SGC-180025

GCOM-C “SHIKISAI”
Data Users Handbook

First Edition
December, 2018

Preface
As the global environment change has become a worldwide concern such as global warming-up
progress in recent years, the studies related to climate change prediction are promoted in order tokeep environment where future people are able to lead their lives. For elucidating global climatechange, weather forecasting, water circulation mechanisms, JAXA (Japan Aerospace Exploration
Agency) is promoting “GCOM (Global Change Observation Mission)” which aims to verify theeffectiveness by constructing systems that enable long-term continuous global-scale observations
and by utilizing their data to the climate change studies, weather forecasting, and fishing industry.

GCOM is consisting of the two series of observation satellites: Global Change Observation
Mission-water (GCOM-W) and Global Change Observation Mission-climate (GCOM-C). GCOM-W“SHIZUKU” was launched on May 18, 2012, and is now continuing observation operation withAMSR2 (Advanced Microwave Scanning Radiometer 2) on board. Subsequently, GCOM-C“SHIKISAI” was launched on December 23, 2017, and the initial checkout operation for threemonths after launch was completed and is continuing observations SGLI (Second-generation
Global Imager) on board.

SGLI mounted on GCOM-C is the succession sensor of the Global Imager (GLI) mounted on
ADEOS-II (MIDORI II) and is the imaging radiometer which measures the radiation fromnear-ultraviolet to thermal infrared region (380 nm-12 μm) in 19 channels. Global observation of

once for approximately every two days is possible at mid-latitude near Japan by observation width
at ground greater than 1,000 km. In addition, SGLI realizes high resolution than the similar global
sensor and has a polarized observation function and a multi-angle observation function.

The observation data by SGLI is transmitted to JAXA/Tsukuba Space Center via online after

downlinked to ground stations (JAXA domestic stations in Japan and Svalbard Station) and dataprocessing and provision is performed. 28 types of geophysical variable data set (vegetation/
biomass, sea surface temperature, ocean-color, aerosol, cloud characteristics, snow-ice
characteristics, etc.) covering land, atmosphere, ocean and cryosphere are generated by the SGLI
global observation data.

JAXA performs Initial calibration and validation of SGLI observation data, and Level1 products(radiance) and Level2 & Level3 products (geophysical variable) will be released to the public via
Globe Portal system (G-Portal：https://gportal.jaxa.jp/gpr/) in December 2018.

This handbook intends to provide the necessary information for using GCOM-C/SGLI product. We
hope that effectiveness of these data are demonstrated in fishery and weather prediction, as well

as contribute to solve the global environmental change mechanism and climate change impact,
monitoring of global environmental change and environmental preservation.
December 2018

Japan Aerospace Exploration Agency
GCOM Project TeamGCOM-C “SHIKISAI” Data Users Handbook
First Edition
Contents

GCOM-C “SHIKISAI” Data Users Handbook
Contents
Chapter 1 Introduction .................................................................................................................. 1-1

1.1 Purpose of This Document ........................................................................................................ 1-1
1.2 Scope of This Document ............................................................................................................ 1-1

1.3 Overview of GCOM-C/SGLI Mission ....................................................................................... 1-2
Chapter 2 Overview of GCOM-C/SGLI ......................................................................................... 2-1

2.1 Overview of GCOM-C................................................................................................................ 2-1

2.2 Second-generation Global Imager （SGLI） ............................................................................... 2-5
Chapter 3 Overview of GCOM-C Ground System ......................................................................... 3-1

3.1 Overall System .......................................................................................................................... 3-1
3.2 Satellite Control System ........................................................................................................... 3-2
3.3 Mission Operation System ........................................................................................................ 3-2

3.4 Receiving and Recording Equipment ....................................................................................... 3-3
3.5 Ground System Operation ........................................................................................................ 3-4
Chapter 4 SGLI Products .............................................................................................................. 4-1

4.1 Product Definitions ................................................................................................................... 4-1
4.2 Level1 Processing Algorithms ................................................................................................ 4-23

4.3 Level2 & Level3 Processing Algorithm .................................................................................. 4-38
4.4 Product Formats ...................................................................................................................... 4-50

4.5 Data Calibration and Validation ............................................................................................ 4-50
Chapter 5 Data Providing Service ................................................................................................. 5-1

5.1 Product Provision Policy ........................................................................................................... 5-1

5.2 User Tool .................................................................................................................................... 5-3
5.3 I/O Toolkit .................................................................................................................................. 5-3i

GCOM-C “SHIKISAI” Data Users Handbook
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Contents

List of Tables and Figures
Table 2-1 Main Specifications of GCOM-C ............................................................................ 2-3
Table 2-2 Satellite Bus Subsystems ...................................................................................... 2-4
Table 2-3 SGLI Channel Specification ................................................................................... 2-6

Table 2-4 VNR Sensor Performance ..................................................................................... 2-6
Table 2-5 SWIR Sensor Performance.................................................................................... 2-7

Table 2-6 TIR Sensor Performance ....................................................................................... 2-7
Table 2-7 SGLI Calibration Types ........................................................................................ 2-13

Table 2-8 SGLI Basic Observation Pattern .......................................................................... 2-17

Table 3-1 Ground Station Satellite Operation ........................................................................ 3-4

Table 3-2 Near-real time Products (Japan and Vicinity) ........................................................ 3-4

Table 3-3 Near-real time Products (Global) ........................................................................... 3-5
Table 4-1 Definition of SGLI Products.................................................................................... 4-1

Table 4-2 Average Solar Irradiance F0 (at 1AU) .................................................................... 4-5
Table 4-3 Level1 Product Granule ID ..................................................................................... 4-6
Table 4-4 Level1 Product Granule ID Setting Value .............................................................. 4-7

Table 4-5 Symbols for Seconds in Level1 Product Granule ID ............................................. 4-8
Table 4-6 Spatial Resolution Identification ........................................................................... 4-11
Table 4-7 List of Level2 Product (1/2) .................................................................................. 4-12
Table 4-8 List of Level2 Product (2/2) .................................................................................. 4-13

Table 4-9 List of Level2 Statistical Product .......................................................................... 4-14
Table 4-10 Level2 Product (Scene) Granule ID ................................................................... 4-17

Table 4-11 Level2 Product (Tile/Global) and Level3 Product Granule ID ............................ 4-17
Table 4-12 Level2 Product (Scene) Granule ID Setting Value ............................................ 4-18
Table 4-13 Level2 Product (Tile/Global) and Level 3 Product Granule ID Setting Value .... 4-18

Table 4-14 List of Level3 Product (1/2) ................................................................................ 4-20
Table 4-15 List of Level3 Product (2/2) ................................................................................ 4-21

Table 4-16 Brief Overview of SGLI Sensor .......................................................................... 4-23
Table 4-17 Brief Overview of Level1 Process ...................................................................... 4-24

Table 4-18 Overview of Preprocessing ................................................................................ 4-27
Table 4-19 Sequence of Pixels for VNR-NP Observation ................................................... 4-29

Table 4-20 Sequence of Pixels for VNR-PL Observation .................................................... 4-29
Table 4-21 Overview of SGLI Level2 & Level3 products Verification Method（1/6） ............ 4-53
Table 4-22 Overview of SGLI Level2 & Level3 products Verification Method（2/6） ............ 4-54
Table 4-23 Overview of SGLI Level2 & Level3 products Verification Method（3/6） ............ 4-55
Table 4-24 Overview of SGLI Level2 & Level3 products Verification Method（4/6） ............ 4-56
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Table 4-25 Overview of SGLI Level2 & Level3 products Verification Method（5/6） ............ 4-57
Table 4-26 Overview of SGLI Level2 & Level3 products Verification Method（6/6） ............ 4-58

Table 5-1 Provision of GCOM-C/SGLI Products.................................................................... 5-1
Table 5-2 G-Portal Product Storage Area/Storage Period (Real time Storage) .................... 5-2

Table 5-3 SGLI User Tool Function ........................................................................................ 5-3
Figure 1-1 Development and Operation Schedule for JAXA Earth Observation Satellite..... 1-3
Figure 1-2 Mission of JAXA’s Climate and Environment Observation Satellite .................... 1-3
Figure 1-3 Optical Thickness of Aerosol Observed by Terra/Aqua........................................ 1-5

Figure 1-4 Estimated value- and factor-based aggregation of radiative forcing (Source: IPCC
AR5 WG1 SPM) .............................................................................................................. 1-6

Figure 1-5 Vegetation Index and Sea Surface Temperature observed by Terra/Aqua.......... 1-8
Figure 1-6 Balance of CO2 Emissions and Absorptions (Source: IPCC AR5 WG1 SPM） ... 1-8

Figure 1-7 Sea Color around Japan Observed by SGLI ....................................................... 1-9
Figure 1-8 Land Surface Temperature (left) and Vegetation (right) Based on SGLI Data ................. 1-10
Figure 1-9 Color Composite Images of Areas around Japan Based on SGLI data ............ 1-12

Figure 1-10 Volcanic Smoke of Mt. Sinabung in Sumatra, Indonesia Observed by SGLI .. 1-13
Figure 2-1 Appearance of GCOM-C ...................................................................................... 2-2

Figure 2-2 Appearance of SGLI ............................................................................................. 2-5

Figure 2-3 Relationship between Geophysical Variables of Climate Change Factors and SGLI
Observation Wavelengths............................................................................................... 2-7

Figure 2-4 VNR Response Function ...................................................................................... 2-7

Figure 2-5 IRS Response Function ....................................................................................... 2-8
Figure 2-6 Observation Image of SGLI .................................................................................. 2-8

Figure 2-7 Tilting Mechanism of PL Observation ................................................................... 2-9
Figure 2-8 VNR SRU............................................................................................................ 2-10

Figure 2-9 IRS SRU ............................................................................................................. 2-11
Figure 2-10 Channel Array in VNR-NP ................................................................................ 2-12
Figure 2-11 Array of Polarizing Direction of VNR-PL ........................................................... 2-12

Figure 2-12 Definition of IRS Scanning Angle and View ..................................................... 2-13

Figure 2-13 VNR Solar Diffuser and Internal Lamp Calibration .......................................... 2-14
Figure 2-14 IRS Calibration Features .................................................................................. 2-15

Figure 2-15 Overview of SGLI Observation Operation ........................................................ 2-17

Figure 3-1 GCOM-C Ground System .................................................................................... 3-2
Figure 4-1 SGLI Level1 and Level2 Processing Flow ........................................................... 4-2
Figure 4-2 SGLI Level2 and Level3 Processing Flow ........................................................... 4-2

Figure 4-3 GCOM-C Path Definition (Path 1 to 151) ............................................................. 4-9iii

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Figure 4-4 GCOM-C Path Definition (Path 181 to 331) ......................................................... 4-9
Figure 4-5 GCOM-C Path Definition (Path361 to 481) .......................................................... 4-9
Figure 4-6 EQA (sinusoidal equal area) projection .............................................................. 4-16

Figure 4-7 Definition of Level3 Product EQA projection (1-Dimension) .............................. 4-22

Figure 4-8 Definition of Level3 Product EQR projection...................................................... 4-22
Figure 4-9 Definition of Level3 Product PS projection ......................................................... 4-23

Figure 4-10 VNR-NP Level1 Processing Flow..................................................................... 4-24
Figure 4-11 VNR-PL Level1 Processing Flow ..................................................................... 4-25

Figure 4-12 IRS-SWIR Level1 Processing Flow.................................................................. 4-25

Figure 4-13 IRS-TIR Level1 Processing Flow ..................................................................... 4-26
Figure 4-14 Radiometric Information Calculation: (left) NP/PL/SWIR, (right) TIR .............. 4-28
Figure 4-15 Relationship between IRS Lines and Pixels .................................................... 4-30

Figure 4-16 Processing Flow of Calculation of Latitude and Longitude .............................. 4-32

Figure 4-17 Schematic Diagram: Calculation of Latitude/longitude of Observation Point .. 4-34
Figure 4-18 Definition of Level1B Reference Coordinate .................................................... 4-34

Figure 4-19 Ideal Observation Value (left) and Observation Value with Stray Light (right) . 4-35
Figure 4-20 Overall Flow of Precise Geometric Corrected Radiance Algorithm ................. 4-39

Figure 4-21 Outline of SGLI Calibration and Verification Schedule .................................... 4-50

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Introduction

Chapter 1Introduction
Melting glacier and ice sheet in polar region, extraordinary high temperature and supertyphoon that take a heavy toll of lives, expansion of infectious diseased areas such as dengue
fever…today, the global warming has been seriously progressing and changes in the global

environment that have never been experienced are observed. The average global

temperature has risen by about 1°C over the past 30 years, and concerns are being raised

about a further rise in the future. However, it is difficult to forecast the future climate andcurrent temperature projections for the year 2100 are uncertain and vary in a range ofapproximately 2°C. The reasons for uncertainty in the forecasts are that the sensitivity and themechanism of the climate system are still unknown. "Radiation budget" and "carbon cycle"
are the factors that have a particularly large impact on climate.

Through continuous and long-term observations of radiation budget and carbon cycle, we

can derive quantitative relationships between the fluctuations of these elements and climatechange, making it possible to predict their impact on the climate.

GCOM-C (Global Change Observation Mission – Climate “SHIKISAI”), the climate change

observation satellite developed by JAXA(Japan Aerospace Exploration Agency), conducts
long-term global observations of geophysical variables related to the global climate system

across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean,and cryosphere. The data will be used to contribute to higher accuracy of global warming
prediction.

1.1 Purpose of This Document

This document provides users who obtain SGLI products publically released by JAXA with

the information needed to make effective use of these products. It also gives information

related to various products including standard products, GCOM-C satellite, the onboard
sensor, and the ground system.
1.2 Scope of This Document

This document provides overview of the information necessary for the users including

descriptions of the satellite, the sensor, the ground systems, the products and the data

providing service. This document includes information that users need to access the data
providing service in order to obtain data sets. The document is comprised of five chapters andan annex:

Chapter 1

Introduction

Chapter 3

Overview of GCOM-C Ground System

Chapter 2

Overview of GCOM-C/SGLI

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Introduction

Chapter 4
Chapter 5

SGLI Products

Data Providing Service

Annex — List of Abbreviations, Related Information, SGLI Product Formats
1.3 Overview of GCOM-C/SGLI Mission

SGLI (Second-generation Global Imager) is the sensor mounted on GCOM-C satellite. This

chapter gives the descriptions of the GCOM concept as a background to the measurement
sensor, followed by the descriptions of SGLI.（Source: GCOM-C Climate Change Observation
Satellite (Briefing document), (Japanese)）
1.3.1 GCOM Concept

The Global Change Observation Mission (GCOM) project was launched for the purpose of

conducting global and long-term observations of the earth’s environment from space. Its mainrole is to observe water circulation and climate change, and monitor the earth’s health fromspace. GCOM consists of two series of satellites: GCOM-W (Global Change Observation

Mission – Water “Shizuku”) and GCOM-C (Global Change Observation Mission – Climate“Shikisai”). The GCOM-C, equipped with the Second-generation Global Imager (SGLI) – a
multi-band optical imaging radiometer, conducts observations of clouds, aerosols(atmospheric dust), ocean color, vegetation, snow/ice, and other properties. The GCOM-W,
equipped with the Advanced Microwave Scanning Radiometer 2 (AMSR2), conducts
observations of precipitation, water vapor, ocean wind speeds, sea surface temperature, soil
moisture content, snow depth, and more.

A global and long-term observation by GCOM is expected to help monitor and elucidate themechanism behind water circulation and climate change. Figure 1-1 shows the development
and operation schedules for JAXA earth observation satellites, and Figure 1-2 shows themission of JAXA’s climate and environment observation satellites.

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Introduction

Figure 1-1 Development and Operation Schedule for JAXA Earth Observation Satellite
Because of the earth’s massive size, it is difficult for one country to act alone in conducting

observations needed to grasp the trends in global environmental changes. Therefore, acoordinated international framework called the Global Earth Observation System of Systems(GEOSS) has been established, under which participating countries are administering theirspace programs for monitoring the climate and the environment. GCOM is the mission that
contributes to GEOSS.

Figure 1-2 Mission of JAXA’s Climate and Environment Observation Satellite

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1.3.2 Overview of SGLI Mission

1.3.2.1 Radiation Budget: Observation of Aerosol and CloudThis section describes the observation of aerosol and cloud in relation to the radiationbudget. Aerosol is a collective term for fine solid particles floating in the atmosphere. Aerosols
and clouds have a major influence on climate change, but the extent of the influence in regard

to global warming is still uncertain. GCOM-C contributes to improve the precision of climatechange forecast through its highly accurate, long-term observation of aerosol and cloud.

There are various types of aerosols such as “soot” produced by fossil fuels or forest fire,“sulfuric acid (sulfate)” derived from the emitted gas of fossil fuels, volcanic explosion or

marine plankton, “marine salt and yellow sand” blown by wind, and “volcanic ash” and “pollen.”
PM2.5, which recently became a big topic as it was brought from China in high density andhas negative impact on human health, is also a type of aerosol. Those extremely small floats,
aerosols, are greatly affecting the global environment in the form of global warning and acidrain.

One of the impacts is “direct effect” that lowers the ground surface temperature as aerosols

scatter or absorb the sunlight directly. When the Mt. Pinatubo in Philippines erupted in 1991, ahuge amount of microparticulated volcanic ash and volcanic gas became aerosols andformed a “Parasol” that shut off the sunlight. As a result, for over two years, the temperature

was lowered by about 0.5 °C globally. At the same time, it is known that the sulfuric acidaerosol carried to the stratosphere destroyed a significant volume of the ozone layer. In thisway, aerosol itself has very strong impact.

Another impact is “indirect effect” that aerosols become condensation nucleuses when cloudis formed by moisture. Moisture in the atmosphere becomes water or ice as the temperature
decreases and forms cloud particles. At the time, the larger the number of aerosols to be

nucleuses, the larger the volume of small cloud particles. When clouds are formed from anequivalent volume of moisture, the cloud color becomes whiter and brighter as the contained

cloud particles becomes smaller, and accordingly, the sunlight coming into the earth isreflected well and thus the ground surface temperature is lowered. Just as described,
aerosols have the effect to impact on the climate change through cloud formation.

The aerosol direct and indirect effects have a major influence on climate change, but theextent of influence in regard to global warming is still uncertain. This is because expert
opinion is still divided on whether clouds, which are changed in characteristic as global

warming progresses, will cause the acceleration or deceleration of global warming.. The
long-term observation of aerosols and clouds is necessary to get accurate quantification of
the influence of aerosols.

In addition to the visible and near-infrared radiometer of other conventional satellites,

GCOM-C also combines proprietary near-ultraviolet and polarization observation functions
that can provide highly accurate observations of aerosols emitted from human activity,
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volcanoes, forest fires and other terrestrial events. All these features will enable long-term
global observation of aerosols.

Figure 1-3 shows the aerosol optical thickness observed from NASA’s Terra/Aqua satellite in

October 2015. The redder the map, the thicker the optical thickness. In Indonesia, a large
amount of smoke produced by the large-scale open burning performed every fall causes

serious air pollution in neighboring nations (center of the figure). Most of the aerosol emitted
through human activity is generated on land and terrestrial aerosol was difficult to observewith a high level of accuracy with conventional satellites.

Figure 1-4 is a bar graph showing the extent of the effect of global warming and coolingcaused by greenhouse gases, aerosols and other factors as reported by the IPCC. The
estimation of the influence of aerosol still includes a great extent of uncertainty.

Figure 1-3 Optical Thickness of Aerosol Observed by Terra/Aqua*Terra/Aqua data was processed at JAXA

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Figure 1-4 Estimated value- and factor-based aggregation of radiative forcing (Source:
IPCC AR5 WG1 SPM)

1.3.2.2 Carbon cycle: Observation of vegetation and sea surface temperatureThis section describes the observation of vegetation and sea surface temperature in relation

to the carbon circulation. CO2 is regarded as the primary contributor to global warming.
Although vegetation and sea surface temperatures are said to greatly add to theconcentration of carbon dioxide to the atmosphere, accurately estimating the extent ofabsorption of both factors is a major difficulty. GCOM-C observes global vegetation,

temperature distribution and their changes over a long period of time to understand theabsorption and emission mechanism of CO2 and contributes to improve the prediction
accuracy of climate model.

The CO2 concentration in the atmosphere has been greatly increased in the last few

decades. Although CO2 concentration has been fluctuating between 200 and 300 ppm overthe past tens of thousands of years, the fluctuation was very gradual. This is because thebalance between the CO2 emission from breathing of animals and plants and decompositionof their carcasses and the CO2 the absorption by plant photosynthesis and dissolution into thesea controlled the rapid change of CO2 concentration in the atmosphere.

