Repeatability or stability of a measurement between adjacent samples or 

within a single integrated measurement interval. Short-term repeatability is quantified 

from measurement noise with a timescale of typically seconds to minutes. 

See Noise 

Medium-Term: Repeatability or stability of response from a stable input between 

consecutive or succeeding integrated measurement intervals. Medium-term repeatability 

is typically quantified via benchmark tests that are included as part of a measurement 

sequence. Medium-term repeatability sources may include on-board stimulator sources, 

vicarious ground sources, and stellar references. The medium-term repeatability 

timescale is typically minutes to hours. 

Long-Term: Repeatability or stability between widely separated measurement intervals. 

Long-term repeatability is typically quantified via benchmark tests that periodically 

measure constant radiometric source(s) over the life of the sensor. Long-term 

repeatability sources may include on-board stimulator sources, vicarious ground sources, 

and stellar references. The long-term repeatability timescale is typically hours to days, up 

to the lifetime of the sensor. 

Responsivity Domains 

Remote sensing systems provide information about an object by recording the response to 

radiation coming from the object in one or more of five responsivity domains: 

Radiometric: Response to electro-magnetic radiant energy as a function of quantity 

(relative and/or absolute) 

Spatial: Energy distribution as a function of position 

Spectral: Energy distribution as a function of wavelength or frequency 

Temporal: Variation of energy distribution as a function of time 

Polarization: Orientation of electric field vector 
120 

Calibration measurements seek to limit interaction between responsivity domains by varying 

parameters in one domain while holding parameters pertaining to other domains constant. 

Saturation 

Saturation is the state or behavior of a remote sensing instrument when the upper limit of 

dynamic range is met or exceeded. Typically, the charge storage or transfer capacity of 

individual pixels is exceeded, leading to anomalous effects in other pixels. Saturation effects 

begin to appear as the limit is approached and met and become more severe beyond the limit. 

Soft Saturation: Saturation effects that begin to appear as the limit of dynamic range is 

approached and may be amenable to correction, resulting in useful data. 

Hard Saturation: Saturation effects that occur when the limit of dynamic range is 

exceeded and that cannot be corrected to produce useful data. 

Scanner / Scanning Sensor 

A scanner or scanning sensor uses a moving FOV or viewing angle to achieve greater FOR, as 

opposed to a sensor that maintains a static viewing angle. 

See Starer/Staring Sensor 

Push-Broom: A scanning sensor in which the FOV is moved parallel to the direction of 

motion of the sensor. 

Wisk-Broom: A scanning sensor in which the FOV is moved perpendicular to the 

direction of motion of the sensor. 

Sensor Performance Model 

The sensor performance model is a mathematical model of a sensor's response, including noise 

and background sources, that is typically implemented on a computer to facilitate rapid 

calculation of multiple parameter cases. A detailed sensor performance model is essential to 

successful calibration planning to validate calibration source configurations and equipment 

designs. 

See Radiometric Model 

SI (International System of Units) 

The International System of Units (French - Système International) is the system of scientific 

units used for scientific, acceptable radiometric calibration. 

Spectral Resolution 

Spectral resolution is a measure of a sensor’s power to resolve features in the electromagnetic 

spectrum. It is defined by the equation R = λ/Δλ where Δλ is the smallest difference in 

wavelengths that can be distinguished at a wavelength of λ. 

Spectrometer 

A spectrometer is an instrument that makes measurements in multiple narrow bands or discrete 

wavelengths (spectral bins) to trace out the shape of an emission or transmission curve. 

Multi-Spectral: Measurements containing multiple spectral bins or bandpasses. 

Hyper-spectral: Oversampled spectrally or containing many spectral bins. 
121 

Starer / Staring Sensor 

A starer or staring sensor is a sensor that maintains a static FOV or viewing angle, as opposed to 

one that moves the FOV by means of a scan mirror, for example. 

See Scanner/Scanning Sensor 

Steradian 

A steradian is a unit of solid angle, or an angle in two dimension, that describes the volume 

enclosed by the angle subtended in each dimension. 

Stierwalt Effect 

The Stierwalt effect refers to out-of-band leakage through a filter of light scattered at high 

oblique angles by defects in the filter coating layers. When placed immediately in front of a 

detector, a filter showing the Stierwalt effect will show higher out-of-band transmission than 

would be expected from standard transmittance measurements where the detector is far from the 

filter. 

