Looking Ahead: The Water’s Edge of the USGS National Land Imaging Program

FINAL DRAFT September 2022

LOOKING AHEAD: 

THE WATER’S EDGE OF THE USGS 

NATIONAL LAND IMAGING PROGRAM

Building the Global Commons of Earth 

Observation Data

A Report of the National Geospatial Advisory Committee

Landsat Advisory Group

September 2022
Looking Ahead: The Water’s Edge of the USGS National Land Imaging Program

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National Geospatial Advisory Committee (www.fgdc.gov/ngac) i

Table of Contents

1.0 Executive Summary........................................................................................................... 1

2.0 Introduction...................................................................................................................... 2

3.0 The Earth Observation Landscape..................................................................................... 3

4.0 Landsat and the ‘Global Virtual Constellation’ .................................................................. 5

5.0 Evolving the Role of the National Land Imaging Program ................................................. 6

6.0 Leading Global Land Imaging Analysis Ready Data ........................................................... 7

6.1 Calibration Standards, Tools, and Leadership..............................................................................7

6.2 Global Land Imaging Analysis Ready Data ...................................................................................8

6.3 ARD Harmonization Software Tools............................................................................................9

7.0 Going Beyond Analysis Ready Data..................................................................................10

8.0 LAG Findings and Recommendations ...............................................................................12

Table of Figures 

Figure 1: The petabytes (PB) of Earth Observation (EO) data being collected have grown 

significantly and are projected to continue growing. ......................................................................4
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1.0 Executive Summary

USGS asked the Landsat Advisory Group (LAG), subcommittee of the National Geospatial 

Advisory Committee (NGAC), to prepare a paper that explores what earth observation 

products, in forms of data, algorithms or workflows, the federal government should 

appropriately provide to optimize benefit to the public, particularly in light of the industry 

transformations. As such, this paper prospects the scope to which USGS can maximize its 

productivity and optimize its leadership role in this transforming environment of evolving 

technology and expanded providers and users. This report examines the current state of earth 

observation collection, distribution, and use, reflects on the quality and content needs of the 

global community, and highlights the following findings and recommendations:

Finding #1: The earth observation landscape is being fundamentally transformed by two 

major trends. The first is an explosion in the accessibility of earth observation data, both 

from new sensors coming online and its availability on cloud platforms. The second is the 

rise of ‘Analysis Ready Data (ARD) Harmonization’ products that promise to enable the 

scientific community and non-expert data users to spend more time conducting research 

into urgent issues in earth system science, such as climate change, food-energy-water 

nexus, and other priorities, rather than data cleaning and standardization procedures. 

Finding #2: These two trends open the possibility for a completely harmonized ‘Global Land 

Imaging Analysis Ready Data commons’. This would mean that diverse disaggregated 

sensors from different countries and companies can all contribute to a coherent, consistent 

record of change on earth. Such data commons allow integrated digital representations of 

the physical, ecological, and human environments to facilitate shared views and solutions to 

local, regional, and global challenges. 

Finding #3: Harmonized ARD baseline can provide a foundation for many higher level data 

products like biophysical measurements, land classifications and change monitoring. The 

USGS science community is uniquely positioned to be able to supply these measurements of 

the state of the earth’s health, and the need for such measurements is greater than ever 

before. The massive longitudinal observations of the earth and the ever innovative machine 

learning algorithms synergize the advances in pattern mining and knowledge discovery of 

the Earth’s past, present, and future. 

Recommendation #1: USGS should work with NIST, OGC and ISO to create the standards 

that would underpin harmonized analysis ready data. This will likely require CEOS agencies’ 

resourcing, as well as targeted work on Cal/Val and WGISS to support international 

standards.

Recommendation #2: Standards must account for sensors of differing quality than Landsat 

and Sentinel 2, incorporating them into the global commons with clear communication of 

quality. 
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Recommendation #3: USGS should continue to produce higher level science products built 

on the harmonized data baseline provided to other users, and the USGS should seek out 

international government, likely in participation in CEOS, and public-private-partnerships to 

encourage a wide range of valuable measurements of our planet’s health. USGS should 

maintain open communication with the commercial remote sensing industry – both those 

that collect data and those that focus on data analytics – to ensure synergy between 

government developments, academic research, and trends in the commercial sector.