Today, CO2 emission has been increased dramatically by human activities, and theaforementioned balance is corrupting. At least a half of CO2 emission from human activities

has been absorbed by plants and marine, but as the volume of CO2 emission is extremely
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Introduction

large, CO2 concentration has been increased by 40% comparing to 1750s when industrial
revolution was started. Such rapid increase of CO2 concentration has never been seen before
in earth’s history.

In the future, when global warming is accelerated, if the volume of CO2 absorption by plants

and marine can be predicted, the influence of global warming on vegetation and marine on

global warming can be understood. However, regarding plants, leaves absorb CO2 byphotosynthesis but other parts emit CO2 by breathing for day and night. Further, absorption
and emission of CO2 changes depending on the climate conditions such as temperature andrain fall, CO2 concentration in the atmosphere, and species or age of trees. Therefore, it isextremely difficult to accurately estimate the change of forest distribution in the future and how
the change influences the volume of CO2 absorption.

In addition, the volume of CO2 absorbed by ocean from the atmosphere is fluctuated by theCO2 concentration in the atmosphere and sea surface. Especially, factors that influence CO2
concentration of the sea surface are various including sea surface temperature and creature
activities. For instance, if the water temperature rises, just like warmed soda, CO2 in sea

water cannot be dissolved completely and it will be released to the atmosphere. As biologicalactivities increase, CO2 concentration becomes lower as phytoplanktons consume CO2. If

global warming is accelerated, in the future, higher temperature of sea water disturbs thedissolution of CO2 strongly and thus marine absorption of CO2 is predicted to be decreased.
However, when other various factors such as the effect of marine acidification caused by theabsorbed CO2 on the marine ecology are considered, accurate estimation of absorption
volume is also a very difficult task.

GCOM-C leverages its mid- to high-resolutions of 250 m, 500 m, and 1 km and highfrequency observation capability to observe vegetation and sea surface temperatures globally.
In particular, GCOM-C can use its multi-angle observation feature to collect data on

vegetation roughness which is expected to greatly improve the accuracy of land coverclassification of coniferous forests, broad-leaved forest, grasslands, deserts and other terrains
across the globe.

Figure 1-5 shows the vegetation index (land) and sea surface temperature (ocean) observedby Terra/Aqua in August 2017. The vegetation index is a proxy for vegetation distribution andproductivity: it is shown that the value is high in the Amazon and low in the Sahara. Also, theNorth Atlantic Current is apparent from the observation of sea surface temperature that is
flowing from the Gulf of Mexico to the western coast of Europe.

Figure 1-6 shows the balance of CO2 emissions (red) and absorptions (blue) in theatmosphere as reported by IPCC. Comparing to the estimation of CO2 volume emitted byusing fossil fuels and cement production, there is still great uncertainty regarding theestimation of CO2 volume that can be absorbed through land-sea ecosystems.
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Figure 1-5 Vegetation Index and Sea Surface Temperature observed by Terra/Aqua*Terra/Aqua data was processed at JAXA

Figure 1-6 Balance of CO2 Emissions and Absorptions (Source: IPCC AR5 WG1 SPM）
1.3.2.3 Use of Observation Data

GCOM-C is used to observe various global environments that are affected by climatechange in conjunction with other factors such as aerosol, sea surface temperatures,
vegetation, snow and ice, solar radiation, and phytoplankton. Data collected by GCOM-C will

not only play a role in future climate projection, but will also bring many benefits to our daily
lives.

(1)to (4) shows the samples of GCOM-C/SGLI data use. For other data use, please refer to

JAXA/EORC (https://suzaku.eorc.jaxa.jp/GCOM_C/monitor/gallery/index_j.html).

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Introduction(1) Example of Data Use 1 - Fishery -Seafood is an important part of daily diets in many countries. GCOM-C is used for wide-area

observation of sea surface temperatures that greatly affect the ecosystems of marine life
including plankton, which is the first link in the food chain. Conducting science-based analysisof the collected data and using the results to help fisheries search for productive fishingground will lead to more stable catches. Helping us enrich our diet is also another importantrole of GCOM-C.

Figure 1-7 shows color composite images of the periphery of Tsushima Straits at 11:10 AM

on January 1st, 2018 (JST: Japan Standard Time) (center image) and the coastal area andoffshore of Kanto region at 10:28 AM on January 6th, 2018 (JST) (right image) which wereobserved at a resolution of 250 m (the map on the left shows where the images are taken).
SGLI equipped with a channel for marine observation is able to observe dark sea surface at ahigh sensitivity so that it is able to capture the slight difference in the color generated due tothe concentration difference of suspended matters and plankton. As shown in Figure 1-7,

enabling to observe the sea color of coastal sea areas in detail, it is expected to be useful for
predicting fisheries and understanding red tide occurrence status.

Figure 1-7 Sea Color around Japan Observed by SGLI

Color composite images of the periphery of Tsushima Straits at 11:10 AM on January 1 , 2018 (JST) (center
image) and the image at the right side shows the coastal area and offshore of Kanto region at 10:28 AM onth
January 6 , 2018 (JST) (right image). The reflectance of each channel, VN7, VN6, ad VN4 is assigned to
RGB.

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Introduction(2) Example of Date Use 2 – Agriculture –Data collected by GCOM-C regarding the distribution of cultivated land around the world,

crop history, and agricultural climate (solar radiation, land surface temperature, etc.) can beused to determine the growing conditions and production volume of crops. GCOM-C, forexample, can collect global crop information regarding wheat, soybeans and other produce
which Japan retains mainly through imports, and this information can be used to conduct risk

analysis and evaluations to help establish strategies to manage Japan’s food supply. The

provision of rice crop information of Asian countries to applicable organizations is also
expected to contribute to the stabilization of the global food supply.

GCOM-C is able to observe a wide range of wavelength from near-violet to thermal infrared

and tells the thermal condition of ground surface according to the observation of thermal

infrared wavelength range. The left figure of Figure 1-8 shows the image of ground surface

temperature estimated from the thermal infrared band (wavelength 10.8µm, 12.0 µm)
observed around 10:40 on August 1st, 2018. The white color areas in the image indicate

clouds. The right figure of Figure 1-8 shows the image of vegetation, and the darker the green

color, the more the vegetation. Comparing the ground surface temperature with the vegetation,
it is shown that the daytime temperature is extremely high in big cities such as Tokyo, Nagoya,

Kyoto and Osaka, and on the contrary, the daytime temperatures in forest areas are relatively
lower.

The wavelength range of thermal infrared can be observed even during nighttime as theobservation objective is the radiation (thermal release) from the earth. In addition, compared
to conventional thermal infrared observations by earth observation satellites, the capability ofobservation at high spatial resolution of 250 m and high frequency is one of the mostsignificant characteristics of GCOM-C.

Figure 1-8 Land Surface Temperature (left) and Vegetation (right) Based on SGLI Data

The land surface temperature observed around 10:40 on August 1 , 2018. The white color areas in theimage indicate clouds (edge of cloud or thin cloud may not be detected as cloud). Note that, as of August
2018, the accuracy of ground surface temperature is under verification as it is in the initial calibration
verification period before data release. The right image shows the vegetation (NDVI).
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Introduction(3) Example of Data Use 3 - Oceanographic Condition and Weather -The use of GCOM-C to collect sea surface temperature images at a high resolution of 250 m,

and the use of polarization, multi-angle, and near ultraviolet observations to obtainhigh-accuracy terrestrial aerosol data, can contribute to improving the accuracy of ocean
climate information, numerical forecasting, and yellow dust prediction models. GCOM-C canalso be used to monitor water quality around coastal and basin areas, and the collected data
can be provided to fisheries to help identify occurrences of and reduce damage caused by redtide, blue-green algae, blue tide and other phenomena.

Figure 1-9 shows the image of observation around Japan on March 29th, 2018. The left

figure shows a color composite image that the observation data of SGLI’s red (VN08: 673.5nm), green (VN05: 530 nm), and blue (VN03: 443 nm) channels are respectively assigned to

RGB so that the colors appear just as seen by human eyes. In the image, the areas with thickyellow sands appear more yellow-brown than other areas (yellow arrows in the left image of

Figure 1-9). The right figure shows a false-color composite image in which blue (VN03: 443nm), violet (VN02: 413 nm) and near-ultraviolet (VN01: 380 nm) observation data areassigned to RGB. The existence of aerosols on land and the contrast with clouds and sea

surface become clearer, and the contract between yellow sands and other type of aerosols
can be distinguished more clearly. In the right figure, it can be seen that aerosols are spreadto northern Kyushu and Chugoku areas. In addition, volcanic smoke extending to the northeast from the periphery of Sakurajima and smoke probably caused by the forest fire at the topof the image can be seen.

GCOM-C plans to quantify aerosol quantities and properties with high precision on a global

scale using a characteristic function of polarization observation in addition to manyobservation channels.

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Figure 1-9 Color Composite Images of Areas around Japan Based on SGLI data

The color composite images prepared from the observation data of March 29 , 2018. In the left figure, red(VN08: 673.5 nm), green (VN05: 30 nm), blue (VN03: 443 nm) are, and in the right figure, blue (VN03: 443nm), violet (VN02: 413 nm), near-ultraviolet (VN01: 380 nm) are assigned to RGB respectively.
th(4) Example of Data Use 4 - Volcano and Forest Fires -Not only are volcanic eruptions damaging the environment, the smoke emitted from theeruptions can also cause serious disruptions to our everyday lives such as causing flightcancellations. Forest fires also cause enormous physical damage to natural resources andman-made structures, and breathing in the smoke can have serious health effects. These
factors are turning into an important global issue. GCOM-C can be used to observe infraredand ultraviolet radiation that is unseen to the naked eye, and the infrared scanner can detect
the state of volcanic activity and the areas where forest fires are occurring, and the ultraviolet
scanner can detect smoke emitted from volcanic activity or fires. This information can be used
to reduce damage caused by volcanoes and forest fires and help to achieve greater safety
and security in our everyday lives.

On February 19th, 2018, Mt. Sinabung in northern part of Sumatra, Indonesia was erupted.

Figure 1-10 shows the image of volcanic smoke around the mountain peak captured by
GCOM-C at 250 m resolution in visible – thermal infrared wavelength band around 3:40

(world time) at the date. The top image of Figure 1-10 shows the true-color composite with
RGB wavelength bands of SGLI, and the light-brown matters around the center are
considered to be volcanic ashes. The bottom left image of Figure 1-10 shows the thermal

infrared (11 µm wavelength) brightness temperature image, and since the volcanic ashes
reaching the upper atmosphere emit infrared radiation close to the temperature at the altitude,
it is possible to estimate the level of altitude that the volcanic ashes reach by the observed
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Introduction

infrared brightness temperature. In addition, the bottom right figure of Figure 1-10 shows thebrightness temperature difference (brightness temperature of 11 µm minus that of 12 µm)

image. As the absorption band of silicate minerals consisting volcanic ashes is around 10 µm,
observation of brightness temperature difference between 11 µm and 12 µm enables todistinguish volcanic ashes from water cloud and ice cloud even during night time without
sunlight. The area of volcanic ashes shows strongly negative brightness temperature
difference indicating that there exist volcanic minerals characteristic of volcanic ashes.

Volcanic ashes spread up in the air not only disturb the operation of aircrafts but also, fromthe long-term view, affect the climate change as the sunlight is blocked. GCOM-C is expected
to observe these phenomena globally.

Figure 1-10 Volcanic Smoke of Mt. Sinabung in Sumatra, Indonesia Observed by SGLIThe top image shows the true color composite with RGB wavelength band, the bottom left shows thethermal infrared (11 µm wavelength) brightness temperature, and the bottom right shows the brightness
temperature difference (brightness temperature of 11µm minus that of 12 µm) image.

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Chapter 2 Overview of GCOM-C/SGLIGCOM-C conducts long-term and continuous global observation and data collection to

elucidate the mechanism behind fluctuations in radiation budget and carbon cycle needed tomake accurate projections regarding future temperature rise. At the same time, cooperatingwith research institutions having a climate numerical model, it contributes to reduction oferrors in temperature rise prediction derived from the climate numerical model andimprovement of accuracy of prediction of various environmental changes.

SGLI, the sensor mounted on GCOM-C, is an optical imaging radiometer that conducts

multi-band observation in the range from near-ultraviolet to thermal infrared region (380 nm to
12 μm). It realizes high resolution than GLI (Global Imager) and has a polarized observation
function and a multi-angle observation function. The polarized observation function enables toobserve the land aerosols that have been conventionally difficult to capture and improves theaccuracy of estimation of parasol effect by aerosols. In addition, the multi-angle observation
function enables to monitor the long-term fluctuation of vegetation biomass and improves themeasurement accuracy of vegetation production volume. Through those observations, while
improving the accuracy of prediction of temperature rise by the climate numerical model, theglobal forest carbon stock is monitored.
2.1 Overview of GCOM-C

GCOM-C that incorporates SGLI, a succession machine of GLI, as mission equipment

inherits observations that have been done by ADEAOS (ADvanced Earth Observing Satellite)
series of various physical amounts determining the global climate. In addition, although theland aerosols and vegetation biomass observations are essential for high-accuracy climate
numerical model, they have not been accurately observed. Thus, focusing on thoseobservations, further enhancement of observation functions is being promoted.

Figure 2-1 and Table 2-1 show the appearance and main specifications of GCOM-C

respectively. Table 2-2 shows the overview of functions of the satellite bus subsystems.

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Figure 2-1 Appearance of GCOM-C

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Table 2-1 Main Specifications of GCOM-C

Launch

H-ⅡA

Launch vehicle
Launch site

Tanegashima Space Center

Launch date

December 23, 2017

Sun-synchronous sub-recurrent Orbit （frozen orbit）
Orbit

WeightAltitude （above the equator）
Inclination

798km

98.6°

Local sun time at descending node

10:30±15min.

Launch mass

Approx. 2060kg（including the propellant）

Box shape with two solar array paddles
Shape

Life
Power generated
Mission equipment

Satellite body section

Approx. 2.5m×2.5m×4.6m

Solar array paddle （after
deployment）

Approx. 7.1m×2.1m

Design life

5 years

Probability of survival

4,000W min. （End of life）

Second-generation Global Imager
Visible and Near Infrared Radiometer
InfraRed Scanning radiometer

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GCOM-C “SHIKISAI” Data Users Handbook
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Overview of GCOM-C/SGLISatellite Bus Subsystem

Table 2-2 Satellite Bus Subsystems

Mission Data Handling Subsystem（MDHS）

Function
MDHS subsystem transmits observed data to the ground. After thedata obtained by SGLI is digitalized using the mission data coder,
it is stored in the mission data recorder (MDR) and simultaneously
converted to X-band frequency (8 GHz band) and transmitted to
the ground.

Telemetry, Tracking and Command
Subsystem (TT&C)

TT&C subsystem processes commands received from the groundand transmits satellite status data to the ground. The system
deciphers commands received from the S band frequency (2GHz)
and transmits the processed telemetry data to the ground.

Attitude and Orbit Control Subsystem(AOCS)AOCS controls the attitude and orbit of the satellite. The attitude iscontrolled by the reaction wheel assembly based on the data fromstar trackers. The orbit is controlled by using thrusters. GPS is
used to determine the position of the satellite.

Electrical Power Subsystem (EPS)EPS uses power generated by the solar array paddle subsystem
to supply power to each component and charge the batteries, andsupplies power from the charged batteries during eclipse. For
enhanced fault tolerance, EPS is equipped with an independent
two bus power system.

Solar Array Paddle Subsystem (PDL)PDL generates power for the satellite from sunlight. During orbit,
the paddle drive mechanism (PDM) rotates the solar array paddles(PAD) so that the solar panel surface is always directed towards
the sun.

Deployment Monitor Subsystem(DM)

DM is equipped with floodlights which enables the camera tomonitor the deployment and rotation of the solar array paddles
even when in eclipse.

Reaction Control Subsystem (RCS)RCS subsystem uses thrusters to generate propulsion. The
propellants are stored in the propellant tank and supplied to thethrusters during the mission.

Structure Subsystem (STR)

STR is comprised of the Mission Module equipped with the SGLI
components and star trackers and the Bus Module equipped withbus components. It is also equipped with a mechanical interface
compatible with the H-IIA launch vehicle.

Thermal Control Subsystem (TCS)

TCS subsystem controls the temperature of each satellite
component from the launch phase to the end of the mission. The
TCS offers passive thermal control through MLI, OSR and othercomponents, as well as active thermal control through heat pipes.
The heater can be turned on and off by the heater control circuit.

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2.2 Second-generation Global Imager （SGLI）

SGLI is an optical sensor with 19 spectral channels that can measure light intensity, withhigh accuracy and wide range, from near-ultraviolet to thermal infrared (380 nm to 12 μm)
radiation reflected or emitted from the earth. Using SGLI to conduct global and long-term

observations of clouds, aerosols, ocean color, vegetation, snow and ice, and othercomponents can help elucidate the mechanism behind fluctuations in radiation budget andcarbon cycle needed to make accurate projections regarding future temperature rise.

SGLI is the next generation model of Ocean Color Temperature Scanner (OCTS) on board

the Advanced Earth Observing Satellite (ADEOS), and the Global Imager (GLI) installed on

the ADEOS-II. SGLI consists of two radiometer components: Visible and Near Infrared
Radiometer (VNR) and InfraRed Scanning radiometer (IRS). Some of the improvements

made over the GLI includes its higher spatial resolution (from 1 km to 250 m), and thepolarized and its multi-angle observation capabilities for observing on-land aerosol and othercomponents. Table 2-2 shows the appearance of SGLI. Table 2-3 shows the SGLI channel

specification and Table 2-4 to Table 2-6 show SGLI sensor performance pre-launch on the
ground test. Figure 2-3 shows the relationship between geophysical variables of climatechange factors and SGLI observation wavelengths, and Figure 2-4 and Figure 2-5 show thetotal wavelength characteristics of each band. The wavelength characteristics are available at
JAXA/EORC (http://suzaku.eorc.jaxa.jp/GCOM_C/data/prelaunch/index.html).

Figure 2-2 Appearance of SGLI
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Table 2-3 SGLI Channel Specification

Table 2-4 VNR Sensor Performance
CenterWave lengthBand

VNR-NP(3telescope)

VNR-PL

VN01
VN02
VN03
VN04
VN05
VN06
VN07
VN08
VN09
VN10
VN11
PL01_+60
PL01_0
PL01_-60
PL02_+60
PL02_0
PL02_-60

379.9
412.3
443.3
490.0
529.7
566.1
672.3
672.4
763.1
867.1
867.4

Band width
nm

10.6
10.3
10.1
10.3
19.1
19.8
22.0
21.9
11.4
20.9
20.8

672.2

20.6

866.3

20.3

2-6

Signal Level

Lstd（Spec.）

Saturation
Level

W/m2/str/µm
60240-24175
305-31864
457-46753
147-15041361-364
33
95-96
23
69-70
25
213-21740351-359837-38
30
305-306
295
25
315
293
396
30
424
400

SNR
at Lstd
624-675786-826487-531858-870457-5221027-1064
988-1088
537-564
1592-1746
470-510471-511
609
707
614
646
763
752

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Table 2-5 SWIR Sensor Performance
CenterWave length

Band
SW1
SW2
SW3
SW4

SWIR

1.05
1.39
1.63
2.21

Band width
nm

21.1
20.1
195.0
50.4

Signal Level

Lstd（Spec.）

Saturation
Level

W/m2/sr/µm
57
289.2
8
118.9
3
50.6
1.9
21.7

SNR
at Lstd
951.8
347.3
100.5
378.7

Table 2-6 TIR Sensor Performance
CenterWave length

Band
TIR

T1
T2

10.78511.975

Band width
nm

756
759

Signal Level
Tstd（Spec.）

NEdT
at Tstd

K
300
300

K
0.08
0.13

Figure 2-3 Relationship between Geophysical Variables of Climate Change Factors and
SGLI Observation Wavelengths

Figure 2-4 VNR Response Function
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Figure 2-5 IRS Response Function
2.2.1 SGLI Principle of Operation

SGLI consists of two radiometer components: Visible and Near Infrared Radiometer (VNR)

and InfraRed Scanning Radiometer (IRS). Figure 2-6 shows the observation image of SGLI.
SGLI is able to conduct observation once in two days around Japan (latitude 35°). The
observation width of SGLI VNR is 1,150 km, and that of SGLI IRS is 1,400 km.

Figure 2-6 Observation Image of SGLIALOS is the observation satellite by Advanced Land Observing Satellite “Daichi.” Also, MODIS is an opticalsensor (moderate resolution imaging spectrometer) developed by NASA/GSFC and mounted on NASA’s
earth observation satellite Terra/Aqua.