Strawman Calibration Planning 

Strawman calibration planning is an iterative method for obtaining consensus on a calibration 

approach among project leadership, scientists, and engineers. The goal is to produce an optimum 

calibration while minimizing cost, schedule, and uncertainty. The strawman plan ideally 

addresses the entire duration of the sensor lifetime, establishes the initial plan for calibration 

measurement methods, provides a first draft of measurement combinations, and makes budgeted 

estimates of uncertainty. 

System-Level Calibration 

See End-to-End Calibration 

Technical Intelligence (TI) 

TI is a broad term for the end product of a remote sensing instrument designed for military use 

such as surveillance and reconnaissance. 

Temporal Frequency Responsivity 

Temporal frequency responsivity describes a sensor's response change with respect to changes in 

input signal as a function of time. Through Fourier analysis, temporal frequency responsivity is 

typically reported in terms of frequency. The temporal frequency responsivity of a sensor 

characterizes its response to time-varying signals. 

Test Conductor 

The test conductor executes specific calibration test procedures to accomplish specific 

calibration tests. Test conductor responsibilities may be distributed between more than one test 

conductor to cover all calibration tests, depending on the scope of the calibration. The test 

conductor's role is generally filled by a calibration engineer. 

Traceability 

Traceability is the property of a measured value whereby it can be related through an unbroken 

chain of calibrations, with quantitative and documented uncertainties to an original standard of 

measurement (such as SI). 

Transmittance 

See Filter → Transmittance 
122 

TVAC / TVAC Chamber 

TVAC testing simulates the space environment or other conditions that may be of interest during 

test and calibration of an electro-optical instrument. This testing is performed in a TVAC 

chamber, which is an enclosed space from which the atmosphere can be removed and the 

temperature controlled. 

Uncertainty 

See Measurement Uncertainty 

Uncertainty Budget 

An uncertainty budget is a list of uncertainty components that are put together to produce the 

combined uncertainty of a given measured value. During calibration planning, terms in the 

uncertainty budget are estimated to predict overall calibration performance and identify areas 

where additional work may be needed to meet test requirements. 

Uniformity – Irradiance Uniformity Over FOV 

The irradiance uniformity correction corrects for the effects of design and residual aberrations in 

the telescope that can alter the shape of the PRF and the ensquared energy (fraction of point 

source irradiance falling on a single pixel) over the FOR. These variations, along with 

transmittance variations for different field angles, affect the extracted point source amplitude. 

Validation 

Validation is the process of confirming that the specifications and requirements set out in the 

design of an operation were sufficient to meet the objectives of the operation. 

Verification 

Verification is the process of confirming that an operation, such as sensor design and assembly 

or calibration data collection, has met the specifications and requirements set out in the design of 

the operation. 

Vicarious Calibration 

Vicarious calibration is achieved by comparison or transfer of calibration between different 

systems viewing the same scene. For example, on-orbit vicarious calibrations are often carried 

out by using SNO events to observe an Earth scene from two satellites at (nearly) the same time 

under identical illumination conditions. 

 
123 

BIOGRAPHICAL SKETCHES OF AUTHORS 

Daniel Bancroft (Frontier Technology, Inc.) 

Director of Sensor and Data Services 

Dr. Bancroft has demonstrated significant leadership capabilities in several different scientifically 

related positions. His leadership and technical strategy were largely responsible for the successful 

installation of the ELAPSTM data management software in July 2014 in support of MDA/SS. He 

was also an acknowledged leader in the Midcourse Space Experiment (MSX) Data Management 

system and he played a significant role in assuring the success of the MSX Program. He has also 

contributed to the STSS, RR-AIIRS, VIIRS, Space-Based Infrared System (SBIRS) High and 

MDA Russian American Observation Satellites (RAMOS), Ballistic Missile Defense Organization 

(BMDO) Virtual Data Center, and USAF Warfighter Programs. Dr. Bancroft has extensive 

experience with the design and development of data systems, data system test programs, data 

tracking, data system modeling, and analysis software design. He has successfully developed, 

administered, and executed several research projects during his tenure at FTI. 