The VNR is comprised of the optical unit (SRU：Scanning Radiometer Unit) and the electricalunit (ELU：Electronic Unit). The optical unit is comprised of a non-polarized light observation
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sensor (NP sub-unit: 380 nm to 868.5 nm, 11 channels) that scans areas in the nadir direction,
and a polarized light observation sensor (PL sub-unit: 673.5 nm, 868.5 nm, 2 channels) thatcan provide multi-angle observations by switching to azimuth angles ranging from +45° to-45°. The sensor’s detector employs a charge-coupled device (CCD), and is an electricalscanning method (pushbroom scanner method) radiometer that does not require mechanicalscanning.

The non-polarized (NP) observation sensor has three telescopes having 24° field of vieweach in the different directions, accordingly with a scanning width of 70° in total(approximately 1,150 km). NP sensor observes the wavelength of 11 channels extracted

inside of the sensor in the resolution of 250 m on land and in coastal areas, and in the
resolution of 1 km in oceanic regions.

The polarized (PL) sensor has two telescopes for 673.5 nm and 868.5 nm and performs
polarization observation in three angles, 0° and ±60°. PL sensor is mounted on the tilting

mechanism that can be set at any degree in the range of ±45° in forward/backward ofalong-track direction. For forward view of +45° and backward view of -45°, a scanning width ofapproximately 1,150 km can be observed in the resolution of 1 km. Figure 2-7 shows thetilting mechanism of PL observation and Figure 2-8 shows VNR SRU. The ELU has thefunction to monitor and control the SRU and then edits and outputs the detected signals.

Figure 2-7 Tilting Mechanism of PL Observation

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Figure 2-8 VNR SRU
IRS is comprised of the optical unit and the electrical unit. The optical unit divides the light reflectedfrom the earth into the spectral regions of Short Wavelength Infrared (SWI: 1.05 μm to 2.21 μm, 4channels) and Thermal Infrared (TIR: 10.8 μm, 12.0 μm, 2 channels) by a dichroic mirror via
Ritchey-Chretien type optical system and leads the light into the respective detectors. The

InGaAs detector is used for the short wavelength infrared, and the Photo Voltaic (PV)-type
HgCdTe detector is used for the thermal infrared. The InGaAs detector is cooled to -30 °C

using a Peltier element, and the HgCdTe detector is cooled to 55 K using the Stirling coolingsystem.

To avoid influence from the incident angle dependence of the scan mirror reflectivity, the IRS

scanning method employs the mechanical scanning method (whisk-broom method) in which

the scan mirror is set at an angle of 45 degrees with respect to the rotation axis. Using thisscanning method, the IRS scans the earth’s surface once every 0.74 s, and observes

approximately 80° width (1,400 km) in one scan. Figure 2-9 shows IRS SRU. In the same way
as for VNR, ELU monitors and controls SRU unit then edits and outputs the detected signals.

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Figure 2-9 IRS SRU

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2.2.2 SGLI System Configuration

The 11-channel observation wavelengths of VNR-NP sensor are extracted by positioning

spectral filter strips on the focal plane of the 11-line CCD (length of approximately 80 mm).
The spectral filter is composed of two filters to enable suppression of response at out-of-band

wavelengths. VNR-PL is observed the polarization at -60°, 0°, and 60°angles to calculate the
Stokes parameters I, Q, and U. PL’s three polarized angles are extracted by positioning

polarizing filters, composed of three polarizer strips in each angle on the focal plane of the
11-line CCD. Figure 2-10 shows the channel array in VNR-NP, and Figure 2-11 shows thearray of polarizing direction of VNR-PL.

In CCD of VNR, the pixel number of a line is 6000 each telescope. In NP, 4 pixels in CT

direction and 4 pixels in AT direction are added, that is, 16 pixels are digitally added at

on-board to make one pixel corresponding to 250 m. On the other hand, in PL, 7 pixels in CT
direction x 7 pixels in AT direction = 49 pixels are digitally added at on-board to make one
pixel corresponding to 1 km.

Figure 2-10 Channel Array in VNR-NP

Figure 2-11 Array of Polarizing Direction of VNR-PL
IRS employs mechanical scanning method using 45° mirror, and the scan mirror rotates atthe constant rate of 740 ms to leads the observing light to IRS internal. Figure 2-12 shows thedefinition of IRS scanning angle and view.

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Figure 2-12 Definition of IRS Scanning Angle and View
The number of samples is the number of DSP output samples.

2.2.3 Calibration Concept

SGLI evaluates the health check and the trend by evaluating the data obtained throughon-orbit calibration and calibration maneuver conducted periodically. Table 2-7 shows SGLI
calibration types.

Table 2-7 SGLI Calibration Types

VNR

SWIR
TIR

On-orbit calibration

Solar
diffusercalibration

Internal
lamp
calibration

Dark
image
calibration

Black
body
calibration

〇

〇

〇

〇

〇

〇

‐

‐

‐

〇：Once in 8 days◇: Each scan

Deep
space
calibration

Lunarcalibration
maneuver

‐

‐

‐

◇

◇

◇

□: Once in a month2-13

Calibration maneuver
Solar angle
correction
maneuver90-deg yaw
maneuver

□

△

△

□

△

‐

□

‐

‐

△: Once in a year

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2.2.3.1 VNR Calibration

VNR has the radiometric calibration function using solar light, internal lamps, and lunar light,

and also has an electrical calibration function. For the calibration employing the solar light andinternal lamps (white LED and infrared LED), the light reflected from the expanded diffuser isused. By facing the diffuser in the nadir direction of the non-polarized light observation sensor,
and using the tilting device to face the polarized observation sensor toward the diffuser, thereflected light from the diffuser can be received and calibrated by each sensor.

Figure 2-13 VNR Solar Diffuser and Internal Lamp Calibration
2.2.3.2 IRS Calibration

Figure 2-14 shows IRS calibration functions. IRS collects ground observation data in a scan rangeof 80° from it’s one 360° rotation of the scanning mirror, and acquires data of the calibration
sources (solar light, internal lamps, black body and deep space) from angles outside theobservation range. For the short wavelength infrared channel (SWIR), the radiometric calibrationis conducted using the solar light, internal lamp (LED with wavelength of 1.6 μm, halogen lamp)

and lunar light. In addition, for the thermal red channel, every two-point calibration is conducted foreach scanning using the data obtained by observing the black body (high temperature calibration
source) and deep space (low temperature calibration source).

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Figure 2-14 IRS Calibration Features
2.2.3.3 Calibration Maneuver

In GCOM-C, the maneuver is performed to obtain data to calibrate the sensor.

(1) Lunar Calibration Maneuver

In SGLI lunar calibration pitch maneuver, the lunar observation images captured bymaneuvering GCOM-C attitude around the pitch axis are used as radiometric calibration data
to evaluate sensor input and output properties, inter-band relative sensitivity, and dark signal
noise. The moon that reflects solar light is stable light source and is suitable as a long-term

calibration light source. Until the end of observation operation, the lunar calibration pitch
maneuver is performed approximately every 29 days that is the synodic period of the moonand sun. In addition, observation is conducted when the Phase Angle is around 7°. The

channels to be calibrated are all VNR observation channels and all IRS observation channels.
(2) Solar Angle Correction Maneuver

The earth is circling around the sun and due to the earth’s elliptical orbit and orbit inclination,

the yaw axis (solar β angle) changes gradually. The solar angle correction yaw maneuver of

SGLI calibrates the yaw axis direction in solar calibration by maneuvering the attitude of

GCOM-C around the yaw axis. The channels to be calibrated are all VNR observation
channels and IRS-SWIR channel.
(3) 90-deg Yaw Maneuver

SGLI 90-deg yaw maneuver uses the terrestrial target of homogeneous reflectance, which

captured by maneuvering GCOM-C’s attitude by about 90° around the yaw axis, as
radiometric calibration data to evaluate the PRNU (Photo Response Non-Uniformity).

Rotating the satellite around the yaw axis, maneuver is conducted so that different CCD pixels
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are able to observe almost same terrestrial target. The channels to be calibrated are all VNR
observation channels.

2.2.4 Observation Operation

The basic observation pattern of SGLI and overview of observation operation of SGLI are

respectively shown in Table 2-8 and Figure 2-15. The daytime observation is performed at all
19 channels. The resolution for land and coastal observation is 250 m or 500 m (thermal

infrared only), and the resolution for open ocean observation is 1 km. In addition, thenighttime observation is conducted in the land region of shortwave infrared channel (SWIR)
and global region of thermal infrared channel (TIR). Regarding the start up and end timing of

VNR and IRS SWIR observation in the terrestrial daytime observation, it turns ON earlier thanthe end of shade period on ground for twilight observation, and turns OFF after the start ofshade period. In VNR polarized tilt observation, the normal observation sequence is tilt
forward for north side and tilt backward for south side considering the solar position. In
addition, each sensor’s observation is turned ON and OFF independently. Note that the

aforementioned patterns are basic observation patterns, and the actual observation operation
will be planned considering the data volume to be downlinked.

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Table 2-8 SGLI Basic Observation PatternFigure 2-15 Overview of SGLI Observation Operation

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Overview of GCOM-C Ground System

Chapter 3 Overview of GCOM-C Ground System
3.1 Overall System

GCOM-C ground system consists of three systems: “satellite control system,” “mission

operation system,” and “research system.” The satellite control system mainly provides

satellite operation. The mission operation system processes and provides data. The researchsystem develops algorithms and conducts validations.

Figure 3-1 shows the overview of GCOM-C ground system. The tracking network system

uses the ground network system to receive telemetry, send commands and operate ranging.
In addition, it receives X-band data at the receiving and recording equipment of domestic andinternational stations, demodulates the observation data, and generates ASD (APID Sorted
Data). As the foreign station, Svalbard station (northern latitude of 78 degrees) offered by a
Norwegian company, Kongsberg Satellite Services AS (KSAT), is used.

The satellite control system establishes mission operation plans and satellite operation plansand processes the telemetry/commands coming through the tracking network and Svalbard
station.

The mission operation system processes the X-band observation data received at thereceiving and recording equipment of domestic and foreign stations. In the data processing,Level0, Level1 and Level2 & Level3 processing are performed using ASD as input, and data

storage control are provided. In addition, it processes near-real time products and provides
them to alliance organization.

The research system receives the Level1 and Level2 & Level3 products from the mission

operation system and verifies the algorithms and parameters for processing and correction.

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Figure 3-1 GCOM-C Ground System
3.2 Satellite Control System

Telemetry and command operation of satellite is principally performed using the groundnetwork system (GN station) of JAXA. Telemetry data of satellite are stored in a data recorderin orbit, and received at Svalbard station (KSAT) in each orbit cycle. The data is then sent tothe satellite control system at Tsukuba Space Center to check the satellite condition at theinvisible zone in orbit.

3.3 Mission Operation System

GCOM-C mission operation system consists of “GCOM-C data processing function” thatprocesses mission data downlinked from a satellite to a ground station at X-band and “GCOM

information system function” that stores and controls the processed GCOM-C data, products
and satellite operation information and transmits data inside and outside of the system. It also

provides near-real time products to alliance organization. The outlines of the data processing
system and information system are described below:(1) Data Processing Function(a) Process Control Function

The function to control GCOM-C data processing in general including development ofprocessing plans, status management, control of functions unique to the processes based on
the plans (calling out of external functions etc.), and load control of processing computer.
(b) Process Individual Function
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The function to perform processing individual to GCOM-C/SGLI (Level1 processing andLevel2 & Level3 processing) based on the processing plan provided by the processing control
function.

(2) Information System Function(a) System Control Function

The function to manage the system configuration for the entire mission operation system.
(b) Data Transmission Function

The function to provide data for inside and outside of the mission operation system.
(c) Data Storage Function

The function to store operation information related to the data and products generated by thedata processing function.

(d) System Monitoring Function

The function to monitor the performance of the mission operation system.
3.4 Receiving and Recording Equipment

The receiving and recording equipment (X-band stations) for GCOM-C operation comprise

Katsuura station, Hatoyama station, Tsukuba station, and KSAT (Svalbard station).
Katsuura station, Hatoyama station and Tsukuba station in Japan generally receive the realtime visible signals. Svalbard station receives the global observation data.

Katsuura station, the primary domestic station, is the equipment to receive and record theobservation data at domestic antenna and provides ASD to the mission operation system.
The main purpose of Katsuura Station’s regular X-band operations is the reception andtransfer of real time observation data as well as real time HK telemetry data. Hatoyama
station and Tsukuba station are positioned as a backup domestic station and serve as the

backup domestic station when the primary Katsuura Station is offline because of contention
with other satellites or for regular maintenance operations.

On the other hand, the KSAT/Svalbard station operates S-band TT&C (telemetry, command),generates the ASD of X-band observation data and HK telemetry data and transmits them to

the GCOM-C mission operation system. The main purpose of the KSAT/Svalbard Station’s
normal X-band operations is the reception and transfer of stored observation data recordedon the satellite’s data recorder as well as stored HK telemetry data. Table 3-1 summarizes thesatellite operations at each ground station.

In addition to the above, real time observation data will be distributed directly from thesatellite by X-band transmissions to ground stations (domestic and international direct

receiving stations) that require direct receiving. Transmissions to domestic and international
direct receiving stations are operated on the basis of an agreement between JAXA and thecorresponding domestic or international institution.
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Table 3-1 Ground Station Satellite Operation

StationKatsuura Station(X-band)
Hatoyama
Station/Tsukuba
Station(X-band)
KSAT/Svalbard
Station（S/X-band）
JAXA New GN
Station(S-band)

TT & C Operations (S-band)
RealCommandtime/Stored
Ranging
Telemetry－－

Mission Data Transfer Operations (X-band)
StoredStoredReal time
Real time
Telemetry
Observation
Observation
Telemetry
Data
Data
○
○
○
○
(Japan andvicinity)

－

－

○

○

○

○
(Japan andvicinity)

○*1

○

○

○

○

（○）
(North Poleand vicinity)

○

○

－

－

－

－

*1 Svalbard Station undertakes ranging operations only when necessary.

3.5 Ground System Operation

3.5.1 Processing by Mission Operation System

Observation data received at the KSAT/Svalbard Station and domestic stations (Katsuura

station, Hatoyama station and Tsukuba station) are transferred as ASD to the mission
operation system at the Tsukuba Space Center where the data undergo Level0, Level1 andLevel2 & Level3 processing. The orbit and attitude information used in Level1 processing isbased on the use of the onboard GPS receiver’s orbit and attitude values contained in thesensor packet’s payload correction data (PCD).

3.5.2 Delivering Products to Alliance Organizations

SGLI Level1 products and physical variables products (Level2) generated by the Level2 &Level3 processing, for Japan and vicinity, and for global are provided in near-real time to thealliance organization within the delivery time requirements from observation to productdelivery (near-real time processing requirements). Table 3-2 and Table 3-3 show the currently
defined near-real time products. The details of those products are provided in Chapter 4.

The number of alliance organizations and products subject to near-real time delivery may

expand in the future.

Table 3-2 Near-real time Products (Japan and Vicinity)
SGLI
Near-real time Product

Delivered Product1) Level1B
2) Sea surface temperature (Level2)
3) Chlorophyll-a concentration (Level2)
4) Normalized water-leaving radiance (Level2)
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Overview of GCOM-C Ground System

Delivered Product5) Snow and Ice cover extent (Level2)

Table 3-3 Near-real time Products (Global)

SGLI
Near-real time Product

Delivered Product1) Level1B
2) Sea surface temperature (Level2)
3) Snow and Ice cover extent (Level2)
4) Snow and ice surface temperature (Level2)5) Land surface temperature (Level2)
6) Aerosol over ocean (Level2)
7) Aerosol over land (near-ultraviolet) (Level2)
8) Aerosol over land (polarization) (Level2)

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Chapter 4 SGLI Products
4.1 Product Definitions

SGLI products including Level2 & Level3 products are in HDF5 format and consist of Group,

Dataset, and Attribute. For the definitions of HDF5 format, please refer to the HDF Group(http://www.hdfgroup.org).

4.1.1 Process Level Definitions

Table 4-1 shows the definition of processing level of SGLI products.

Processing Level

Level-0

Level-1A

Level-1B

Level-2

Table 4-1 Definition of SGLI Products
Description

Uses ASD as input. Data set after sorting the previous data set based on the timeand packet sequence count in the ASD and applying loss processing.
Granule scene radiance products after the following processing using Level-0
data as input.
・Delete duplicate packet. Interpolate lost packets in the mission data with dummy
packets
・Edit and cut out of scenes (include overlap data)
・Radiometric correction
・Geometric correction
・Append information of lost packet and quality information
Granule scene radiance products after the following processing using Level-1A
product as input.
・Radiometric correction
・Geometric correction and resampling to Level-1B reference coodinates
・Append geometric information per wavelength band and calibration information
Or low-resolution resampling products derived from high-resolution Level-1B
products.
Granule scene, global, and tile (*1) data of geophysical variables derived fromLevel-1B or Level-2 products including global and tiled mosaic products with thespatial resolutions of 250m and 1km for scene and tile products, and4km(1/24deg.) for global products.
*1: Tiles are defined as 10 degrees square at the equator on the Sinusoidal grid.

Level-2 statistics

Temporal (8-day and 1-month) statistics or cloud-free mosaic of Level-2 tile
products with the same spatial coverage and resolution.

Level-3

Spatial (1/12deg. And 1/24deg.) and temporal (1-day, 8-day and 1-month)statistics of Level-2 products with the global spatial coverage.

4.1.2 Process Flow

Figure 4-1 shows the processing flow of Level1 product and Level2 product. The left side of

Figure 4-1 is the upstream of the process, and the arrows indicate main input data of theprocess. In addition, Figure 4-2 shows the processing flow of Level2 and Level3 products.
Using Level2 products as input, Level2 statistic product and Level3 temporal statistic, spatial
statistic products are generated. The types of products are described in 4.1.3 , 4.1.4 , and
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4.1.5 . The overview of Level2 & Level3 algorithms are provided in 4.3

Figure 4-1 SGLI Level1 and Level2 Processing Flow

Figure 4-2 SGLI Level2 and Level3 Processing Flow

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4.1.3 Leve-1 Product

Level1 product is generated for each sensor of VNR-NP, VNR-PL, and IRS (SWIR + TIR). In

addition, according to the difference of processing performed to the observation data as

shown in Table 4-1, they are divided into Level1A and Level1B. For Level1B productgenerated at high resolution, Level1B product obtained by resampling it to 1 km resolution is
generated too.

4.1.3.1 Level1A Product

The brief information stored in Level1A product is shown below. Please refer to ProductFormat Description for more details.
(1) Stored Data

Observation DN value is stored for each band as image data. Please refer to 4.2.2.2 for thealgorithm to convert to radiance. The geometry is not corrected, and the observation positionof ground of pixel is varied each band. Therefore, the latitude/longitude information of 10

pixels interval in each band is appended. However, there is no interval in AT direction of IRS.
The stored geometric information is the center position of the pixel.
(2) Image Dataset Size(a) VNR-NP[telescope (Left, Nadir, Right) x line x pixel]= [3 x line*x 1500 (250 m) or 375 (1 km)]*line value varies depending on the file.

(b) VNR-PL[line x pixel]= [line* x 857 (1 km)](c) IRS-SWIR[line x pixel]= [line* x 4584 (250m) or 1146 (1 km)](d) IRS-TIR[line x pixel]= [line* x 4584 (250 m) or 2292 (500 m) or 1146 (1 km)]

4.1.3.2 Level1B Product

The brief information stored in Level1B product is shown below. Please refer to ProductFormat Description for more details.
(1) Stored Data

The top of atmosphere radiance SI (Scaled Integer) after radiometric correction is stored as
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16-bit image data. However, the upper 2 bits are the flag to identify the stray light correction
information and thus the conversion to actual radiance should be done carefully.

The geometries are projected to L1B reference coordinates (see 4.2.2.4 ) commonly for

VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as

geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10

pixels interval are stored commonly for band. On the other hand, since the precise satellite
position in observed pixels is varied depending on band, the satellite azimuth angle and thesatellite zenith angle are stored by 10 pixels interval for each band. The stored geometric
information is the center position of the pixel.

In addition, QA flag corresponding to the observation image is appended to Level1B product.

(2) Image Dataset Size(a) VNR-NP*[line x pixel]= [line* x 5000 (250 m) or 1250 (1 km)]line value varies depending on the file.

(b) VNR-PL[line x pixel]= [line* x 1000 (1 km)](c) IRS-SWIR[line x pixel]= [line* x 5000 (250 m) or 1250 (1 km)](d) IRS-TIR[line x pixel]= [line x 5000 (250 m) or 2500 (500 m) or 1250 (1 km)](3) Conversion from SI Value to Top of Atmosphere Radiance
To convert SI value to top of atmosphere radiance [ ]([ ], , ) and calculate as follows using,, , , extract the lower 14 bit of SI value

values stored to Level1B product. In

addition, because of the sensor specification, band that is saturated depending on theobserved target exists. Here, l indicates line and p indicates pixel.
[ ]

,,

=[

]

×[ ]

,,

+[

]

(4) Conversion from SI Value to Top of Atmosphere Reflectance (solar zenith angle
non-correction)

Regarding VNR and SWIR, to convert SI value to top of atmosphere reflectance [ ]extract the lower 14 bit of SI value ( [ ]
_

,

_

,,

,, ,

) and calculate as follows using

values stored to Level1B product.. The solar-earth
4-4GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

distance is corrected for [ ], , , but cos

shows the average solar irradiance
[ ]

,,

=[

_

is not corrected. In addition, Table 4-2_

of each band normalized at 1AU.

]

×[ ]

,,

+[

Table 4-2 Average Solar Irradiance F0 (at 1AU)

Band

*1

VN01
VN02
VN03
VN04
VN05
VN06
VN07
VN08
VN09

F0[W/m^2/um]*21092.1436
1712.1531
1898.3185
1938.4602

Band

*1

VN10
VN11

PL01
PL02

1850.9604

SW01

1502.5667

SW03

1797.1344
1502.3177
1245.3663

SW02
SW04

]

_

F0[W/m^2/um]*2956.2323
956.5352
1503.605

956.8333
646.5213
361.2250
237.5784

84.2413*1 For VNR-NP, the value of Nadir telescope is shown.

*2 Thuillier et al., The Solar Spectral Irradiance from 200 to 2400
nm as measured by the Solspec Spectrometer from the Atlas andEureca Missions, Solar Physics, 214 (1): 1-22, May 2003.

4.1.3.3 Level1 Product Granule ID Definition

Granule ID means the code that enables to uniquely identify the earth observation satellite

data. The file name of product is appended the extension “.h5” that indicates HDF5 file after
Granule ID. Table 4-3 shows the granule ID assignment rules for Level1 product. Table 4-4
and Table 4-5 show the granule ID setting values.

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ID

Table 4-3 Level1 Product Granule ID

Scene ID

Byte

1

2

GID

G

C 1 S G 1 _ Y Y YYM

M D D H H m m

s

P P P

S

S

_

L

L

x1

x2

_

Example G

C 1 S G 1 _ 2 0 1

1

1

1

A

0

0

6

_

1

B

S

G

_

(fix)

Month

Day

3

Hour

_

4

5

min

1

2

Path*2
Observation start UT*1

4-6

Scene
*3

_

Level
*4

Type
*5*6

_

35

36

37

38

39 40 41

K

K

K

m

r

_

a

p

p

p

I

R

S

N

K

_

z

0

0

1

parametersubsystem*7

resolution*9

(fix)

Year

2

34

mode*8er

Sensor3

sec

Paramet Satellite

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

1

33

algorithm ver.*10

3 4

Product ID

_

ver.
*11

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Table 4-4 Level1 Product Granule ID Setting Value

No.
GID
*1.
YYYYMMDD
HHmms

Item
Observation
start UT

*2.
*3.
*4.

PPP
SS
LL

Path
Scene
Level

*5.
*6.

x1
x2

Type
Type

*7.

KKK

subsystem

*8.

m

mode

*9.

r

resolution*10. aalgorithm ver.

*11. ppp

parameterver.

Details
Set the ideal start time of product. As there is someoverlap before and after product, it does not matchwith the time of product start line (UTC time)
The number of seconds is expressed by single
alphabet.

Set the path number. Setting value: 1 to 485
Set the scene number. Setting value: 1 to 24
Set the processing level.
Case of L1A: 1A
Case of L1B/L1B (Low resolution resampling) : 1B
Fix to S (standard product)
Set the processing type for each operation.
Case of standard processing (global): G
Case of near-real time processing (around Japan): L
Case of near-real time processing (global): N
Set the type of observation data.
Case of VNR-NP: VNR
Case of VNR-PL: POL
Case of IRS (SWIR+TIR): IRS
Set the type of daytime/nighttime observation.
[Standard product]Case of daytime observation (Day): D
Case of nighttime observation (Night): N[Calibration product]
Solar calibration: S
Internal lamp calibration: L
Electrical calibration: E
Maneuver: M
Set the resolution.
K: 1000 m
L: 1000 m (Low resolution resampling )Q：250 mCase of IRS, there additional exist four types: H, Y,
X, and M. See Table 4-6.
Set the algorithm version.
Setting value: 0 to 9, A t Z.
Set the parameter version.
Setting value: 000～999.

4-7

Note

Table 4-5
See 4.1.3.3 (1)See (2)－－
See 4.1.3.3 (3)－To link to VNR
sensor’s ON/OFF, it
may not match withthe ground surface
status(Daytime/Nighttime) incalibration mode or
irregular observation.
See 4.1.3.3 (4).
See 4.1.3.3 (5)－－GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

Table 4-5 Symbols for Seconds in Level1 Product Granule ID
Symbol for number
Number of seconds
of seconds
A

00≦sec＜03C

06≦sec＜09E

12≦sec＜15B03≦sec＜06D09≦sec＜12F

15≦sec＜18G18≦sec＜21H21≦sec＜24J24≦sec＜27

L30≦sec＜33N36≦sec＜39K27≦sec＜30M33≦sec＜36P39≦sec＜42Q42≦sec＜45R45≦sec＜48T51≦sec＜54

S

48≦sec＜51U54≦sec＜57W60≦sec＜61V57≦sec＜60(1) Path Number

The path is defined as the satellite passes from the ascending node to the North pole, the Southpole and then reaches the ascending node again, and the path number increases toward thewest at the adjacent orbit interval. The recurrent period of GCOM-C is 34 days, and the numberof orbits is 485. Figure 4-3 to Figure 4-5 show the orbit of path per 30.

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Figure 4-3 GCOM-C Path Definition (Path 1 to 151)

Figure 4-4 GCOM-C Path Definition (Path 181 to 331)

Figure 4-5 GCOM-C Path Definition (Path361 to 481)
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GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products(2) Scene Number

The scene is defined the unit of Level1 processing. In VNR-NP and IRS, a scene is therange of 1/24 of the orbit. After passing the ascending node, the scene with the earliest
observation time is numbered 01, and the scene number is increased by 1 for each of thefollowing scenes in sequence. When a scene is divided into multiple product, since those

products are originally one scene, multiple product with the same scene number with different
observation times are generated.

In VNR-PL, a scene is defined as the range including the whole daytime observation region

within one orbit. Therefore, the scene number is fixed to 00.
(3) Standard/Near-real time Processing

The near-real time product is generated placing the highest priority to the compliance to thedelivery time requirement, and therefore, it may be different from the range of scene
generation in standard processing. The lists of near-real time products are shown in 3.5.2(4) Daytime/Nighttime

In basic observation pattern, SGLI keeps observing the data of VNR and IRS in daytime. Innighttime, VNR observation is stopped, and SWIR of IRS is switched ON/OFF depending on
the observing regions. However, TIR of IRS keeps observing regardless of day/night.

During the period that observation is stopped, no product is generated in principle. However,in cases such as that either one of TIR or SWIR is stopped, the observation suspension isshort, or the timing of observation start is slightly off between VNR and IRS, the missing value
is stored to the corresponding data within a product.

The basic concept of SGLI operation during daytime and nighttime is provided in 2.2.4(5) Spatial Resolution

Since SGLI observes by switching the spatial resolution, the resolution identifications in

granule ID are shown in Table 4-6. In particular, SWIR and TIR data are stored in one productfor IRS, the ID identification is varied depending on the combinations of the spatial resolution.

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Product Type

Table 4-6 Spatial Resolution Identification

VNR-NP
Product

VNR-PL
Product

Resolution
SymbolResolution

Resolution

K

1 km

1 km

H

―

―

Y
X
M

―
―
―

―
―
―

Q

250 m

―

SW01,02,04
Resolution
1 km
1 km
1 km
1 km
1 km
1 km
1 km
1 km
1 km

L

1 km
(Low resolution
resampling)

―

1 km
1 km

OFF
OFF

OFF

IRS
Product
SW03
Resolution
1 km
1 km
1 km
1 km
250 m
250 m
250 m

250 m
1 km
(Low resolution
resampling)
1 km(Low resolution
resampling)
1 km

TIR
Resolution
1 km
1 km
OFF
500 m
500 m
250 m
1 km
500 m
250 m
250 m
OFF
1 km
(Low resolution
resampling)
1 km
1 km
(Low resolution
resampling)

4.1.4 Level2 Product

The Level 2 product is processed using Level1B and Level2 product of upstream of theprocessing flow as the input and are stored the geophysical variables. The types of Level2
product are shown in Table 4-7 and Table 4-8, and the types of statistic products of Level2
products are shown in Table 4-9.

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Table 4-7 List of Level2 Product (1/2)
Level2

Ocean

Cryosphere

Land

Parameter
Normalized water leaving radiance
Photosynthetically available radiation
Atmosphere correction parameter
Chlorophyll-a concentration
Total suspended matter concentration
Colored dissolved organic matterSea surface temperature
Okhotsk sea-ice distribution
Snow and Ice cover extent
Snow surface properties
Top of atmosphere radianceLand atmospheric corrected reflectance
Vegetation index
Leaf area index
Fraction of absorbed PAR
Above-ground biomass
Vegetation roughness index
Land surface temperature

Temporal Statistics
01D ： 1 day
08D ： 8 days
01M ： 1 month

Generation Unit

Resolution
K ： 1 kmH ： 500 m
Q ： 250 m
F ： 1/24 deg
C ： 1/12 deg

Product ID

Scene

-

NWLR

K

Q

Scene

-

IWPR

K

Q

Scene
Scene
Tile
Tile
Tile
Tile
Tile
Tile

01D
01D
01D
01D
01D

SST
OKID
SICE
SIPR
LTOA
RSRF
VGI_

K
K
K
K
K
Q
Q

H
Q
Q
Q
Q

01D

AGB_

Q

Tile
Tile

01D
01D

4-12

LAI_

LST_

Q

Q

Q

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Table 4-8 List of Level2 Product (2/2)
Level2

Atmosphere

Atmosphere(Global)Parameter
Cloud flag
Cloud properties
Aerosol by Non-Polarization
Aerosol by Polarization
Top of atmosphere radiance (global)
Top of atmosphere radiance with clear pixels (global)
Cloud flag (global)
Cloud properties (global)
Aerosol by Non-Polarization (global)
Aerosol by Polarization (global)

Generation Unit

Temporal Statistics
01D ： 1 day
08D ： 8 days
01M ： 1 month

Product ID

Tile
Tile
Tile
Tile
GlobalGlobalGlobalGlobalGlobalGlobal

01D
01D
01D
01D
01D
01D
01D
01D
01D
01D

CLFG
CLPR
ARNP
ARPL
LTOA
LCLR
CLFG
CLPR
ARNP
ARPL

4-13

K
K
K
K
F
F
F
F
F
F

Resolution
K ： 1 kmH ： 500 m
Q ： 250 m
F ： 1/24 deg
C ： 1/12 deg
Q

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Table 4-9 List of Level2 Statistical Product
Level2
Statistics

Cryosphere(Statistics)

Parameter
Snow and Ice cover extent (statistic)
Snow and ice surface temperature (statistic)
Snow grain size of shallow layer (statistic)
Top of atmosphere radiance (statistic)

Generation
Unit
Tile
Tile
Tile
Tile
Tile

Land atmospheric corrected reflectance (statistic)Land(Statistics)

Tile
Normalized vegetation index (statistic)
Tile
Enhanced vegetation index (statistic)
Tile
Shadow index (statistic)
Leaf area index (statistic)
Tile
Tile
Fraction of absorbed PAR (statistic)
Tile
Above-ground biomass (statistic)
Tile
Vegetation roughness index (statistic)
Tile
Land surface temperature (statistic)*1
RVxx ： VNR-NP Band xx Reflectance*2
RSxx ： IRS-SWIR Band xx Reflectance*3
RTxx ： IRS-TIR Band xx Reflectance*4
RPxx ： VNR-PL Band xx Reflectance*5
RNxx ： Reflectance of VNR-NP Band xx co-registered for VNR-PL*6
GEO ： Geometry(V：VNR-NP, I：IRS, P：VNR-PL)

TemporalStatistics
01D ： 1 day
08D ： 8 days
01M ： 1
month

08D
08D
08D
08D

01M
01M
01M
01M

Product ID
SICE
SIPR
SGSL
LTOA
RV01

RV02

RV03

RV04

RV05

RV06

RV07

RV08

RV09

RV10

RV11

RS01

*1

08D

01M

*1

RS02
*2

GEOI
08D
08D
08D
08D
08D
08D
08D
08D

4-14

01M
01M
01M
01M
01M
01M
01M
01M

Resolution
K ： 1 kmH ： 500 m
Q ： 250 m
F ： 1/24 deg
C ： 1/12 deg

*6

NDVI
EVI_
SDI_
LAI_
FPAR
AGB_
VRI_
LST_

*1
*1

RS03
*2

RN08
*5

*1
*1

RS04
*2

RN11
*5

*1
*1

RT01
*2

RP01
*4

*1
*1

RT02
*2

RP02
*4

K
K
K
Q

*1

Q

*2

Q

*6

Q

*6

Q

GEOV
GEOP

Q
Q
Q
Q
Q
Q
Q
Q

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

4.1.4.1 Level2 Product Generation Unit

As shown in Table 4-7 and Table 4-8, the generation unit of Level2 product is scene, tile, or
global. The scene product definitions are same as Level1 product shown in 4.1.3.3 (2).

The product storing the global range by EQA (sinusoidal equal area) projection shown in

Figure 4-6 is defined as a global product and each region divided by grids is defined as a tile.
The tile and global products are generated in the satellite’s direction of the ascending anddescending respectively. The latitude/longitude to each pixel of the tile can be calculated as
below:For the tile whose latitude/longitude (,) is to be calculated, the pixel number of the pixel

in vertical direction (top => bottom) is defined asright) is defined as, and that in horizontal direction (left =>. The pixel number of the upper left pixel within a tile is defined as (0,0).

e.g.) Case of resolution 250 m [Image size 4800 x 4800 pixel, tile number v05h29]= 4800 : Number of pixels in vertical direction in a tile (corresponds to 250 m)= 4800 : Number of pixels in horizontal direction in a tile (corresponds to 250 m)

・= 5 : Tile number in vertical direction・・・ℎ= 29 : Tile number in horizontal direction= 18 : Total number of tiles in vertical direction= 36 : Total number of tiles in horizontal direction

・
・ℎ
・
・

: Total number of pixels from south pole to north pole

: Total number of pixels in east-west direction at the equator= 180.0/= 180.0/
=2×
=

=

+

/

[

/

]

（from S-pole to N-pole）[180.0/ ] （from 180W to 180E）

+ ℎ

×

×

The latitude/longitude (,) are as follows:= 90.0 −== 360.0/

×

[

4-15

×

+ 0.5 ×−]/2 + 0.5

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Figure 4-6 EQA (sinusoidal equal area) projection
4.1.4.2 Level2 Product Granule ID Definition

Table 4-10 and Table 4-11 show the granule ID assignment rule for Level2 product (scene)and Level2 product (tile and global), respectively. In addition, the setting values are shown in
Table 4-12 and Table 4-13 respectively.

Near-resl time Level2 product (tile) name is defined by adding sequence value to granule ID(Granule ID_nnn.h5, nnn is three digit integer). Multiple products with the same granule ID are

created in the case of observing the same tile more than once a day. Sequence value isassigned in the order observed from 000 and it is possible to distinguish these products.

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SGLI Products

ID

Table 4-10 Level2 Product (Scene) Granule ID

Scene ID

Byte

1

2

3 4

5

GID

G C 1 S G 1 _ Y Y Y

6 7 8 9 10 11 12 13 114 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Example G C 1 S G 1 _ 2 0

(fix)

D

D H H m m

s

P

P

P

S

S

_

L

L x1 x2

_

K

K

K

K

r

_

a

p

p

p

1

1

3

5

6

0

1

2

0

6

_

L

2

_

S

S

T

D

K

_

1

0

0

1

min

sec

1

Year
_

1

Month

Day

2

3

4

Hour

Path

Scene

*1

Observation start UT*1

Level

_

*1

S

*2

G

Type
*1

_

Subsystem*3_

parameteralgorithm ver.*1(fix)

Sensor1

Y M M

Resolution*1

Parameter

Satellite

Product ID

ver.
*1

Table 4-11 Level2 Product (Tile/Global) and Level3 Product Granule ID

ID

Scene ID

Product ID1

GID

G C 1 S G 1 _ Y Y Y Y M M

D

D

m

t

t

t

_

g

A A

A

A

_

L

L x1 x2 _

K

K

K

K

r

_

a

p

p

p

G C 1 S G 1 _ 2 0 1

1

3

D

0

1

D

_

A

0

0

0

_

L

2

C

L

F

G

Q

_

1

0

0

1

Mapping*4

Area tile no.

Example(fix)

Sensor
(fix)

Year

1

1

Month

Day

_

Process

Observation start UT*1

time unit
*3

_

4-17

0

*5

_

Level
*6

S

G

Type
*7*8

_

_

Subsystem*9_parameteralgorithm ver.*11

Satellite

1

A/D*2

Parameter

2 3 4 5 6 7 8 9 10 11 12 13 114 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Resolution*10

Byte

ver.
*12

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

No.
*1.
*2.
*3.

Table 4-12 Level2 Product (Scene) Granule ID Setting Value

GID
See Table 4-4
Definition
LL
KKKK

Parameter

Description

Levelsubsystem

Set the processing level: L2
Set the product ID.

Level1

Remarks－
See Table 4-7 forproduct ID

Table 4-13 Level2 Product (Tile/Global) and Level 3 Product Granule ID Setting Value

No.
*1.

GID
YYYYMMDD

Parameter
Observation start UT

*2.

m

A/D

*3.

ttt

Process time unit

*4.

g

Mapping*5.

AAAA

Area tile no.

*6.

LL

Level

*7.
*8.

x1
x2

Type
Type

*9.

KKKK

subsystem*10.

rresolution

*11.

a

algorithm ver.

Description
Set the observation start time.
UTC time system, round down to minute.
Set the satellite’s direction.
Ascending:A
Descending:D
Period of statistic data
1 day
: 01D
8 days: 08D
1 month : 01M
Map projection
EQA (1- dimension) : X
EQA
:A
EQR
:D
PS-N
:N
PS-S
:S
Tile
:T
Tile number
vvhh
vv: Vertical direction number (00 to 17)
hh: Horizontal direction number (00 to 35)
Set the processing level
L2: L2
L3Bin statistic：3B
L3Map
: 3M
Fixed to S (standard product).
Set the type of processing for each
operation.
Case of standard processing (global): G
Case of near-real time processing (around
Japan): L
Case of near-real time processing (global):N
Set the product ID.

Set the resolution.
Case of 1000 m: K
Case of 250 m: Q
Case of 1/24 deg: F
Case of 1/12 deg: C
Set the algorithm version.
Setting value: 0 to 9, A to Z.
4-18

Remarks
－

－

－

－

－

－

－
－

See Table 4-7,
Table 4-8, andTable 4-9 forproduct ID.
－－

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products
*12.

ppp

parameter ver.

Set the parameter version.
Setting value: 000 to 999.

－4.1.5 Level3 Product

The Level3 product uses Level2 products as input and calculates the temporal statistics andspatial statistics of various geophysical variables. Table 4-14 shows the types of L3 products.