Dr. Bancroft’s current responsibility is to support the Northrop Grumman effort on the STSS 

Program. He assists the on-site FTI test team during testing of sensors and payloads in addition to 

fulfilling data management activities. He performs test data analysis with an emphasis on anomaly 

resolution studies and routinely briefs NGAS and their customers on his analysis results. For STSS, 

he designed the test data analysis archive system, developed a models for tracking test data and 

test data analysis progress, developed a sensor telemetry database and associated trending tools, 

and designed an automated data processing pipeline. He is managing the development of an 

advanced set of radiometric data analysis software tools being developed in C++. 

Jim Butler (NASA Goddard Space Flight Center) 

JPSS Deputy Senior Project Scientist; JPSS Flight Project Scientist; NPP Deputy Project 

Scientist; EOS Calibration Scientist; NASA GSFC Radiometric and Diffuser Calibration 

Laboratory Head, Biospheric Sciences Laboratory, NASA GSFC 

Dr. Butler serves as NASA’s project scientist on a number of Earth observing missions. These 

positions include Deputy Senior Project Scientist and Flight Project Scientist for the Joint Polar 

Satellite System (JPSS) since 2011, Deputy Project Scientist for the Suomi National Polar-orbiting 

Partnership (SNPP) since 2004, and Calibration Scientist for the Earth Observing System since 

1995. In these roles, Dr. Butler oversees the calibration and characterization of a large number of 

remote sensing instruments operating from the ultraviolet to the microwave on multiple spacecraft. 

Dr. Butler also is the Principle Investigator for NASA Goddard’s Radiometric Calibration 

Laboratory (RCL), Diffuser Calibration Laboratory (DCL), and Instrument Development and 

Testing Laboratory (IDTL). Since 1984 and 1992, respectively, the RCL and DCL have provided 

radiometric, spectral, spatial, and bidirectional optical scatter measurements to Earth satellite, 

space satellite, and airborne and ground-based validation projects. 

In addition to his project science and laboratory work at NASA, Dr. Butler serves as NASA’s 

representative to the Executive Panel of the World Meteorological Organization (WMO) Global 

Space-based InterCalibration System (GSICS), chair of the SPIE Conference on Earth Observing 

Systems, and program committee member for the Utah State University CALCON conference, the 

SPIE Asia Pacific Remote Sensing Conference on Earth Observing Missions and Sensors, and the 

Bureau International des Poids et Mesures (BIPM) NewRad conference. 
124 

Changyong Cao (NOAA/NESDIS/Center for Satellite Applications and Research) 

Research Physical Scientist, VIIRS SDR Team Lead, and GOES-R Calibration Working 

Group Co-Chair 

Dr. Cao specializes in the calibration of radiometers onboard NOAA's operational environmental 

satellites. In addition to the operational pre-launch and post-launch calibration support, he is 

responsible for developing and refining the methodology for inter-satellite calibration using the 

Simultaneous Nadir Overpass (SNO) method, which has been used for the long-term on-orbit 

instrument performance monitoring of all radiometers on NOAA's polar orbiting satellites, and is 

being used by scientists for quantifying inter-satellite calibration biases in developing long-term 

time series for climate change detection studies. His primary research interest is climate quality 

calibration for Earth observing satellites. 

Dr. Cao has supported the calibration/validation of several infrared sounders and visible and 

infrared imagers including the High Resolution Infrared Sounder (HIRS) and Advanced Very High 

Resolution Imager (AVHRR) on NOAA series of satellites. He has also performed studies using 

hyperspectral observations such as IASI and Hyperion for inter-satellite calibration. His current 

primary responsibility is on the pre-launch and post-launch calibration/validation of the Visible 

Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPP and JPSS J1. He leads the VIIRS SDR 

team, which consists of members from several organizations. From 2007-2008 he also chaired the 

CEOS/WGCV (Committee on Earth Observation Satellites/Working Group on Cal/Val), an 

international committee for space agencies. Dr. Cao has many peer reviewed publications 

(http://www.researcherid.com/rid/F-5578-2010) and serves as peer review for several journals. 

Raju Datla (RD and Consultants, Inc.) 