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GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Table 4-14 List of Level3 Product (1/2)
Parameter
Normalized water leaving radiance
Ocean
(L3)

Cryosphere(L3)

Photosynthetically Available
radiation
Atmospheric correction parameter
Chlorophyll-a concentration
Total suspended matter
concentration
Colored dissolved organic matterSea surface temperature
Snow and Ice cover extent

Snow and ice surface Temperature
Snow grain size of shallow layer

Generation
Unit

TemporalStatistics
01D ： 1 day
08D ： 8 days
01M ： 1 month

Projection
3B ： EQA
3M ： EQR
PS ： Polar
StereoProduct ID
L380
L670

L412

T670
CHLA

T865

Global

01D

08D

01M

3B

3M

Global

01D

08D

01M

3B

3M

PAR_

01D

08D

01M

3B

3M

TSM_

GlobalGlobalGlobalGlobalGlobalGlobal/PolarregionGlobal/PolarregionGlobal/PolarregionGlobal

01D
01D
01D
01D

01D

08D
08D
08D
08D
08D

01M
01M
01M
01M
01M

3B
3B
3B
3B
3B

3M
3M
3M
3M
3M

PS

Land
(L3)

L565

F

F
F
F

SICE

F

01D

08D

01M

3B

3M

PS

SIST

F

01D

08D

01M

3B

3M

PS

SGSL

F

RV01

RV02

RV03

RV04

RV05

RV06

RV07

RV08

RV09

RV10

RV11

RS01

*1

01D

08D

01M

3B

3M

RS02
*2

GEOI
*6
*7

SNZI
GlobalGlobal

L530

F
F

SNZV

Normalized vegetation index
Enhanced vegetation index

L490

CDOM
SST_

*1

Land atmospheric correctedreflectance

L443

Resolution
F ： 1/24
deg
C ： 1/12
deg

01D
01D

08D
08D

01M
01M

4-20

3B
3B

3M
3M

*7

NDVI
EVI_

*1
*1

RS03
*2

RN08
*5

SLZV
*8

SLZI
*8

*1
*1

RS04
*2

RN11
*5

RLAV
*9

RLAI
*9

*1
*1

RT01
*3

RP01
*4

SNZP
*7

*1
*1

RT02
*3

RP02
*4

SLZP
*8

*1
*2

GEOV
*6

GEOP
*6

F

RLAP
*9

F
F

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

Table 4-15 List of Level3 Product (2/2)

Shadow index
Leaf area index
Fraction of absorbed PAR
Above-ground biomass
Vegetation roughness index
Land surface temperature

GlobalGlobalGlobalGlobalGlobalGlobalTemporalStatistics
01D ： 1 day
08D ： 8 days
01M ： 1 month
01D 08D 01M
01D 08D 01M
01D 08D 01M
01D 08D 01M
01D 08D 01M
01D 08D 01M

Classified cloud fraction

Global

01D

08D

01M

3B

3M

Cloud top temperature
Global01D
Cloud top height
Global01D
Water cloud optical thickness
Global01D
Water cloud effective radius
Global01D
Ice cloud optical thickness
Global01D
Aerosol optical thickness over ocean Global
01D
Atmosphere
Aerosol
opticalthicknessoverland
Global(L3)
01D(near ultraviolet)
Aerosol angstrom index over ocean
Global01D
Aerosol angstrom index over land
Global01D(near ultraviolet)
Aerosol optical thickness over land
Global01D(polarized)
Aerosol angstrom index over land
Global01D(polarized)
Single scattering albedo over land
Global01D*1 *6- ： Same as Table 4-9*7
SNZ ： Sensor Zenith Angle (V：VNR-NP, I：IRS, P：VNR-PL)*8
SLZ ： Solar Zenith Angle (V：VNR-NP, I：IRS, P：VNR-PL)*9
RLA ： Relative Azimuth Angle (V：VNR-NP, I：IRS, P：VNR-PL)

08D
08D
08D
08D
08D
08D

01M
01M
01M
01M
01M
01M

3B
3B
3B
3B
3B
3B

3M
3M
3M
3M
3M
3M

SDI_
LAI_
FPAR
AGB_
VRI_
LST_
CFR1
CFR7
CFRL
CLTT
CLTH
COTW
CERW
COTI
AOTO

08D

01M

3B

3M

AAEO

Parameter

Land
(L3)

Generation
Unit

08D
08D

01M
01M

Projection
3B ： EQA
3M ： EQR
PS ： Polar
Stereo
3B 3M
3B 3M
3B 3M
3B 3M
3B 3M
3B 3M

3B
3B

3M
3M

Product ID

AOTL
AAEL

CFR2
CFR8

CFR3
CFR9

CFR4
CFRA

CFR5
CFRH

CFR6
CFRM

Resolution
F ： 1/24
deg
C ： 1/12
deg
F
F
F
F
F
F
C
C
C
C
C
C
C
C
C
C

08D

01M

3B

3M

AOTP

C

08D

01M

3B

3M

AAEP

C

08D

01M

4-21

3B

3M

ASSA

C

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

4.1.5.1 Level3 Product Generation Unit

As shown in Table 4-14 and Table 4-15, the Level3 products is projected to EQA and EQR(equirectangular), or PS (polar stereographic). Figure 4-7 shows the definition of EQA
projection (1-dimension), Figure 4-8 shows the definition of EQR projection, and Figure 4-9
shows the definition of PS projection.

Figure 4-7 Definition of Level3 Product EQA projection (1-Dimension)

Figure 4-8 Definition of Level3 Product EQR projection

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GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Figure 4-9 Definition of Level3 Product PS projection
4.1.5.2 Level3 Product Granule ID Definition

The definition of Level3 product granule ID is shown in Table 4-11, and the product ID isshown in Table 4-14 and Table 4-15.
4.2 Level1 Processing Algorithms
4.2.1 Precondition

SGLI observes four data shown in Table 4-16 using each sensor.

No.

Sensor1.

VNR-NP2.

VNR-PL3.
4.

IRS SWIRIRS TIR

Table 4-16 Brief Overview of SGLI Sensor

Number of
Channels
11 ch

2 ch
(3 polarizers)
4 ch
2 ch

Description of Observation Data

Observation of the strength of lights reflected or absorbed byatmosphere and land surface by NP sensor (non-polarized).
Observation of the polarization status of lights reflected or absorbed
by atmosphere and land surface by PL sensor (polarized).

Observation by SWIR (Short wavelength Infrared) of IRS.
Observation by TIR (Thermal Infrared) of IRS.

The brief overview of Level1 processing is provided in Table 4-17, and the processing flow ofeach sensor is shown in Figure 4-9 to Figure 4-13. Please refer to ATBD (Algorithm

Theoretical Basis Documents) and Level1 Implementation Processing Document for moredetails of each process.

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GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

No.
1
2
3
4

Process

Table 4-17 Brief Overview of Level1 Process
Brief Description

Preprocessing
Radiometric information
calculation
Radiometric correction
Conversion to Level1B
reference coordinate

5

Level1A geometry information
calculation

6

Stray light correction

7

Stripe noise correction

8
9
10
11

No.

1

Re-replacement of radianceto Level1B reference
coordinate

・Depacketing from RAW data
・Engineering value conversion
・Data check・Check for loss
・Calculation of geometric information

・Calculation of radiometric parameter (Gain/Offset)
・Calculation of radiance (radiometric correction)・Calculation of conversion coefficient to convert toLevel1B reference coordinate
・Calculation of observation time of L1A line
・Calculation of latitude/longitude information of eachpixel (WGS84 coordinate system)
・Stray light correction
・Stray light flag calculation
・If there is any stripe noise or radiometric gap
between telescope of VNR-NP, remove such stripe.

・Replacement of radiance after stray light correction
process to Level1B reference coordinate.

Second Stripe noisecorrection

・Removal of stripe noise that remained afterradiometric and No. 7 corrections.

Land-water flag calculation

・Calculation of land-water ratio of footprint of eachpixel.

Stokes vector calculation
Processing

Resampling to low resolution
product

・Conversion to stokes vector.
Brief Description

・Resampling of high resolution (250 m or 500 m)
product to low resolution (1 km) productFigure 4-10 VNR-NP Level1 Processing Flow
4-24

Remarks

－

－
－
－

－
IRS-TIR
excluded－－
Implement after
launch asneeded.
VNR-PL only－
Remarks
VNR-PL
excluded

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

Figure 4-11 VNR-PL Level1 Processing Flow

Figure 4-12 IRS-SWIR Level1 Processing Flow

4-25

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Figure 4-13 IRS-TIR Level1 Processing Flow

4.2.2 Level1 Processing
4.2.2.1 Preprocessing

Input in Level1A processing of normal processing is ASD or Level0 data (mission data,real-time PCD telemetry) file. Preprocessing consists of multiple processes. Preprocessing
inputs the orbit and attitude data to calculate geometry information, corrects the observation

time in each scene and distinguishes the calibration product. Table 4-18 shows the overview
of preprocessing.

In case of re-processing, instead of ASD data or Level0 data, the data equivalent to ASD(Level0 data) is extracted from the Level1A product to conduct the preprocessing.

4-26

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Table 4-18 Overview of PreprocessingNo.
1

Process
ASD input

2

Observation time
calculation

3

Calibration productdiscrimination

4
5

PCD information
extraction
ASD information
extraction

6

Mission data productconversion

7

Scene trimming

9

Attitude data
calculation

10

Geometry information
calculation

8

Orbit data calculation

Overview of Process
Obtains mission data packet or real-time PCD packet from ASD file or Level0
data file and performs the following processing.
・Align packets and delete duplicate
Corrects or converts the time included in the mission data packet or real-time
PCD packet obtained by ASD input process as follows.
・Convert GPS time to TAI93 time system
・Correction of DMS time・Calculation of observation timeDistinguishes the observation data from the calibration data from the mission
data packet obtained by ASD input process.

Extracts converted PCD data (orbit/ attitude information) from the real-time PCD
packet.
Extracts the data stored in Level1A product in re-processing, and restores thepacket of mission data or real-time PCD auxiliary data.

Puts the mission packet data obtained by ASD input process into the format ofeach scan and extracts necessary information.
・Checks the observation data packet for loss, and generates packet lost
information.
・Interpolates the lost observation time of scan.
・Extracts and calculates various information included in mission (observation)
data packet and stores it in Raw_data, Ancillary_data of Level1 product.
・Interpolates tilt angle.
Trims data at specified scene start time or scene end time.
Calculates the satellite position and velocity that corresponds to attitude
determination time.
Calculates the attitude data of attitude determination time.

Calculates following values using geometry information and earth orientation
parameter.
・Solar azimuth angle in satellite coordinate system
・Solar zenith angle in satellite coordinate system
・Lunar azimuth angle in satellite coordinate system
・Lunar zenith angle in satellite coordinate system
・The angle between lunar direction vector and satellite Y-axis (IRS only)

4-27

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

4.2.2.2 Radiometric Information Calculation

The radiometric information is calculated for each sensor. Figure 4-14 shows the schematic

diagrams of VNR-NP, VNR-PL, and IRS-SWIR processing (left) and IRS-TIR processing(right).

Start

Start

Telemetry (e.g. temperature) check

Telemetry (e.g. temperature) check

Gain information calculation

Calculation of average deep spaceobservation countOffset information calculation

Calculation of average black bodyobservation countFinishFinishFigure 4-14 Radiometric Information Calculation: (left) NP/PL/SWIR, (right) TIR(1) VNR-NP, PL(a) Telemetry Check

Limit Check is performed to ASP (Analog Signal Processor) temperature and CCD

temperature converted to count values, and the lines judged to be abnormal are not used for,processing. The temperatures () in normal lines are used to calculate thetemperature-dependent radiometric parameters.
(b) Gain Information Calculation
The total gainis calculated for each telescope/band/pixel using the base gain

characterized by pre-launch ground test, electrical shutter correction coefficient ∆temperature-dependency correction coefficient ∆
[

]

,

, , ,

=[

]

,

, ,

, and gain aging parameter ∆

] ,

・[

, , ,

・

,

, ,

・

.
,

,

, ,

Here, k is telescope, ch is band, r is resolution, s is scan, p is pixel, and poly is polynomial
order. Those parameters are updated according to the on-orbit calibration result.
(c) Offset Information Calculation

VNR sensors are performed multiple offset corrections. VNR sensors have pre/post pixels

for offset correction. The pre/post Optical Black (OB) pixels near the observation pixels areshielded and structurally unexposed, and Scan pixels outside of OB structurally have only

CCD without photodiode (PD). The sequence of pixel is shown in Table 4-19 and Table 4-20.
4-28

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Using these Scan/OB pixels, Scan/OB offset correction value

is calculated line byline.

VNR-NP

Blank

pixel

1-20

VNR-PL
pixel

Table 4-19 Sequence of Pixels for VNR-NP Observation

pre Scan pre OB
21-37

dummy

38-45

46

OBS pixel【1500 pixel (250 m)】

dummy【375 pixel (1 km)】post OB pre Scan

Blank

48-55

73-92

47

56-91

Table 4-20 Sequence of Pixels for VNR-PL Observation

pre Scanpre OB

1-34

dummy

35-44

OBS pixel【857 pixel (1 km)】

45-46

dummy

post OB

47-48

pre Scan

49-58

59-92

As the other offset correction, Dark Signal Non-Uniformity (DSNU) correction is performedfor variation in pixel direction.

is held as a table and is updated according to

the result of on-orbit calibration. The total offset value,(2) IRS-SWIR

, , ,

=

/

,

, ,

is calculated as below.

+

,

, ,

+

,

, ,

(a) Telemetry Check

Limit Check is performed SWIR focal plane temperature, ASP temperature, and pre-amplifier

temperature, and the scans judged to be abnormal are not used for processing. The

temperatures in normal lines are used to calculate the temperature-dependent radiometricparameters.

(b) Gain Calculation
The total gainis calculated using the base gain

characterized by pre-launch

ground test, temperature-dependent correction coefficient ∆∆, and gain aging parameter. The temperature-dependent correction coefficient ∆that depend respectively on detector temperature
pre-amplifier temperature
[

]

, ,,

=[

]

, ,

・

consist of gain functions, ASP temperature

, ,

・

, and

, ,

Here, ch is band, r is resolution, s is scan, l is line, and poly is polynomial order. Thoseparameters are updated according to the on-orbit calibration result. The relationship between
a line and pixel in IRS is shown in Figure 4-15.

4-29

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products

Figure 4-15 Relationship between IRS Lines and Pixels(c) Offset Information Calculation

Using the deep space data observed per scan, the offset value

deep space datais calculated. The

is averaged over several pre/post scans to remove noises. ∆is the correction parameter.

, , ,

=[

]

, , ,

+ ∆

, ,

(d) Non-linearity Correction

Due to the leak current affected by ASP temperature, non-linearity correction is performed to

DN value.

_, , ,,

_

, , ,

=[

]

=

, , ,,
, , ,

−[

]

−

, ,
, ,

(3) IRS-TIR(a) Telemetry Check

The average temperature [] is calculated for the black body temperature. In addition,

limit check is performed on the temperature around black body, and considering the view
factor analysis of calibration black body, weighted temperature [] is calculated.

(b) Calculation of Average Deep Space Count

Using the deep space data observed per scan, the offset value

deep space data
[

]

,

, ,

is calculated. The

is averaged over several pre/post scans to remove noises.

is the correction parameter for average deep space observation count value.
_

,

, , ,

=[

]

,

, , ,

+[

]

,

, ,

Here, tm is TDI mode, ch is band, r is resolution, s is scan and l is line.
4-30

GCOM-C “SHIKISAI” Data Users Handbook
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SGLI Products(c) Calculation of Average Black Body Count

Similar to the deep space data, the black body data

scans to remove noises. [observation count.

]

_

,

,

, ,

is the correction parameter for average black body

=[

, , ,

is averaged over several pre/post

]

,

, , ,

+[

]

,

, ,

4.2.2.3 Radiometric correction

Using the radiometric information obtained in 4.2.2.2 the spectral radiance is calculated.

(1) VNR-NP, PL

The spectral radiance [ ] ,count [

is calculated from the following formula using the observed

, , ,

] ,

, , ,

and the calculated gain and offset.

[ ] ,

, , ,

=

[

]

(2) IRS-SWIR

The spectral radiance [ ]

count

_

[

]

, , ,,, , ,,

,

⋅ [

, , ,

] ,

, , ,

−

,

, , ,

is calculated from the following formula using the observed, , ,,and the calculated gain and offset.
[

=

]

, ,,

⋅

_

, , ,,−_(3) IRS-TIR

The non-linearity of TIR is corrected by offset adjustment function [ ]

[

′]

[
[

′]
′]

, ℎ, ,, ℎ, ,

=[

]

=[

_ ]

, ℎ, , = [_ ], ℎ, ,, ℎ, ,−[ ]−[ ], ℎ, , − [ ]

,

, , ,

, , .

, ℎ, ,, ℎ, ,, ℎ, ,

TIR spectral radiance is calculated by the following formula using the nonlinearity correction

and two-point calibration formula.
[],indicates the black body radiation factor. B ,is the Planck function oftemperature T [K].
=

∑

∑

[ ′]

[ ′]

∙ [

,

, ,

,

, ,

]

[

⋅

⋅

,

∙

[

′]

′]

,

, ,

,

, ,

− [

− [

+ 273.15 + [ ]

=

,

4-31

′]

∙

′]

,

, ,

,

, ,

+ 273.15

GCOM-C “SHIKISAI” Data Users Handbook
First Edition
SGLI Products

=
λ,
Here,

emission

=

2ℎ

∙

1

′ is non-linearity correction coefficient and−1
is effective emissivity of background4.2.2.4 Conversion to Level1B reference coordinate(1) Calculation of Latitude and Longitude

Figure 4-16 shows the outline of calculation of latitude and longitude of observation points.

Figure 4-16 Processing Flow of Calculation of Latitude and Longitude(a) View Vectors Calculation and Conversion

The view vectors of each pixel in the base coordinate of radiometer are calculated using thegeometry function_

given to each sensor. Since VNR (NP, PL) optical systems have awide view angle and many pixels on the focal plane,_is geven by thehigh-dimensional function of the pixel number. In addition, since PL is tilt-driven, the tile angle

rotation matrix must be considered. On the other hand, IRS (SWIR, TIR) have a narrow view

angle and a small number of pixels on the focal plane. However, considering the reflection byrotary scanning mirror, the view vectors are calculated.

(b) Conversion to STT Attitude Determination Coordinate System

The view vectors in the base coordinate of radiometer obtained in (a) are converted to the
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GCOM-C “SHIKISAI” Data Users Handbook
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view vectors in STT attitude determination coordinate system using the pointing alignment
matrix. The pointing alignment matrices consist of fixed bias, long-term bias variation
component, and orbital fluctuation component by thermal distortion.
(c) Conversion to ECI (Mean of J2000)

Using the quaternions contained in real-time PCD, the view vectors in STT coordinate areconverted to Earth-Centered Inertial Coordinate System (ECI). However, since quaternions

are sampled at 10Hz, that are interpolated at the instant imaging timing. In addition, if thequaternion is not available, the vectors are converted to the orbit coordinate system using theattitude error angle at the instant imaging timing and converted to ECI using the satellite
position and velocity vector.

(d) Conversion to ECI （True of Date）

The vectors in ECI (J2000) are converted to ECI (True of Date) by calculating the precession

and nutation using the instant imaging timing and earth orientation parameter.
(e) Conversion to Pseudo ECR

The vectors in ECI (True of Date) are converted to the pseudo Earth-Centered Rotating
Coordinate System (ECR) using Greenwich mean sidereal time.

(f)Conversion to ECR

The vectors in the pseudo ECR are converted to the ECR using the polar motion parameter.

(g) Calculation of Latitude and Longitude
Using the view vector, view vector origin, and satellite positionin ECR, thelatitude and longitude of observation point are calculated. Figure 4-17 shows the schematic
diagram of the process.

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Satellite
position
衛星位置P
S
POPP
光学系主点
View vector origin

Z

ｌ・V

∙

撮像点P
E
Latitude/longitude
of observation point,X
Y

Figure 4-17 Schematic Diagram: Calculation of Latitude/longitude of Observation Point(2) Conversion to Level1B reference coordinate

As Level1B reference coordinate to which Level1A pixel is projected, a virtual frame in whicha push bloom CCD line sensor is arranged on the virtual cylindrical plane centered on thesatellite orbit is defined as Level1B reference coordinate system (Figure 4-18).

Figure 4-18 Definition of Level1B Reference Coordinate
In the conversion process to Level1B reference coordinate, Level1A pixel addresses
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corresponding to the pixel addresses in Level1B coordinate are calculated by theconvergence calculation, and the radiance value of Level1A is transferred in Level1B

coordinate. These address relation are expressed as the coefficients of approximate
polynomial. In addition, these coefficients are stored in Level1B product.
4.2.2.5 Calculation of Level1A Geometry Information

The latitude and longitude information of Level1A are obtained, just as 4.2.2.4 using thecorrespondence relationship between Level1A and Level1B pixel addresses. However, unlike

4.2.2.4 this process uses the coefficients of approximate polynomial to Level1B for each lineof Level1A. These coefficients are given to each telescope in case of VNR-NP and stored in
Level1B product.

4.2.2.6 Stray Light Correction

The stray light is a part of light that passes through the optical path different from the usual

by the characteristics of hardware within the sensor. Figure 4-19 shows the image ofoccurrence of the stray light. The stray light correction means the process to remove suchstray light with the software. When the light enters a pixel, the degree of occurrence of straylight in adjacent areas can be measured by pre-launch ground test. It is able to consider that
SGLI observation images are the convolution of optical transfer function of SGLI stray lightand true observation images, therefore, if the transfer function of SGLI stray light can bemeasured in advance, it is able to remove stray lights from SGLI observation images byapplying deconvolution to observed image on-orbit.

Figure 4-19 Ideal Observation Value (left) and Observation Value with Stray Light (right)
There are two types of stray light correction: the correction performed in Level1A coordinate

system (on-orbit observation image) and the correction performed on a coordinate system after
the simplified geometric correction (ground test coordinate system for VNR and view vector
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coordinate system of SRU for IRS). First, the stray light corrections are performed in Level1A
coordinate system, and then the simplified geometric corrections are performed for the FFT
calculation area divided in CT direction in Level1A coordinated system, and the instant images

at t=t_0 are generated. The stray light sources at t=t_0 are moved due to the rotation of earth att=t_0+∆t. Therefore, if the stray light corrections are performed in Level1A coordinate system,
except the stray light generated near the pixels on focal plane, the deviation of stray light source

affect the correction accuracy. Therefore, the instant images observed at the satellite position att=t_0 are generated. These processes are called the simplified geometric correction.
Subsequently, the stray light corrections are performed to the instant image to obtain stray lightcorrection amount ∆L for each pixel. These stray light correction amount ∆L images are
performed to convert to L1A coordinate system by the inverse simplified geometric correction.
These ∆L images are appended the original image. For more details about stray light types ineach sensor, coordinate system to conduct correction, and correction processes, please see
Algorithm Theoretical Basis Documents and Level1 Implementation Processing Document.
4.2.2.7 Stripe noise correction

In the image after radiometric correction, the stripe noise caused by photo response

non-uniformity or radiometric gap between telescopes of VNR-NP may be remained.
Therefore, the correction processes to remove the stripe noise are implemented. For moredetails, please refer to Level1 Implementation Processing Document.
4.2.2.8 Land-water flag calculation

The process searches for land and water flag source information included in the footprint,

and the percentage of the land area in the footprint is obtained.
4.2.2.9 Calculation of Stokes Vector (VNR-PL)

Using the observed data of 2 telescopes x 3 channels (polarizer: + 60 ° [S09], 0 ° [S10], -60 ° [S11]) of VNR-PL, stokes vector (I, Q, U components) for 2 telescopes are calculated.
There are two types for the calculated models: a simplified model or a model to correct opticalpolarization properties.

(1) Simplified Model

Stokes vector
The data analysis matrix, ,=is obtained fromcomponent from the Mueller matrix.

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=

, ,
,

,

,

, ,

,

, ,

,

, ,

that is obtained by removing gain

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,

1

2

= 1

2

1

n: Band

2

,

2

,

2

,

2

,
,

,

ch: three polarizer: + 60 ° [S09], 0 ° [S10], - 60 ° [S11]s: scan number (line number)p: pixel number,,, , : Spectral radiance (Level1B reference coordinate): Azimuth angle of transmission angle of polarization filter in S09(2) Model to Correct Optical Polarization Properties
The data analysis matrix

systemis obtained from the Mueller matrix of light collection optical, and Mueller matrixes of polarized azimuth angle of polarization filter and optical

depolarization component.

,1 (1  a P , 09 ) cos 2(l  09 ) (1  a P , 09 ) sin 2(l  09 ) = 1 (1  a P ,10 ) cos 2(l  10 ) (1  a P ,10 ) sin 2(l  10 ) 1 (1  a ) cos 2(   ) (1  a ) sin 2(   ) 
P ,11
l
11
P ,11
l
11 


1

The data analysis matrix A in which the matrix components are parameterized is as follows.

Nine components (

,

to

,

) of inverse matrix are sensor calibration parameters.

 nn, p ,11,09 nn, p ,12,09 nn, p ,13,09 


n
n
n
＝

,
n , p ,11,10n , p ,12,10n , p ,13,10 n
 n, p ,11,11 nn, p ,12,11 nn, p ,13,11 

1: Polarization rotation angle of light collection optical system
Since

,

to

are parameters at pixel number,of CCD, it is necessary to refer tothe conversion coefficient to the pixel number on Level1A address.
In addition, since, ,is the stokes vector at coordinate observed by PL sensor, the vector

is converted to scattering plane coordinate. The angle ( ) between the normal vector atscanning plane and the normal vector at scattering plane coordinate is used to coordinatetransformation matrix.

, , _

_

1
0
= 0 cos 20 sin 2

The Level1B product is stored stokes vector

, , _

0
− sin 2
cos 2
_

, ,

, spectral radianceand the coefficient of formula to convert to Level1A address (4.2.2.4 ).
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4.2.2.10 Resampling to low resolution productIn sensors except VNR-PL, Level1B high resolution observation (250-500 m) products areresampled to generate low resolution (1 km) Level1B product. The scene products before andafter the L1B high resolution scene product to be processed are input at the same time. Those

products are to be used for compensation if any part of the overlap of the target product ismissing. The resampling method depends on the data set properties such as averaging
process and extraction of representative values.
4.3 Level2 & Level3 Processing Algorithm

The general descriptions of higher level processing algorithms of land, atmosphere, ocean,cryosphere, and statistical processing are provided in 4.3.1 through 4.3.4 respectively. For
more details, please refer to Algorithm Theoretical Basis Documents (ATBD) of eachalgorithm. Level2 & Level3 processing ATBD is available at the following URL:
https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std.html

4.3.1 Land Algorithm

For land algorithm, as Level2 processing, there is a precise geometric correction processingthat is a process to generate tile data in the Sinusoidal projection. In addition, there are fivestandard algorithms specialized in retrieving the physical variable of land from the tile data.
4.3.1.1 Precise Geometric Corrected Radiance Algorithm(1) As the tile product of precise geometric corrected top of atmosphere (TOA) radiance, itcorrects the parallax due to postural change or altitude using the parameter PGCP withpositional variation estimated using GCP, and stores the image projected onto the grid of
EQA projection centered on 0-degree longitude.
(2) General Description of Algorithm

The precise geometric corrected TOA radiance product consists of multiple numbers ofalgorithms. The general description of individual algorithm is provided below. In addition, theoverall processing flow is shown in Figure.
a. Geometric Orientation Algorithm

Using the Level1B for each path of VNR/IRS and GCP_Info (GCP measurement data) as

input, it obtains orientation elements of VNR and IRS respectively by orientation calculation,and stores the orientation element with orientation residual data of each GCP in the PGCP
file.

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SGLI Productsb. Ortho Mosaic Algorithm

Using the Level1B of VNR/IRS/POL, PGCP and DEM as input, the map of Level1B address

corresponding to the horizontal position of each tile grid and altitude are generated bygeometric projection processing which takes into account the orientation element error of

PGCP. Using the level-1B address map, it sets the level-1B image data and geometric data(such as sensor angle, solar angle) to each grid of the tile. In the case of the overlap ofseveral paths, the data with smaller sensor zenith angle is prioritized. The tiles at 1 km
resolution are generated by the conversion (4 x 4 averaging) of 250 m resolution tiles data.
c. GCP Matching

Using the Level1B positional information (latitude and longitude data) as input, the image of

SGLI and AVNIR-2 (reference) are resampled to the matching image of the same resolution.
The algorithm extracts characteristic points form SGLI image automatically, and by thematching process using Normalized Cross-Correlation, extracts the tie point with AVNIR-2 as

GCP. It calculates the latitude and longitude from AVNIR-2 geometric information data, adds
the height from DEM, and outputs “GCP_info” as GCP (image address, latitude, longitude andheight) automatic measurement data.

Figure 4-20 Overall Flow of Precise Geometric Corrected Radiance Algorithm
4.3.1.2 Land Atmospheric Correction Algorithm(1) Product Definition

The product is the atmospherically corrected reflectance equivalent to that of the land

surface removing the effect of scattering/absorption of light caused by gas molecules andaerosol particles in the atmosphere, and in case of 8-day or 1month product, directional
dependence of the reflectance is corrected.

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SGLI Products(2) General Description of Algorithm

The algorithm uses TOA radiance (reflectance) data of the daytime as input, corrects thescattering/absorption by atmospheric molecules and aerosol, and outputs the ground levelreflectance.

By using tile data of multiple days as input, it outputs the reflectance under the satellite andsolar geometric condition on a target date of processing taking into account the land surface
BRDF.

4.3.1.3 Vegetation Index Algorithm(1) Product Definition

The product consists of the Normalized Difference Vegetation Index (NDVI), Enhanced

Vegetation Index (EVI), and ShaDow Index (SDI). NDVI and EVI are indices indicating thecoverage or activeness of vegetation, and SDI is an index indicating the ratio of shadow ofvegetation using spectrum information.
(2) General Description of Algorithm

NDVI is calculated by the formula (near infrared – red) / (near infrared + red), and the larger

the value becomes, the larger the vegetation density. EVI is calculated by the formula G x(near infrared – red) / (near infrared + C1 x red – C2 x blue + L), where G, C1, C2, L areconstant numbers, and it provides higher sensitivity compared to NDVI where vegetation isdense. SDI is the “fraction of shadow generated by conformation of vegetation (areal

occupation within a pixel)” and is estimated with regression equation as functions of the solar

incident angle and the reflectance at a shortwave infrared band, the coefficients of which are
determined based on in-situ observations.

4.3.1.4 Leaf Area Index/ Fraction of Absorbed Photosynthetically Active Radiation
Algorithm(1) Product Definition

The product consists of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically

Active Radiation (FAPAR). LAI is the total area (m2/m2) of one side of all leaves of plants perunit area of ground, and this product estimates LAI of upper layer vegetation (crown for forest
area, low trees and plants for non-forest area). FAPAR is, among the incident light of thespectrum region absorbed by plants photosynthetically (wavelength: 400-700 nm), thepercentage absorbed by the crown (leaves on trees).
(2) General Description of Algorithm
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a. Derivation of the Relation between LAI, FAPAR and Reflectance (NDVI) (for Each Land
Cover)For each land cover, the algorithm generates a lookup table (LUT) indicating the relationshipbetween LAI, FAPAR, land surface reflectance and crown reflectance using FLiES that
performs 3-dimensional radiative transfer simulations, in relation to arbitrary solar zenith
angle, satellite zenith angle and relative azimuth angle. In the simulations, a tuning with thein-situ data is performed using forest stand structure (number of trees, crown shape, crownheight for instance) that represents each land cover, reflectance/transmittance of individual

leaves, and soil reflectance. In addition, NDVI is calculated with RED/NIR reflectances in thealgorithm.

b. Calculation of Land Surface Reflectance of Satellite’s Nadir Direction (the value when
the satellite zenith angle becomes the minimum during the past 8 days)

Using SGLI observation data for the past 8 days including the observations day, the SGLI
data of the day on which the satellite zenith angle became the minimum (the satellite
elevation became the highest) is selected for each pixel.

c. Estimation of LAI/FAPAR from Bi-directional NDVI (Calculated form Land Surface
Reflectance)

By making use or the bi-directional observation capability, which is one of the uniquecharacteristics of SGLI, LAI/FAPAR is estimated using the reflectances at nadir view direction(VN08 and VN11) and reflectances at slant view direction (PI01 and PI02).
4.3.1.5 Above-Ground Biomass Algorithm(1) Product Definition

The product consists of above-ground biomass (AGB) and vegetation roughness index (VRI).

AGB is the volume of aboveground biomass shown in dry weight (t/ha), and VRI is the index
expressing 3D structural information of vegetation (the unevenness changes in spatial

distribution of canopy density) extracted from the “the dependence of observed radiance on
the observing angle” obtained by multi-angle observations.
(2) General Description of Algorithm

Using the reflectance data (at satellite’s nadir and slant viewing directions) as input, thealgorithm outputs AGB derived based on LUT which indicates the relationship between the
spectral reflectance of canopy and AGB. SGLI non-polarization data at red and near infrared

bands (VN08, VN11) are used for reflectance at nadir view direction, whereas the data of thepolarization telescope (PI01 (red) and PL02 (near infrared)) are used for reflectance at slantview direction. In generation of LUT, the bi-directional reflectance simulator BiRS is used. The
forest structure model to be input to BiRS is generated based on the in-situ data such as
canopy in shadow area, canopy in sunny area, and layout of trees. AGB is calculated using
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the existing land cover classification product for each land cover type.

a. Calculation of Relationship between AGB, VRI and Reflectance (for 8 types of land
cover)

Using BiRS, LUT which makes the relationship between RED and NIR reflectances (atnadir and slant view direction), AGB and VRI are generated for each land cover type of

GlobCover dataset (the LUT is also functions the arbitrary zenith angle, satellite zenith angle,
relative azimuth angle).

b. Estimation of AGB and VRI Using Bi-directional ground surface reflectance

The algorithm determines the estimates of AGB and VRI using the reflectance at satellite’snadir direction (VN08 and VN11) and reflectance at slant direction (PI01 and PI02) at thegeometric condition of the SGLI observation.
4.3.1.6 Land Surface Temperature Algorithm(1) Product Definition

The product consists of the land surface temperature (LST) and emissivities at the two

split-window bands (TI01 and TI02).

(2) General Description of Algorithm

Using the geometric corrected TOA radiance data as input, the algorithm estimates the land

surface temperature and land surface emissivities simultaneously by semi-analytical

approach based on the radiative transfer simulation at two thermal infrared bands and theSplit-Window method. This enables to obtain the land surface temperature taking into accountthe temporal fluctuation of land surface emissivity. The radiative transfer code is developedand implemented for the speed up the processing.
4.3.2 Atmosphere Algorithm

Atmosphere algorithm uses the tile data as input, and there is a process to generate global

products of TOA radiance and four standard algorithms specialized in retrieving physicalvariables of the atmosphere.

4.3.2.1 Global Top of Atmosphere Radiance/Clear Region Composition Algorithm(1) Product Definition

Using observation data of a target day, the algorithm generates a global EQA product (1/24

deg) of TOA radiance (LTOAF) and that TOA radiance using only clear pixels (eliminating
cloudy pixels) (LCLRF)(2) General Description of Algorithm

Using the Level1B (1 km) products of target day as input, the algorithm generates a global
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TOA radiance product. This output product becomes the input to cloud flag and cloudproperties algorithm. In addition, using the TOA radiance tile (1 km) products and cloud flag (1km) products of a target day as input, it generates the composite of global TOA radiance with

only clear pixel (cloudy pixel removed). This output product becomes the input tonon-polarized/polarized aerosol algorithm.
4.3.2.2 Cloud Flag Algorithm(1) Product Definition

The product consists of two flags. One is the cloud flag that indicates the existence of cloudwithin the pixel and the other is the cloud phase flag that indicates whether the cloud particles

are water or ice. Both flags are generated from both daytime and nighttime observation datawith global coverage. In addition, the cloud flag becomes the input of cloud properties/
non-polarized aerosol/ polarized aerosol/ clear region composition algorithm.
(2) General Description of Algorithm

The cloud identification algorithm CLAUDIA (Cloud and Aerosol Unbiased Decision

Intellectual Algorithm) performs multiple threshold tests using the TOA radiance(reflectance/radiance temperature) and their difference or ratio to calculate the confidence
level for each threshold test (0.0: cloud – 1.0: clear) and then integrates the calculated

confidence level to generate the final clear confidence level as output. Since the selection of

the threshold tests is arbitrary, the algorithm has versatility regarding channel configuration ofsatellite sensors. In addition, even when the data include partially missing, the clear
confidence level can be calculated as far as more than one threshold test is performed.
Therefore, there is robustness in the processing. The cloud phase identifies whether the cloudparticle is water or ice using brightness temperatures at infrared bands.
4.3.2.3 Cloud Properties Algorithm(1) Product Definition

The product consists of water-cloud optical thickness, effective radius, ice-cloud optical

thickness, cloud top temperature, and height, and International Satellite Cloud Climatology
Project (ISCCP) cloud type that are defined by the relationship between cloud optical

thickness and cloud-top atmosphere pressure and the morphological cloud type. The cloudoptical thickness and effective radius of cloud particle are retrieved only from daytime
observation data, and the cloud-top temperature and height, and ISCCP cloud type are fromboth daytime and nighttime data.

(2) General Description of Algorithm

The cloud properties algorithm utilizes the Comprehensive Analysis Program for Cloud
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Optical Measurements (CAPCOM), which has been developed for the previous GLI mission
and now customized for the SGLI channel characteristics, and provides functions ofestimation of ice cloud properties and the classification of ISCCP cloud types as additional

new products of SGLI. In addition, the calculation of radiative transfer is speeded up using
LUT, and the out-of-band response around the wavelength of 1.6 µm at SW4 (2.21 µm) is
taken into account by the radiative transfer calculation.

The simulations of light scattering by ice particle are performed assuming Voronoi particle

model for ice particle shape. In addition, ISCCP cloud type classification is performed basedon the relationship between the cloud optical thickness and cloud-top atmosphere pressure.
4.3.2.4 Non-polarized Aerosol Algorithm(1) Product Definition

The aerosol properties over the ocean consist of the optical thickness of aerosol, angstrom

exponent and aerosol type estimated based on the visible and near infrared reflectance. In
addition, the aerosol properties over the land consist of aerosol optical thickness, angstrom
exponent and single scattering albedo estimated based on the near ultraviolet reflectance.
(2) General Description of Algorithm

A common aerosol optical model is used for the retrieval over ocean and land, and the model

is determined based on the skyradiometer observation data. While fixing the particle shape,

real part of complex refraction index and size distributions of large and small particle, thefraction of small particle and complex refraction index (in terms of SSA) are assumed to bevariable. For retrieval, the algorithm estimates an aerosol parameter suitable to the SGLI
observation, and outputs aerosol optical thickness, angstrom exponent and single scattering
albedo.

4.3.2.5 Polarized Aerosol Characteristics Algorithm(1) Product Definition

The product consists of aerosol optical thickness, angstrom exponent and single scattering

albedo over the land estimated from the polarized observation data.
(2) General Description of Algorithm

The algorithm uses non-polarized observation at 380 nm band (VN01) and polarized

observation at 670 nm and 870 nm bands (PL01, PL02) to estimates the aerosol optical

thickness, angstrom exponent and single scattering albedo over the land. By referring topreviously generated LUT based on the radiative transfer calculation, the estimation process
is speeded up.

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4.3.3 Ocean Algorithm

The ocean algorithm uses the Level1B product (scene) as input, and there are total of threestandard algorithms specialized in the physical variables of the ocean.
4.3.3.1 Sea Surface Temperature Algorithm(1) Product Definition

The algorithm calculates the bulk temperature [°C] of sea surface (including cloud detection).
(2) General Description of Algorithm

The sea surface temperature is estimated by approximately solving the infrared radiativetransfer equation (LUT is used for implementation). Clear/cloud is estimated from VN8 andSW2 (only for daytime), T1, and T2 data with a method combined of threshold based tests

and a method based on Bayesian inference. Derived cloud probability, that statisticallyconnects to quality of determined SSTs, is used for the decision of the quality level (QL) of
retrieved SST.

4.3.3.2 Ocean Atmosphere Correction Algorithm(1) Product Definition

The product consists of the normalized water-leaving radiance (NWLR), atmospherecorrection parameter (ACP) and photosynthetically available radiation (PAR). NWLR
[W/m2/sr/μm] is the upwelling radiance just above the sea surface, ACP is the aerosol optical
thicknessused to estimate effects of atmospheric scattering, and PAR [Ein/m2/day] is thephoton flux density within the visible wavelength range (400 to 700 nm) over the ocean whichis potentially available to marine plants for photosynthesis.
(2) General Description of Algorithm

The algorithm uses the Level1B product of the radiance observation in daytime region as

input, corrects for the atmosphere molecule, aerosol, sun-glitter, white cap and Bidirectional

Reflectance Distribution Function (BRDF), and outputs NWLR. ACP is estimated during theaerosol reflectance correction processing in the algorithm. PAR is estimated by simplifying thesolar light incident on the sea surface into two elements: “atmospheric molecule + aerosol”
and “cloud + sea surface”.