President 

Dr. Datla worked as a physicist at NIST for 37 years in a variety of laboratory research settings, 

retiring in 2011. Since that time, he has been a guest researcher at NIST, and has worked as an 

affiliate to NOAA/NESDIS/STAR for the GOES-R calibration task. Dr. Datla's work at NIST 

included 16 years in plasma spectroscopy diagnosing high-temperature plasmas for fusion 

research, and 21 years developing infrared technology and standards for infrared remote-sensing 

applications. He has built and generated plasma sources that produce radiation from X-ray to 

infrared wavelengths and measured radiative properties of plasmas using X-ray, XUV, UV, visible 

and infrared spectroscopic techniques, and laser scattering. Most recently, he led a team of 

physicists, engineers, and technicians in the development of the Low-Background Infrared (LBIR) 

calibration facility at NIST. This facility simulates the thermal infrared background of space and 

houses the NIST absolute cryogenic radiometer (ACR), which is used to absolutely calibrate the 

radiant output of customer cryogenic blackbodies for use as standards to calibrate space bound 

sensors. 

In addition, Dr. Datla has been a proponent for close collaboration of NIST, NOAA, NASA, 

USGS, aerospace contractors, and academic institutions to achieve improved accuracy of optical 

remote sensing data for climate. He was a chair of the Organizing Committee of the Workshop: 

“Satellite Instrument Calibration for Measuring Global Climate Change,” at the University of 

Maryland, College Park, MD, 2002. He was a member of the organizing committees for the annual 

meeting for the Calibration Conference for Remote Sensing (CALCON), Logan, Utah and for the 

workshop “Achieving Satellite Instrument Calibration for Climate Change (ASIC3),” Lansdowne, 

VA, 2006, and organized a workshop at NIST on Bridging Climate Data Gaps in 2009. 
125 

Scott M. Hansen (Space Dynamics Laboratory) 

Calibration/Electrical Engineer 

Mr. Hansen has over two decades of experience in radiometric calibration test plan preparation 

and implementation, test data analysis and reporting, radiometric modeling, and optical test 

equipment preparation, operation, and troubleshooting. Mr. Hansen served as lead calibration 

engineer on the PTSS program, which included conceptual design of PTSS calibration plans and 

ground calibration support equipment. He also developed and continues to maintain a detailed 

PTSS sensor radiometric model that is used to predict sensor performance for trade-off studies and 

also to plan and understand calibration data collection procedures and test results. 

Previously, Mr. Hansen served as lead engineer for critical spectral transmittance and spatial 

uniformity testing of SBIRS-High flight filters at cryogenic temperatures. Mr. Hansen also 

participated in calibration of the WISE and MKV sensors, taking the lead in collection and 

processing relative spectral response data. He served as lead calibration engineer for the Microsat 

program and for RAMOS ground calibration. Mr. Hansen has also been involved in calibration of 

the NFIRE instrument, the ASTIF transfer radiometer, SBIRS-High, DXPS, SABER, WIRE, and 

SPIRIT III programs. 

Dennis Helder (South Dakota State University) 

Associate Dean for Research/Distinguished Professor, College of Engineering, 

Dr. Helder areas of research include radiometric, geometric, and spatial calibration of satellite and 

airborne optical sensors. He has been involved with radiometric calibration of the Landsat series 

of instruments since 1988. He founded the SDSU Image Processing Laboratory in 1991 and is the 

current director. He has been involved with the Landsat 7 Science Team, the EO-1 Science 

Validation Team, and is currently a member of the Landsat Data Continuity Mission Science 

Team. 

Raghu N. Kacker (National Institute of Standards and Technology) 

Senior Researcher 

Dr. Kacker has contributed to design and evaluation of industrial experiments, quality engineering, 

and evaluation of measurement uncertainty in physical measurements and in outputs of 

computational models. His current interests include development and use of combinatorial 

mathematics methods for testing software systems. He has co-authored over 120 refereed 

publications and one book. He has a Ph.D. in statistics (Iowa State University), and is a Fellow of 

both the American Statistical Association and the American Society for Quality. He has also 

worked in industry (AT&T Bell Telephone Laboratories) and academia (Virginia Tech, 

Blacksburg, VA). 

Harri Latvakoski (Space Dynamics Laboratory) 

Calibration and Systems Engineer 

Dr. Latvakoski is currently the principle investigator and program manager on the recalibration 

and deployment of the FIRST instrument, a 10 to 100 μm Fourier transform spectrometer that 

observes the atmosphere from the ground or a high-altitude balloon. He is responsible for 

performing the calibration, operating the instrument in the field, and customer communications. 
126 

Dr. Latvakoski has worked on the calibration of many sensors at SDL, including MTS-C, WISE, 

and FIRST. His calibration roles included developing calibration plans and tests, overseeing and 

performing testing, and processing and analyzing data. He also provided support and oversight to 

the JPSS project in the development of an improved radiometric model for the CrIS instrument. 