4.3.3.3 In-water properties Algorithm(1) Product Definition

The product consists of chlorophyll-a concentration (CHLA), Total suspended matter

concentration (TSM) and Colored dissolved organic matter (CDOM). CHLA [mg/m3] is theconcentration of the green pigment in phytoplankton in sea surface layer, TSM [g/m3] is the
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dry weight of suspended matter in a unit volume of surface water which is the sum of organics

such as phytoplankton and inorganics such as soil and CDOM [m-1] is defined as the lightabsorption coefficient of organics dissolved in surface water.
(2) General Description of Algorithm

CHLA is estimated using the ratio of the remote sensing reflectance between the blue andgreen bands. When the estimated concertation is lower than a threshold, the algorithm further
applies a difference of the spectral remote sensing reflectance. TSM is estimated by anempirical relationship between a remote sensing reflectance ratio and TSM. For CDOM, theabsorption index is derived firstly from an inherent optical property model, and then the lightabsorption coefficient of dissolved organic matter is estimated using an empirical relationshipwith the index.

4.3.4 Cryosphere Algorithm

The cryosphere algorithm uses tile data as input, and there are in total two standard

algorithms specialized in the retrievals of snow/ice cover extents and snow physical variables.
The local product of sea-ice distribution for Okhotsk Sea is derived from the Level1B radiancedata (scene) using the same algorithm for the retrieval of snow and ice cover extents.
4.3.4.1 Snow and Ice Cover Extent Algorithm(1) Product Definition

The product consists of the Snow and Ice Cover Extent product (SICE) covering on global

and Okhotsk sea-Ice Distribution product (OKID) targeting on the Sea of Okhotsk, northern ofJapan. It distinguishes snow, ice, cloud and several types of ground surface focusing on thecryospheric region based on the difference in reflectance characteristics by ground surface
varieties.

(2) General Description of Algorithm

By using the observed reflectance and brightness temperature, the algorithm performs

several threshold tests including normalized difference snow index (NDSI) test, single band
threshold test, and two band test with dynamic threshold, to discriminate cloud and surface
type cover types (snow, sea-ice, bare land), and outputs the identified flag.

① The threshold tests of NDSI are performed to determine snow region, non-snow region
and cloud region.

② As single band reflectance test, multiple tests are performed using the threshold ofreflectance of visible/near infrared range to determine cloud region. The clear pixels
are used to discriminate ground surface types
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SGLI Products③ As two band test with dynamic thresholds, since the reflectance of snow is varied as thegrain size changes due to morphological change of ice particles, LUT is generated inconsideration of that effect on the SW4 and SW3 reflectances. Using the thresholddetermined based on the generated LUT, snow and cloud region are determined.
4.3.4.2 Snow ice physical properties Algorithm(1) Product Definition

As snow and ice physical properties product (SIPR) in snow cover over land and sea-ice

region, snow grain size of shallow layer (SGSL) and snow and ice surface temperature (SIST)
in clear sky region are retrieved.

(2) General Description of Algorithm

It estimates snow and ice physical properties by three steps: forward radiative transfer model,

neural network training, and nonlinear optimal estimation. In the estimation of opticalproperties of snow grain, as the particle shape model, non-spherical Voronoi particle, which is
closer to the real snow grain compared with the spherical shape or hexagonal column shapeused in the former GLI algorithm, is used to estimate scattering properties precisely. Using theoptical properties derived with the Voronoi particle shape model, radiative transfer ofatmosphere-snow system is calculated using the discrete-ordinate radiative transfer(DISORT) model for cases with various grain sizes to estimate the relationship between snow

grain size and radiance. To estimate the snow grain size from the radiance, their relationshipis learned as a nonlinear function using the neural network training, and the snow grain size ofshallow layer is estimated based on the observed radiance by nonlinear optimal estimation

method. The snow and ice surface temperature is estimated by the split window method.

Considering that the snow surface emissivity varies depending on the observation angle,
parameters for the Split-Window method are changed in accordance with the observation
conditions to estimate the temperature.
4.3.5 Statistic Processing Algorithm

The statistics processings of Level2 are temporal statistic and mosaic per 8-day or 1-month.

The spatial resolutions of the input tile products are kept the same. The statistics processingsof Level3 are spatial and temporal statistic per 8-day or 1-month. The spatial resolution of
output is 1/12 or 1/24 degree.

4.3.5.1 Level2 Tile Statistic Algorithm(1) Product Definition

It generates the temporal statistic product of Level2 land/cryosphere tile product.
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SGLI Products(2) General Description of Algorithm

Using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as

input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region
definition and spatial resolutions of the output product are kept those of input data.

The statistics values stored to product are average (AVE), root-mean-square (RMS),

maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of data
used (Nused), date of observation (Date), and quality flag (QA_flag).
4.3.5.2 Level2 Tile Top of Atmosphere Radiance Mosaic Algorithm(1) Product Definition

It generates the mosaic (clear region composite) product of Level2 tile product of precise

geometric corrected TOA radiance.

(2) General Description of Algorithm

Using the Level2 tile product of precise geometric corrected TOA radiance (Daily, Tile, 250m

resolution) as input, the clear region mosaic data for 8-days or 1-month is stored in the output

product. The region definition and spatial resolutions of the output product are kept the input
data. Maximum absolute NDVI composite method is used for mosaicking process. In thismethod output data store the TOA radiance of a single observation day on which the value of|NDVI-α| becomes the maximum during the temporal interval (8-day or 1-month). Here, α is atuning parameter.

4.3.5.3 Level2 Tile BRDF-corrected Land Reflectance Algorithm(1) Product Definition

The product stores temporal statistics of the atmospheric corrected reflectance with thecorrection of its directional dependence.
(2) General Description of Algorithm

Using the Level2 atmospheric corrected land surface reflectance tile product (Daily, Tile, 250

m resolution) as input, BRDF correction is performed for the land surface in clear region using

data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in
the output product. The region definition and spatial resolutions of the output product are kept

the input data. In 8-day and 1-month processing, the land surface reflectance data of the past
24 days (including the last day of the 8-days) and that of the past 1-month, respectively are
fitted to generate appropriate BRDF models with the correction coefficients.
4.3.5.4 Level3 Global Spatial Binning Algorithm
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It generates the Level3 daily spatial binning statistics product (EQA) by reducing the spatial
resolution of the Level2 product.

(2) General Description of Algorithm

Using the Level2 products (Daily, Tile-250 m and 1 km [land and cryosphere], Scene-1 km[ocean], Global Bin-1/24 deg [atmosphere]) as input, the algorithm outputs and stores thestatistics of geophysical variables in the spatial resolution 1/24 deg grid (land, cryosphere andocean) or 1/12 deg grid (atmosphere). The statistics values stored in the output product are

average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN),
number of input data (Ninput), number of data used (Nused), date of observation (Date), andquality flag (QA_flag).

4.3.5.5 Level3 Global Temporal Binning Algorithm(1) Product Definition

It generates the Level3 8-day or 1-month temporal binning statistics product (EQA).
(2) General Description of Algorithm

Using the Level3 spatial binning product (Daily, Global Bin-1/24 deg [land, cryosphere andocean] or 1/12 deg [atmosphere]) as input, the algorithm outputs and stores 8-day or 1-monthstatistics. The statistics values stored in the output product are average (AVE),
root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input
data (Ninput), number of data used (Nused), date of observation (Date), and quality flag
(QA_flag).

4.3.5.6 Level3 Global Map Algorithm(1) Product Definition

It generates the Level3 map-projected statistics products projected to the equirectangular(EQR) grid for all products and also projected to polar stereographic (PS) projection only forcryosphere products(2) General Description of Algorithm

Using the Level3 spatial/temporal binning statistics products (Global Bin-1/24 deg[land/cryosphere/ocean], Global Bin-1/12 deg [atmosphere]) as input, the algorithm projects

the statistics to the EQR (PS in case of cryosphere). The statistics values stored in the output
product are average (AVE) and quality flag (QA_flag).

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4.4 Product Formats

The details of SGLI Level1 and Level2 & Level3 product formats are available at G-Portal.

■ SGLI Level 1 Product Format Description■ SGLI Higher Level Product Format Description
4.5 Data Calibration and Validation

GCOM-C calibration and validation guarantee the quality and reliability of GCOM-C/SGLI

standard products through a series of calibration and validation activities including pre-launch

ground test, sensor model development, on-orbit calibration data evaluations and accuracy

evaluation results of geophysical variables. Figure 4-21 shows the outline of calibration andvalidation schedule.

Figure 4-21 Outline of SGLI Calibration and Verification Schedule

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4.5.1 Calibration

4.5.1.1 Calibration Phase

The schedules in each phase from pre-launch are shown below.

(1) Sensor Performance Evaluation Phase (pre-launch)This phase identifies sensor properties associated with calibration, conducts ground test andanalysis, and examines corrective methods as needed. Based on the results of these
examinations, the coefficient of the sensor model for radiometric and geometric correction aredetermined and the values are inputed to Level1 processing algorithms as the pre-launch
initial value.

(2) Initial Checkout Phase (Launch to Launch + 3M)

This phase obtains the on-orbit calibration data of solar diffuser, internal lamp, black body andinitial images to evaluate the healthiness of various functions of satellite and sensor (earth

observation and calibration function etc.). In addition, the maneuver observation functions
obtain the data for the evaluation of lunar calibration or β angle dependency of diffuser. The
geometry calibration evaluates GCP to stabilize the geometry accuracy at an early stage ofinitial calibration and validation phase.

(3) Initial Calibration and Validation Phase (Launch + 3M to Launch 12M)

This phase conducts solar calibration, internal lamp calibration, deep space calibration, lunar

calibration, electrical calibration, and GCP evaluation periodically to perform regular check of

the sensor performance and trend evaluation. The phase also conducts image evaluation,
vicarious calibration, and cross-calibration to estimate sensor characteristic necessary for the

product creation. For Level1 product data release, the results of those calibrations arereflected to algorithms.

(4) Regular Evaluation Phase (from Launch + 12M and later)This phase regularly evaluates the on-orbit calibration, and the product accuracy is

continuously assured by reflecting the calibration result to the algorithm as needed.
Particularly, regarding the temporal fluctuation of sensor sensitivity, detailed analysis of theeffect of β angle in solar calibration, the effect of lunar age in lunar calibration and thevicarious calibrations are performed in combination.
4.5.2 Validation

4.5.2.1 Product Validation

The variation is performed to evaluate the accuracy of geophysical variables products, and it

is conducted by comparison with the in-situ data obtained by in-situ observation. The
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accuracy evaluation of SGLI Level2 & Level3 products is conducted by comparing the target
accuracy defined for each mission phase with in-situ data, other satellites data, and thenumerical model data etc.

Table 4-21 to Table 4-26 describe the target accuracy and validation method for each phase.

The calibration and validation monitor systems are developed to automate the series ofprocessing from data obtaining to matchup analysis of SGLI observation value andparameters.

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Table 4-21 Overview of SGLI Level2 & Level3 products Verification Method（1/6）
Product [Definition・Unit]

Accuracy*1

Satellite-observed radianceRelease(Data(Level1B）
Def.: Satellite-observed radiances which are radiometrically releaseand geometrically corrected with inter-band registration. threshold)Standard
Calibration information is added.
Unit: W/m2/sr/m
Goal

Land

Precise geometric corrected radiance (LTOA)
ReleaseDef.: This product contains 1) PGCP parameters whichindicate geometric biases estimated using GCP, and 2)
radiance images which are projected to sinusoidal Standard
projection plane with the center longitude of 0 degree after
the correction of the geometric biases using the PGCP.
Unit: W/m2/sr/m
Goal

Cal/Val Method
5% (absolute*1)
geometric accr.<1pixel
except TIR: 5%(abs.*1), 1%(relative)
TIR: 0.5K (@300K)
geometric accr.<0.5pixel
Except TIR: 3%(abs.*1),
0.5% (relative)
TIR: 0.5K (@300K)
geometric accr.<0.3pixel<1pixel<0.25pixel

0.3 (<=443nm), 0.2(>443nm) (scene)*L1

Vegetation index (VGI)
Def.: Indices indicating vegetation cover and activity such as
NDVI and EVI
Unit: none

Grass land： 25% (scene),
Forest： 20% (scene)

Standard
Goal

Accuracy of precise geometric correction is evaluated as RMS error of pixel
position using GCPs.

<0.5pixel

Land atmospheric corrected reflectance (RSRF)
ReleaseDef.: Land surface reflectance corrected for the effects ofatmospheric scattering and absorption. Correction of
directional anisotropic effects are also made for 8-day and Standard
monthly composite products.
Unit: none
Goal
ReleaseAccuracy of radiance is evaluated as RMS error based on vicarious calibration,
on-board calibrations with solar diffuser and blackbody and so on.
Geometrical accuracy is evaluated using GCP as RMS error of pixel position
after systematic geometric correction.
Accuracy of radiance is evaluated as RMS error based on vicarious calibration,
on-board calibrations with solar diffuser and blackbody, and maneuver
operations for moon calibration and inter-band calibration (yaw-direction
maneuver).
Geometrical accuracy is evaluated using GCP as RMS error of pixel position
after systematic geometric correction.

0.1 (<=443nm), 0.05(>443nm) (scene)*L1
0.05 (<=443nm), 0.025(>443nm) (scene)*L1

Grass land: 20% (scene),
Forest： 15% (scene)

Grass land： 10% (scene),
Forest: 10% (scene)

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RMS error between satellite-derived reflectances and ground truth
measurements is estimated at a region where aerosol optical thickness at
500nm is less than 0.25.
RMS error between satellite-derived reflectances and ground truth
measurements is estimated.
RMS error is evaluated comparing SGLI-derived VI with in-situ measured VI
derived from spectroradiometer data at JaLTER, JapanFlux, PEN, Yatsuga-taketower site etc. and also with other satellite VI products.
RMS error is evaluated comparing SGLI-derived VI with in-situ measured VI
derived from spectroradiometer data at JaLTER, JapanFlux, PEN, Yatsuga-taketower site etc.

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Table 4-22 Overview of SGLI Level2 & Level3 products Verification Method（2/6）
Product [Definition・Unit]
Above-ground biomass (AGB)
Def.: Dry weight of above-ground vegetation
Unit: t/ha

Accuracy*1
ReleaseGrass land： 50%,
Forest： 100%

Standard

Grass land：30%, Forest：
50%
Grass land：10%, Forest：
20%
Grass land･Forest：40%(scene)Grass land・Forest：20%(scene)Grass land・Forest：10%(scene)

Goal

Land

Vegetation roughness index (VRI)
ReleaseDef.: An index indicating plant vertical structure observed frommulti-angle directions.
Standard
Unit: none
Goal

Shadow index (SI)
Def.: An index indicating shadow fraction of vegetation area
inferred from spectral reflectance.
Unit: none

Fraction of absorbed PAR (FAPAR)
Def.: Fraction of photosynthetically active radiation absorbed
by vegetation
Unit: none

Cal/Val Method

ReleaseStan- dard
Goal

Grass land・Forest： 30%(scene)Grass land・Forest： 20%(scene)Grass land・Forest： 10%(scene)ReleaseGrass land： 50%,
Forest： 50%

Standard

Grass land：30%, Forest：
20%
Grass land：20%, Forest：
10%

Goal

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RMS error is evaluated comparing SGLI-derived AGBIO with in-situ measured
AGBIO at JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived fromdirect measurements of dry weight of grass at grass land, indirect estimation
with allometry equation as functions of tree diameter at brest height (DBH) andtree height, or 3-D laser scanner measurements at forest), and also with AGBIO
derived from other satellites, existing database and literature value.
RMS error is evaluated comparing SGLI-derived AGBIO with in-situ measured
AGBIO at JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc.
RMS error is evaluated comparing SGLI-derived VRI with in-situ measured VRI
at JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived fromspectoral reflectance data acquired using tower and RC helicopter and so on).

RMS error is evaluated comparing SGLI-derived VRI with in-situ measured SI at
JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived from spectoral
reflectance data acquired using tower and RC helicopter and so on), or
comparing with SI inferred from data of other satellite high spatial resolution
optical sensor as an aid.
RMS error is evaluated comparing SGLI-derived FAPAR with in-situ measured
FAPAR at JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived fromdata of PAR meter or spectroradiometer data measuring upward and downward
PAR at forest canopy and floor.), and with other satellite FAPAR products.
RMS error is evaluated comparing SGLI-derived FAPAR with in-situ measured
FAPAR at JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived fromdata of PAR meter or spectroradiometer data measuring upward and downward
PAR at forest canopy and floor.).

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Table 4-23 Overview of SGLI Level2 & Level3 products Verification Method（3/6）
Product [Definition・Unit]

Accuracy*1

Leaf area index (LAI)
ReleaseDef.: The sum of the one sided green leaf area per unit groundarea.
Unit: none

LandStandard
Land surface temperature (LST)
Def.: Temperature of terrestrical land surface.
Unit: KelvinCloud flag (CLFG)
Def.: Cloud discrimination flag including the classification ofcloud type and phase (liquid/solid).
Unit: none

Goal

Atmosphere

Cloud top temp/height (CLTTH)
Def.: Temperature and height of cloud top layer.
Unit: Kelvin for temperature, km for height

Grass land： 50%,
Forest： 50%
Grass land：30%, Forest：
30%
Grass land：20%, Forest：
20%
Less than 3.0K (scene)

RMS error is evaluated comparing SGLI-derived LAI with in-situ measured LAI at
JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived from data oflitter trap or LAI-2000 and spectroradiometer data measuring downward radiantflux etc. at forest floor.), and also with LAI of other satellite, database andliterature value.
RMS error is evaluated comparing SGLI-derived LAI with in-situ measured LAI at
JaLTER, JapanFlux, PEN, Yatsuga-take tower site etc. (derived from data oflitter trap or LAI-2000 and spectroradiometer data measuring downward radiantflux etc. at forest floor.).

Less than 1.5K (scene)

RMS error is evaluated comparing SGLI-derived LST with in-situ measured
radiation temperature at the bare ground and forest surface with uniform land
cover and also comparing with other satellite LST products.
RMS error is evaluated comparing SGLI-derived LST with in-situ measured LST
and also comparing with LST calculated from ground weather station(GTS etc.)
measurement data considering the injection rate.

Release10% (comparison with
sky-camera binary image)Classification error is evaluated comparing SGLI derived CLFG withskycamera images.

Standard

Evaluated as the cloud
fraction products.
Evaluated as the cloud
fraction products.
20% (as solar radiation)*A1

Same as the classified cloud fraction.

ReleaseStandard
Goal

Goal
Classified cloud fraction(CLFR)
Def.: Cloud fractions for 9 cloud types which are classified
based on the ISCCP classification rule.
Unit: percent

Cal/Val Method

ReleaseStandard

Less than 2.5K (scene)

15%( as solar radiation)

*A1

Goal

10%（as solar radiation)*A1

Standard

3K *A3 /2km *A3

ReleaseGoal

1K *A2

1.5K *A3 /1km*A3

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Same as the classified cloud fraction.
Overall classification error is evaluated comparing SGLI derived solar radiation
which is monthly average for every 0.1 degree global grid with in-situ measured
solar radiation, skycamera images, and existing cloud fraction climate datasets
such as ISCCP(the International Satellite Cloud Climatology Project).

The release criterion shown in the left column indicates a threshold for SGLI TIRband brightness temperature by which the ability to sense cloud top temperature
is evaluated indirectly. The accuracy of TIR band is assessed through the
product evaluation process of sea surface temperature etc. Also confirmed is theconsistency of SGLI derived cloud top temperature with object analysis data of
air temperature profile and cloud top height with climate over ocean in daytime.
RMS error is evaluated comparing SGLI derived CLTTH with those derived fromairbone and satellite borne lidar, sounder and radiometer etc. for uniform liquidclouds with moderate optical thickness.

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Table 4-24 Overview of SGLI Level2 & Level3 products Verification Method（4/6）
Product [Definition・Unit]

Water-cloud optical thickness & effective radius (CLOTER_W) Release
Def.: Optical thickness and effective radius of water cloud
droplets
Standard
Unit: none for thickness, m for radius

Ice-cloud optical thickness (CLOT_I)
Def.: Optical thickness of ice cloud.
Unit: none

Atmosphere

Accuracy*1

Cal/Val Method
10%/30% (optial
thickess/radius) *A4
100% (as cloud liquidwater*A6)

Goal

50% *A5 /20% *A6

Release30% *A4

Standard

70% *A6

RMS error is evaluated comparing SGLI derived CLOTER_W with those fromother satellite sensors for clouds of mid- to low latitude regions (monthly
average).
RMS error is evaluated comparing cloud liquid water converted from SGLI
derived CLOTER_W with those measured with microwave radiometer on the
ground.
Overall RMS error is evaluated comparing SGLI derived CLOTER_W with thosederived from microwave radiometer and skyradiometer (for optical thickness)and other satellite sensors (both param.).
RMS error is evaluated comparing SGLI derived CLOT_I with those from othersatellite sensors for clouds of mid- to low latitude regions (monthly average). .
RMS error is evaluated comparing SGLI derived CLOT_I with those fromskyradiometers at ground observation network and other satellite sensors.