For the CLARREO mission, Dr. Latvakoski worked as PI on developing a blackbody that can meet 

the strict uncertainty requirement of 0.1 K (k = 3); he was responsible for modeling the blackbody 

physics, performing relevant component testing, and designing a blackbody to meet the 

requirements. 

Dr. Latvakoski’s experience includes extensive research and development of infrared optical 

systems for use in a wide variety of applications. He has designed infrared instruments using 

single element detectors, detectors arrays, optical filters, Fabry-Perot interferometers, and FT-IR 

spectrometers, fiber optics, and polarizing optics as needed. He has also developed instruments 

for environmental research, astronomical research, polarimetery, turbine engine combustion 

diagnostics, and measurement of film properties during semiconductor growth. 

Martin Mlynczak (NASA Langley Research Center) 

Senior Research Scientist 

Dr. Mlynczak conducts fundamental research into the energy balance and climate of the 

atmosphere from the Earth’s surface to the edge of Space. He leads the development of innovative 

techniques (instruments, technology, models, and algorithms) used to remotely sense the 

atmosphere from satellites, suborbital rockets, aircraft, high altitude balloons, and the ground. He 

also analyzes existing datasets and develops new techniques to interpret atmospheric data. The 

range of topics presently under study spans non-LTE radiative processes and solar-terrestrial 

coupling in the thermosphere to climate feedback processes due to tropospheric water vapor and 

cirrus clouds. 

Dr. Mlynczak has led or co-led a multitude of satellite and suborbital projects, technology 

development projects, and theoretical/modeling efforts. He is presently the Associate Principal 

Investigator of the SABER instrument on the TIMED satellite; the Principal Investigator of the 

FIRST Fourier Transform Spectrometer instrument; the Deputy Project Scientist of the 

CLARREO mission; and the Principal Investigator of basic research funded under the NASA 

Living with a Star program. 

In addition to these roles, Dr. Mlynczak is an Affiliate Scientist at the High Altitude Observatory 

of the National Center for Atmospheric Research and is formerly an Associate Editor for the 

Journal of Geophysical Research-Space Physics. In 2012 Dr. Mlynczak received the NASA 

Distinguished Service medal, the highest honor bestowed by the Agency. 

Thomas L. Murdock (Frontier Technology, Incorporated) 

Vice President, Technology 

As VP of Technology, Dr. Thomas L. Murdock has been, over the last forty years, a leader in the 

development of IR sensors, science and technology; the processes used in EO sensor data 

collection, performance evaluation, and calibration; reduction, analysis and modeling of 

geophysical data; and the application of this knowledge to space systems design and operation. 

Dr. Murdock was a Co-Investigator on the National Aeronautics and Space Administration 

(NASA) Cosmic Background Explorer (COBE) program, and he led a segment of the calibration, 
127 

testing, and modeling efforts that support the Diffuse Infrared Background Experiment (DIRBE) 

portion of that mission. He also participated in the data analysis that led to the exceptional scientific 

results of the COBE mission, including the first detection of the structure in the microwave cosmic 

background and confirming the blackbody shape of its spectrum. The COBE program won the 

Gruber Foundation Cosmology Prize in August 2006, and in December 2006 two of the principal 

investigators won the Nobel Prize in Physics. 

Dr. Murdock was the MDA Midcourse Space Experiment (MSX) Principal Investigator for Data 

Certification and Technology Transfer with responsibility for the oversight of the MSX calibration 

and characterization efforts for the eleven EO-IR sensors and the certification of the radiometric 

and goniometric accuracy and precision of the MSX data products from the cryogenic portion of 

that mission. 

James Q. Peterson (Space Dynamics Laboratory) 

Principal Electro-Optical Sensor & Calibration Engineer 

Mr. Peterson has worked at the USURF Space Dynamics Laboratory since he received his MSEE 

in 1988. Prior to this, he worked at SDL as a student engineer for four years. Mr. Peterson is 

currently a Principal Engineer (Engineer IV) in the calibration and EO Directorate, where he 

specializes in spectral calibration, and is actively involved in EO sensor design, assembly, 

calibration, modeling, data collection and analysis. 

Mr. Peterson was the lead of the SDL Ground and Airborne Measurements, Experiments and 

Signatures (GAMES) team and has supported and/or led many field measurement campaigns 

including both manned and un-manned aircraft EO signature measurements. He also provided data 

for validation and verification (V&V) of the SPIRIT III model development. 

He has extensive experience in IR radiometry, hyper-spectral imaging, hyper-temporal analysis 

(frequency signatures), and imaging polarimetry. His role in the spectral calibration of flight-ready 

EO sensors, using a Fourier transform spectrometer, has made him a national leader in IR spectral 

calibration. 

David B. Pollock (University of Alabama in Huntsville)

Associate Research Professor/Senior Research Scientist Electrical Computer 

Engineering/Center for Applied Optics

While Professor Pollock’s primary responsibility is education, he also works to advance the state 

of radiometric calibration. Specific areas of expertise include: radiometric calibration of remote 

sensors from the vacuum ultra-violet (300 nm) through the long wavelength infrared (30 μm) over 

a temperature span of 4 K to ~ 6x106

 K, and prediction of ionizing radiation for space sensors. Dr. 

Pollock also has expertise in sensor operation in the endo- and exo-atmosphere operational 

environments. Prof. Pollock’s thorough understanding of physics and physics-based models for 

the generation, propagation, and detection of electromagnetic energy provide the insight to 

collaborate with National Laboratories, Department of Defense, and commercial companies. 

Since 1960, Prof. Pollock has worked with the international remote sensing community to establish 

remote sensor data uncertainty relative to the International System of Units. Current specific work 

is collaboration with colleagues to recognize a lunar flux scale traced to the International System 

of Units. 
128 

Prof. Pollock served as the Deputy Principal Investigator for Data Certification and Technology 

Transfer during the Midcourse Space Experiment Program. He contributed his experience to the 

ASIC3

 Workshop on Definitions of Measurement Uncertainty Quantities. 

Ray W. Russell (The Aerospace Corporation) 

Senior Scientist, Remote Sensing Department, Space Science Applications Laboratory 

Dr. Russell’s research is concerned with spectroscopic studies of comets and meteor trails, dust 

enshrouded stars, planetary nebulae, planets, and the Moon. Both fixed and variable IR stars used 

as on-orbit calibrators for IR sensors are currently part of his observing program for the Air Force 

and astronomical communities. 

As a designated NASA expert, Dr. Russell has served on numerous proposal review panels, 

including the design review team for WIRE. In addition, he served as the Data Certification and 

Technology Transfer team “Watchdog” for SPIRIT III on the Midcourse Space Experiment 

(MSX). The National Academy of Sciences twice invited him to present material from his work 

on various programs, including MSX. He has served in a review and advisory capacity to numerous 

DoD programs, such as Brilliant Eyes, Halo I and II, Argus, SBIRS Low, and SBIRS High. The 

JASON group invited him to present material on infrared observations of RSOs. He has been on 

the organizing committee for the SDL/USU calibration conferences for several years. He has over 

120 reviewed publications, and is a visiting astronomer at the NASA IRTF. He is currently 

involved in the generation of a high accuracy stellar spectral energy distribution catalog based 

mainly on his observations of ~ 90 bright IR stars spanning over 25 years. 

Deron Scott (Space Dynamics Laboratory) 

Radiometric Engineer 

Mr. Scott is a program manager and radiometric engineer at SDL. With over 20 years of experience 

in the calibration of infrared sensors, he has worked on the calibration of many sensors, including 

serving as the technical lead for calibration of the Commercially Hosted Infrared Payload 

(CHIRP), and lead calibration engineer for the Geosynchronous Infrared Fourier Transform 

Spectrometer (GIFTS) sensor module and the SBIRS High program transfer radiometer. He has 

also served as co-chairman for the annual CALCON conference since 2007. 

Mr. Scott is currently the program manager for SDL's involvement with the Joint Polar Satellite 

System (JPSS), and SDL's work with the Global Space-Based Inter-Calibration System (GSICS). 