Goal
Aerosol over the ocean (ARNP)
ReleaseDef.: Optical thickness, Ångström exponent, and classificationof aerosol over ocean estimated using visible and nearinfrared band.
Standard
Unit: none

20 % *A6
0.1(monthly ave. of a_670, Overall RMS error is evaluated comparing SGLI derived ARV with those fromother satellite sensors and climate datasets based on the past satellite865) *A7
observations (monthly average).
0.1(scene’s a_670, 865)*A7 RMS error is evaluated comparing SGLI derived ARV with those from othersatellite sensors and shipborne in-situ observations (AERONET/Maritime
Aerosol Network).
0.05(scene’s a_670, 865)

Land aerosol by near-UV (ARNP)
Def.: Optical thickness and light absorption coefficient ofaerosol over land estimated using near-ultraviolet band.
Unit: none

0.15(monthly ave.
ofa_380) *A7
0.15(scene’s a_380) *A7

RMS error is evaluated comparing SGLI derived ARU with those fromskyradiometers at ground observation network (Skynet, Aeronet) and othersatellite sensors.

0.1(scene’s a_380 )0.15(monthly ave. ofa_670, 865) *A7
0.15(scene’s a_670, 865)

RMS error is evaluated comparing SGLI derived ARP with those fromskyradiometers at ground observation network (Skynet, Aeronet) and othersatellite sensors for fine mode particles.

Goal

ReleaseStandard

Goal
Aerosol by Polarization (ARPL)
ReleaseDef.: Optical thickness, Ångström exponent, and classificationof aerosol estimated using polarization bands.
Standard
Unit: none
Goal

*A7

0.1(scene’s a_670, 865)

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Table 4-25 Overview of SGLI Level2 & Level3 products Verification Method（5/6）
Product [Definition・Unit]

Accuracy*1

Normalized water leaving radiance (NWLR)
Def.: The upwelling radiance just above the sea surface.
Unit: W/m2/str/um or 1/sr

Release60% (443~565nm)

Standard

50% (<600nm)
0.5W/m2/str/um (>600nm)

Goal
Atmospheric correction param.(ACP)
Def.: Aerosol optical properties for the atmospheric correction
over ocean.
Unit: none

ReleaseStandard

Ocean

Goal

Photosynthetically Available Radiation (PAR)
ReleaseDef.: Photon flux density within the visible wavelength range(400 to 700 nm) over ocean which is potencially available to Standard
plant for photosynthesis.
Goal
Unit: Ein/m2/day or mol photons/m2/day
Chlorophyll-a concentration (CHLA)
Def.: Concentration of the green pigment in phytoplankton insea surface layer.
Unit: mg/m3

Total Suspended Matter concentration (TSM)
Def.: Dry weight of suspended matter in a unit volume of
surface water which is the sum of organics such as
phytoplankton and inorganics such as soil.
Unit: g/m3
Colored dissolved organic matter (CDOM)
Def.: Light absoption coefficient of organics dissolved insurface water.
Unit: 1/m

Cal/Val Method

30% (<600nm)
0.25W/m2/str/um (>600nm)
80% (a_865)
50% (a_865)
30%

20% (10km/month)

15% (10km/month)
10% (10km/month)

Release-60~+150% (open sea)

Standard-60~+150%

Goal-35~+50% (open sea),-50~+100% (coastal)

ReleaseStandard-60~+150% (open sea)
-60~+150%

Goal
Release-50~+100%-60~+150% (open sea)

Standard
Goal-60~+150%-50~+100%

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RMS error is evaluated comparing SGLI derived NWLR with in-situ opticalmeasurements conducted during simultaneous ship observations campaign andalso comparing with other satellite products.
RMS error is evaluated comparing SGLI derived NWLR with in-situ opticalmeasurements conducted during simultaneous ship observations campaign.
RMS error is evaluated comparing SGLI derived aerosol optical thickness withthose from in-situ measurements using radiometers during simultaneous shipobservations campaign and also comparing with other satellite sensors.
RMS error is evaluated comparing SGLI derived aerosol optical thickness withthose from in-situ measurements using radiometers during simultaneous shipobservations campaign.

RMS error is evaluated comparing SGLI derived monthly averaged PAR withthose derived from mooring buoy such as NDBC, TAO/TRITON etc. as solar
radiation or PAR.

RMS error is evaluated comparing SGLI derived CHLA with those derived fromsea water samples by fluorescence method or HPLC analysis and also withother satellite products.

RMS error is evaluated comparing SGLI derived CHLA with those derived fromsea water samples by fluorescence method or HPLC analysis.
RMS error is evaluated comparing SGLI derived TSM with those derived fromsea water samples by filtration method and also with other satellite products.

RMS error is evaluated comparing SGLI derived TSM with those derived fromsea water samples by filtration method.
RMS error is evaluated comparing SGLI derived CDOM with those derived fromsea water samples by optical measurements and also with other satellite
products.

RMS error is evaluated comparing SGLI derived CDOM with those derived fromsea water samples by optical measurements.

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Table 4-26 Overview of SGLI Level2 & Level3 products Verification Method（6/6）
Product [Definition・Unit]

Accuracy*1

Sea surface temperature (SST)
Def.: Temperature of sea surface.
Unit: C

Release

0.8K (daytime only)Standard

0.8K

Release10% (comparison with other Overall RMS error is evaluated comparing SGLI derived SICA with othersatellite products)
satellites’ same products and climate of related geophysical parameters derived
from the past observations.

Snow and Ice covered area (SICA)
Def.: The extent of global snow and ice cover.
Unit: none

Cryosphere

Okhotsk sea-ice distribution (OKID)
Def.: The extent of sea ice in Okhotsk Sea.
Unit: none

Snow and ice surface Temperature (SIST)
Def.: Temperature of snow and ice surface.
Unit: Kelvin

Goal

Stan- dard
Goal

ReleaseStandard
Goal

ReleaseStan- dard

Snow grain size of shallow layer (SNGSL)
Def.: Grain size of snow ice particle in shallow layer derived
mainly from SGLI 865nm band reflectance.
Unit: m

Goal

ReleaseStandard
Goal

Cal/Val Method

0.6K

Overall RMS error is evaluated comparing SGLI derived SST with those derived
from other satellite sensors and also comparing with those from GTS, internet
distribution buoy measurements and ship measurements (iQuam etc.) in
daytime.
Overall RMS error is evaluated comparing SGLI derived SST with those derived
from other satellite sensors and also comparing with those from GTS, internet
distribution buoy measurements and ship measurements (iQuam etc.).

7%

Overall RSM error is evaluated comparing SGLI derived SICA with those derived
from moderate and high spatial resolution satellite sensors and also with snow
5%and ice cover information obtained at ground stations etc.
10% (comparison with other Overall RSM error is evaluated comparing SGLI derived OKID with othersatellite products)
satellites’ same products and climate of related geophysical parameters derived
from the past observations.
5%3%

5K (comparison with othersatellite products andmeteorologicalmeasurements)
2K
1K

100%(evaluated withclimate of
temperature-snow grain
size relationship)
50%
30%

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Overall RSM error is evaluated comparing SGLI derived OKID with thosederived from moderate and high spatial resolution satellite sensors and also with
ice information obtained at ship etc.
Overall RMS error is evaluated comparing SGLI derived SIST with those fromother satellite sensors, air temperatures from GTS and ice buoys, and climate
datasets derived from the past observations.

RMS error is evaluated comparing SGLI SIST with those from in-situ radiometermeasurements and snow pit works, air temperatures from GTS and ice buoys.
Overall error is evaluated comparing SGLI derived SNGSL with other satellites’
products and climate derived from the past observations.
RMS error is evaluated comparing SGLI SNGSL with those from in-situ
radiometer measurements and snow pit works. And also consistentcy is
confirmed with other satellite product as an aid.

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Common note:*1 The "release threshold" is minimum levels for the first data release at one year from launch. The "standard" and "research" accuracies correspond to full- andextra success criteria of the mission respectively. Accuracies are shown by RMSE basically.
Land note:*L1 Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with AOT@500nm less 0.25
Atmosphere note:*A1 Comparison with in-situ observation on monthly 0.1-degree*A2 Vicarious val. on sea surface and comparison with objective analysis data
*A3 Inter comparison with airplane remote sensing on water clouds of middle optical thickness*A4 Release threshold is defined by vicarious val with other satellite data (e.g., global monthly statistics in the mid-low latitudes)*A5 Comparison with cloud liquid water by in-situ microwave radiometer*A6 Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence and large error of the ground measurements)*A7 Estimated by experience of aerosol products by GLI and POLDER

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Data Providing Service

Chapter 5 Data Providing Service
In principle, GCOM-C/SGLI products are provided through G-Portal (Globe Portal System:

https://gportal.jaxa.jp/gpr/). G-Portal is a portal system to provide all earth observation
satellite data processed at/provided by JAXA, and GCOM-C/SGLI standard products andnear-real time products are provided through this system to users via internet. For details

such as data search or ordering of GCOM-C/SGLI products, please refer to G-Portal User’s
Manual.

https://gportal.jaxa.jp/gpr/assets/mng_upload/COMMON/upload/GPortalUserManual_en.pdf

5.1 Product Provision Policy

GCOM-C products are available to users shown in 5.1.1 For details of the right or usage

condition of GCOM-C data, please refer to “GCOM-C Data Distribution Policy.”
5.1.1 User Categories

Users are categorized into alliance organizations or general users as shown below. The data

categories available for users are shown in Table 5-1.

(1) Alliance Organizations

Organization and researcher contributing to calibration and validation of GCOM-C/SGLI andresearch of climate change.

Organization and researcher adopted in research announcement (RA)Organization owning weather forecast and climate model.

Organization verifying the utility of using the information of fishing, oceanographic conditionand sea route management etc.
(2) General Users

General users other than the above.
Table 5-1 Provision of GCOM-C/SGLI Products

Level1A Product

Level1B Product
Level2 Product
Level3 ProductAlliance
Standard /Standard
Standard
Standardorganization
Near-real time
/Near-real time
General user
Standard
Standard
Standard
Standard(*)(*) JAXA screens the application for “special user” submitted by a user and makes near-real time products
available for the users JAXA approves.

5.1.2 Product Provision Procedure

G-Portal has a storage area from which users can obtain the latest data. The storage area

and the storage period of the latest product and near-real time product are shown in Table
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5-2. The storage period may be modified for each product. In addition, the access authorityof near-real time products is as shown in Table 5-1. Products outside of the storage period

can be obtained by order within the range of generation management. Ordered products
such as trimming/format conversion etc. are also provided via G-Portal.

Table 5-2 G-Portal Product Storage Area/Storage Period (Real time Storage)
Product

Standard Level1A

G-Portal Storage Area

Standard Level1B
Standard Level2

Scene
Tile

Near-real time level1B
Near-real time level 2

31 days
31 days

For latest product

GlobalStandard Level3

Storage Period

31 days(*1)

Entire period

Entire period

For near-real time
product

Entire period
7 days7 days(*1) Some types of the scene produdts are stored over an entire period.

5.1.3 Processing Version

JAXA releases GCOM-C/SGLI data after maintaining the algorithm of GCOM-C continuously

and carrying out trial process and calibration/validation using SGLI data. The version 1 ofprocessing algorithms is given GCOM-C/SGLI product are released. After the first data
release, the algorithm version will be updated every 1.5 years for better product accuracy.

Upon re-processing along with such upgrade, products with different processing version may

be generated in the same sensor/period/obsevation area. Since the products processed by

the latest algorithm are provided as a general rule (refer to 5.1.4 for generation management),
it is necessary to pay attention to the processing version when obtaining and referring to data.

Each processing version can be identified by the product version given to the file name(granule ID). Please refer to Table 4-3, Table 4-10, and Table 4-11 for granule ID.
5.1.4 Generation Management of Products

Only 1-generation management, which is the latest generation management, is the basic

concerning GCOM-C products of G-Portal. However, GCOM-C re-processing is planned forevery predetermined period backward and thus there is a period in which the second
generation exists in a phased manner.
5.1.5 Terms of Use

For the details of data provision policy and handling of personal information of G-Portal,
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please refer to JAXA’s site policy (http://www.jaxa.jp/policy_j.html).
5.2 User Tool

SGLI user tool is provided for users. The user tool is distributed for free of charge basically,

and individual user support for the tool is not provided as the online help etc. is prepared. The

tool is available at G-Portal. Table 5-3 shows the overview of the user tool functions. Pleaserefer to “SGLI User Tool Instruction Manual” for more details.

Function
Image Display
Zoom In/Out/Transfer
Data triming

Format Conversion

Movie File
Conversion
Annotation Display
Help
Batch Processing
Function

Table 5-3 SGLI User Tool Function

Description
Obtains observation data and latitude/longitude information from SGLI products,
and displays the image on the screen by the projection method corresponding to
the product.
Zooms in/out the specified point in the image displayed on the map.
To the image displayed on the map, outputs the area specified by mouse
operation in the supported file format by using the format conversion function.
Outputs the data of the specified area of the product displayed on the map in thefollowing file formats:・Binary format
・CSV format
・KML (KMZ) format
・Image format (JPEG, TIFF, BMP, PNG)
・GeoTiff format
・NetCDF format
・HDF5 format
Obtains SGLI product and outputs as a movie file (AVI format/KML (KMZ) format/MPEG2 format).
Displays the meta information stored in the product displayed on the map. Also,
displays the product information (channel, observation time; only for a case of
single channel display) and latitude/longitude (and observation value) at theposition of the image specified by mouse operation at the bottom frame of thescreen.
By menu operation, displays the document describing the user tool operation and
FAQ.
Records the operation by the user tool and outputs it as the command history andexecutes the command as a batch processing by reading and executing thecommand history.

5.3 I/O Toolkit

SGLI product I/O toolkit (SGTK：SGLI Toolkit) is provided for users to enable the use of C

language or Fortran language programs. SGTK is used for easy access to SGLI product dataof HDF. This tool is available at G-Portal. For details of installation and usage, please refer to“SGLI product I/O Toolkit Instruction Manual”.

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Appendix

Annex 1 Acronyms
Acronyms

Description

AMSR2

Advanced Microwave Scanning Radiometer 2

ADEOS-Ⅱ

ADvanced Earth Observing Satellite -Ⅱ

ALOS

ADEOS
AOCS
APID

Advanced Land Observing Satellite

ADvanced Earth Observing Satellite

Attitude and Orbit Control Subsystem

Application Process IDentifier

ASD

APID Sorted Data

AT

Along Track

ASP

ATBD

AVNIR-2

Analog Signal Processor

Algorithm Theoretical Basis Documents

Advarced Visible Near Infrared Radiometer 2

BiRS

Bi-directional Reflectance Simulator

CAPCOM

Comprehensive Analysis Program for Cloud Optical Measurements

BRDF
CCD

Bidirectional Reflectance Distribution Function
Charge Coupled Device

CCE

Cooler Control Electronics

CLAUDIA

CLoud and Aerosol Unbiased Decision Intellectual Algorithm

DEM

Digital Elevation ModelCDA
CT

DISORT

Cooler Dewar Assembly

Cross Track

DIScrete Ordinate Radiative Transfer

DM

Deployment Monitor subsystem

DSP

Digital Signal Processor

ELU

ELectronic Unit

DSNU
EearthCARE/CPR
EORC
EPS

Dark Signal Non-Uniformity

Earth Clouds, Aerosols and Radiation Explorer/Cloud Profiling Radar
Earth Observation Research Center

Electrical Power Subsystem

EQA

sinusoidal EQual Area

ESA

Earth Sensor Assembly

EQR
EVI

FAPAR

EQuiRectangular

Enhanced Vegetation Index

Fraction of Absorbed Photosynthetically Active Radiation
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FFT

Fast Fourier Transform

FLiES

Forest Light Environmental Simulator

GCOM-C

Global Change Observation Mission - Climate

GCOM

GCOM-W

Global Change Observation Mission

Global Change Observation Mission - Water

GCP

Ground Control Point

GLI

GLobal Imager

GEOSS

Global Earth Observation System of Systems

GN

Ground Network system

GOSAT-2

Greenhouse gases Observing SATellite-2

GOSAT

Greenhouse gases Observing SATellite

GPM/DPR

Global Precipitation Measurement/Dual-frequency Precipitation RadarGPS

Global Positioning System

GTSGlobal Telecommunication System

HDF

Hierarchcal Data Format

G-Portal
GPSR
HCE

Globe Portal system

GPS Receiver

Heater Control Electronics

HK

House Keeping

IFOV

Instantaneous Field Of View

IPCC

Intergovernmental Panel on Climate Change

HPLC
IOP

High Performance Liquid Chromatography
Inherent Optical Properties

IRS

InfraRed Scanning radiometer

IRS-SRUIRS Scanning Radiometer Unit

IRS-ELUIRS Electronic Unit

ISCCP

International Satellite Cloud Climatology ProjectI-DSP

IRS Digital Signal Processor

KSAT

Kongsberg Satellite Services AS

I-ASP
JAXA

IRS Analog Signal Processor

Japan Aerospace Exploration Agency

LAI

Leaf Area Index

LSTLand Surface Temperature

MDHS

Mission Data Handling Subsystem

LED
LUT

MDR

Light Emitting Diode
Look-Up Table

Mission Data Recorder

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MLI

Multi Layer Insulation

NAP

Non-algae particles

MODIS

MODerate resolution Imaging SpectrometerNASA

National Aeronautics and Space Administration

NDBC

National Data Buoy CenterNASA/GSFC

NASA Goddard Space Flight Center

NDSI

Normalized Difference Snow Index

NIR

Near InfraRed

NDVINormalized Difference Vegetation IndexNP

Non PolarizedOB

Optical Black

PCD

Payload Correction Data

PDL

Solar Array Paddle Subsystem

NP-ASP
OCTS
PD

PGCP
PL

PL-ASP

Non Polarized Analog Signal Processor

Ocean Color Temperature ScannerPhoto Diode

Precise Geometric Correction Parameter
PoLarized

PoLarized Analog Signal Processor

POL

POLarization

RA

Research Announcement

RMS

Root Mean Square

SGLI

Second-generation Global Imager

SNR

Signal to Noise Ratio

SSA

Single Scattering Albedo

PS

RCS
SDI

SMCU
SRU
SST

STR
STT

Polar Stereographic

Reaction Control Subsystem
ShaDow Index

Scan Motor Control Unit

Scanning Radiometer Unit
Sea Surface Temperature
Structure Subsystem
Star Tracker

SWIR

Short Wave InfraRed

TAIInternational Atomic Time

S-ANT
TCS

S-band Antenna

Thermal Control Subsystem
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TIR

Tharmal InfraRed

TT&C

Telemetry, Tracking and Command Subsystem

UT

Universal Time

VNR

Visible and Near Infrared Radiometer

VNR-SRUVNR Scanning Radiometer Unit

UTC

Coordinated Universal Time

VNR-ELUVNR ELectronic Unit

VRI

Vegetation Roughness Index

V-DSP

VNR Digital Signal Processor

WGS84

World Geodetic System 1984

X-ANT

X-band ANTenna

Annex 2 Related Information
Annex 2.1 Bibliography(1) GCOM-C Climate Change Observation Satellite (Briefing document) (Japanese)(2) SGLI Level 1 Product Format Description(3) SGLI Higher Product Format Description(4) SGLI Algorithm Theoretical Basis Documents (Japanese)(5) SGLI Level 1 Implementation Processing Document (Japanese)(6) Level 2 & Level 3 Algorithm Theoretical Basis Documents(7) SGLI User Took Instruction Manual(8) G-Portal Earth Observation Data and Information System User Manual(9) GCOM-C Data Distribution Policy(10) SGLI Products I/O Toolkit Instruction Manual(11) SGLI Characrerization Guide (Japanese)

Annex 2.2 Relevant Website■ JAXA’s site(1) JAXA website

http://global.jaxa.jp/(2) Global Change Observation Mission – Climate “SHIKISAI” (GCOM-C) Webpage
http://global.jaxa.jp/projects/sat/gcom_c/
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Appendix(3) GCOM-C/EORC
https://suzaku.eorc.jaxa.jp/GCOM_C/index.html■ Overseas Site

(1) HDF Homepage

http://www.hdfgroup.org/

Annex 2.3 Contact Inforation

Please contact the following for data provision and user handbook information.
2-1-1, Sengen, Tsukuba-city, Ibaraki 305-8505, Japan

National Research and Development Agency Japan Aerospace Exploration Agency
Earth Observation Data and Information System (G-Portal) Support OfficeE-mail：z-gportal-support[*]ml.jaxa.jp(Note) Please change [*] to @.

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