As a member of the JPSS government team, SDL provides calibration and validation analysis of 

the CrIS and ATMS sensors, including subsystem testing, ground calibration, and on-orbit 

operations. SDL's GSICS role includes providing support to NASA and NOAA for on-orbit 

processing of meteorological data. 

John A. Seamons (Space Dynamics Laboratory) 

Senior Calibration Engineer 

Dr. Seamons is actively involved in the on-orbit calibration of the commercially hosted infrared 

payload (CHIRP) for the US Air Force (USAF). He has worked on the calibration of many sensors 

at SDL, including ABIR MTS-B, 3GIRS CHIRP, and TIRS. He has also participated in the GSICS 

activities. 
129 

During the ABIR MTS-B calibrations of sensors #1134 and #1135, Dr. Seamons performed the 

role of lead shift calibration engineer. He developed the IDL-based scripts for execution of 

automated data collection using the SDL Test Director software, and played a pivotal role in 

ensuring that the maximum amount of useful calibration data was collected during the two 

calibrations. Dr. Seamons work on the 3GIRS CHIRP calibration project included serving as a 

lead shift calibration engineer during the CHIRP calibration. He developed the IDL based scripts 

for execution of automated data collection using the SDL Test Director. 

Dr. John Seamons is currently involved in infrared calibration efforts in the Infrared Space (IS) 

System Directorate at the Space and Missile Systems Center (SMC) at Los Angeles Air Force Base 

as a contractor from SDL. The IS mission is to develop, acquire, and sustain space-based infrared 

surveillance, tracking, and targeting capabilities for missile early warning and defense, battlespace awareness, and technical intelligence. 

Tom Stone (USGS) 

Project Scientist, Lunar Calibration 

Dr. Stone heads the Lunar Calibration project at the U.S. Geological Survey (USGS) Astrogeology 

Science Center in Flagstaff, AZ. In this position, he facilitates radiometric calibrations of spacebased instruments against the Moon as a reference light source, and directs the further development 

of the photometric models that constitute the lunar reference. He is involved with analysis of the 

lunar radiometry from Earth observing satellite sensors, and generates and provides the reference 

lunar irradiances corresponding directly to the spacecraft observations. He has developed or 

collaborated on lunar calibrations for numerous research instruments flown by NASA (NPPVIIRS, MODIS, SeaWiFS, ASTER, CERES, ALI, Hyperion and others) and for operational 

satellite agencies such as NOAA (GOES-5 through GOES-15, AVHRR), USGS (Landsat-8 OLI) 

and EUMETSAT (Meteosat-5 through Meteosat-10). He is involved with planning on-orbit lunar 

calibrations for future research and operational instruments such as the Orbiting Carbon 

Observatory (OCO)-2, Earth Polychromatic Imaging Camera (EPIC), Meteosat Third Generation 

FCI, European Polar Satellite METimage, the Advanced Baseline Imager (ABI) on GOES-R, and 

the VIIRS instruments on the JPSS constellation. While earning his Ph.D. degree at the University 

of Arizona in Tucson, Dr. Stone conducted radiometric and spectral calibrations of low-light 

airglow and plasma imaging instruments flown on the space shuttle (GLO, UVSTAR) and into 

deep space (IMAGE-EUV, DS-1 MICAS), where he was involved with design, integration and 

testing, TVAC, ground-support equipment, flight operations, and calibration acquisition, analysis, 

and reporting activities. 

Joe Tansock (Space Dynamics Laboratory) 

Principle Calibration Engineer, Calibration Group Lead, and Deputy Test and Calibration 

Division Director 

Mr. Tansock has primary responsibility for all aspects of system calibration at SDL including 

systems engineering support, test and calibration, specialized calibration equipment, and 

calibration activities throughout sensor operational life including on-orbit calibration. Specific 

areas of expertise include calibration planning, test, data analysis, and the development of 

specialized calibration equipment. 

Mr. Tansock has provided calibration, systems engineering, and/or lead support to numerous 

projects, including SPIRIT III, SABER, DXPS, RAMOS, DXPS, WIRE, GIFTS, MKV, SDL- 
130 

XR, CLARREO IR Spectrometer, NGC transfer radiometer recalibration, ABIR, and PTSS. He 

has also been involved with the design, test, and calibration (including analysis) of numerous 

specialized test equipment systems such as HMC, MIC5, SDL-XR, and BB sources. 

In addition to his technical roles at SDL, he served as committee and conference chair for the 

“Characterization and Radiometric Calibration for Remote Sensing” conference held at SDL/USU 

from 2000 through 2005, and continues to serve as a CALCON technical committee member. 

Working with steering and organizing committees, he also organized and conducted the Achieving 

Satellite Instrument Calibration for Climate Change (ASIC3

) workshop held at Landsdown, VA in 

May 2006. 

Alan Thurgood (Space Dynamics Laboratory) 

Director, System Calibration & Test Division 

Mr. Thurgood has been involved in the design, integration, testing, and flight support of numerous 

cryogenically cooled infrared systems during more than 30 years with the Space Dynamics 

Laboratory (SDL). He is currently the director of SDL’s System calibration and Test Division and 

has led the calibration group at SDL since 1992. 

Mr. Thurgood has worked with interferometer/spectrometers during most of his career, including 

two cryogenically cooled rocket-borne field-widened interferometers that were used to study the 

upper atmospheric phenomenology during the Aurora Borealis. He also supported testing and field 

operations for the Balloon Altitude Mosaic Measurements (BAMM) program, which was one of 

the earliest IR hyperspectral imaging sensors. Mr. Thurgood has led the technical development of 

numerous sensors and calibrators, including the calibrator for NASA’s DIRBE sensor, a Michelson 

interferometer for the EXCEDE III program. and a multispectral imaging system for the BEPOP 

program, which was a ground based sensor that was taken to a number of facilities and attached to 

host telescopes to track numerous space objects. He also was the System Engineer for the 

development phase of the GIFTS program. 

Mr. Thurgood has played a key role in many calibration efforts including the Advanced Flight 

Telescope (AFT) sensors, MDA’s Near Field Infrared Experiment (NFIRE) Track Sensor Payload 

(TSP), and the Spatial Infrared Imaging Telescope III (SPIRIT III). He was named chairman of 

the SPIRIT III Performance Assessment Team (PAT), which was responsible for the on-orbit 

calibration planning and analysis in addition to sensor performance verification throughout the 

mission. 

Richard Williams 

Dr. Richard Williams is the system architect and project engineer for the Advanced Sensor 

Integration and Test Facilities at Northrop Grumman Corporation in Azusa CA. These test 

facilities have been used to integrate, functionally test and calibrate the Space Based Infrared 

(SBIRS) sensors and payloads. 

 
131 

Xiaoxiong (Jack) Xiong (Sciences and Exploration Directorate at NASA GSFC) 

Optical Physicist 

Dr. Xiong is an optical physicist in the Sciences and Exploration Directorate at NASA Goddard 

Space Flight Center (GSFC), currently supporting Terra, Aqua, S-NPP, and JPSS missions and 

projects on optical sensors pre-launch and on-orbit calibration and characterization. He received a 

B.S. degree in optical engineering from Beijing Institute of Technology, Beijing, China, and a 

Ph.D degree in physics from University of Maryland, College Park. In addition to remote sensing 

applications and sensor calibration, Dr. Xiong had also worked in the fields of optical 

instrumentation, nonlinear optics, laser and atomic spectroscopy, and resonance ionization mass 

spectrometry at universities, private industry, and the National Institute of Standards and 

Technology (NIST) in Gaithersburg, Maryland. Currently, Dr. Xiong also serves as chair for two 

of the SPIE conferences on “Earth Observing System” and “Earth Observing Missions and 

Sensors: Development, Implementation, and Characterization”. 

Dr. Howard Yoon (Optical Radiation Group, Sensor Science Division, NIST) 

Physicist 

Howard Yoon is a physicist with over 18 years of experience in spectroradiometry and radiation 

thermometry in the Sensor Science Division at NIST. He has organized the NIST 

Spectroradiometry Short Course since its inception, and is the U.S. national representative for 

radiation thermometry on the Consultative Committee for Thermometry at the BIPM. He received 

his Ph.D. in solid-state physics from the University of Illinois at Urbana-Champaign and his BA 

from Swarthmore College. He has coauthored over 110 technical publications, mostly in the areas 

of spectroradiometry and radiation thermometry, and has 2 patents in the area of 

spectroradiometry. He has twice won the NIST Astin award for measurement science and was also 

the recipient of the U.S. Department of Commerce silver medal for scientific achievement.