2.0 Introduction

In recent years there has been a remarkable change in the volume of data, ease of access, and 

processing power for Earth Observation data. This has expanded the diversity of applications, 

products, and services to quite non-homogeneous user groups. While the amount and types of 

remotely sensed data now available from a growing number of providers overwhelm most 

users, some difficulty addressing the variety has been mitigated by harmonization efforts like 

Harmonized-Landsat-Sentinel (HLS) and Sen2Like. These products abstract out the more 

challenging parts of remote sensing to create cross-sensor Analysis Ready Data, facilitating 

work with the volume of data being generated. Collectively (along with other data fusion efforts 

driven by the research and commercial landscapes), these efforts point to the near term 

realization of harmonized/standardized earth information based on the collective inputs of 

Landsat, Sentinel, and other Earth observation platforms.

The National Land Imaging Program leadership from the U.S. Geological Survey (USGS) 

requested that the Landsat Advisory Group (LAG), a subcommittee of the National Geospatial 

Advisory Committee (NGAC), prepare a paper that explores what earth observation products, in 

forms of data, algorithms or workflows, the federal government should appropriately provide 

to optimize benefit to the public, particularly in light of the industry transformations. With 

increasing capability and capacity of national, academic, and commercial actors in the Earth 

observation community, the roles and responsibilities will evolve amongst actors. This shifting 

boundary between the federal government's responsibility and those of new operational actors 

will be continuous, and here we introduce the analogy of the “Water’s Edge” in recognition of a 

growing, dynamic community and how each actor interfaces with one another. We encourage 

every actor to embrace the changing landscape and adapt programs and plans over the years to 

increase public benefit and create a more efficient Earth Observation Enterprise.

These trends shift USGS from being the primary source of global monitoring imagery to a key 

leader within the global ‘virtual constellation’. This builds upon Landsat’s unprecedented record 

of collecting and distributing, multi-decadal calibrated observations of the earth’s land surface. 

The Landsat program has improved the spatial and thematic accuracy of its imagery over the 

fifty years of operation as well as increased the spatial and temporal resolution. It has further 

enhanced the quality of those resolutions by increasing the spatial and temporal resolution of 

this record by combining Landsat’s data with other open global monitoring missions. The NLI’s 

core mission is to provide Federal and public access to a collection of authoritative remote 

sensing datasets that best enable their exploitation. Among those datasets are USGS products 
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such as Level-1 satellite quantized image data, Level 2 geophysical parameter products (e.g., 

Surface Temperature, Surface Reflectance, and Aquatic Reflectance), and the other derivative 

Level 3 sciences products.

1 With Landsat Collection 2, these products have been spatially 

aligned to enable improved time-series analysis in support of applications such as climate 

change studies. As the referenced website clearly illustrates, the “Water’s Edge” of data 

content, product development, and application focus cannot remain static. Questions 

necessarily emerge. Should users anticipate major changes in the total scope of the remote 

sensing and GIS community’s work with the inclusion of newer sensor systems? Will any 

change in focus to the more complex Level “X” products require different expertise than what 

USGS already has? This paper examines those issues, offering insights and recommendations as 

well suggesting further study.

3.0 The Earth Observation Landscape

There are two transformative trends occurring in the Earth Observation (EO) domain. The first 

is a phase shift in the volume of data, ease of access, and processing power for EO data. More 

earth observation satellites and collection platforms are being engineered, launched and/or 

flown than generally anticipated only two decades ago. Each of them can have far more 

capable and diverse sensors, acquiring much more complex data than previously. Petabytes of 

new data arrive every single week. The promising use of both onboard processing and cloud 

computing has enabled some initial editing or fusion. Remote sensing workflows are 

increasingly shifting to the cloud, with use of Google Earth Engine, Microsoft Planetary 

Computer and government systems being developed on AWS that enable global scale 

computations. In turn, this revolutionizes access to EO data, as but one example, with copies of 

Landsat and Sentinel missions available on every cloud. Users no longer need to select which 

images they want and then wait for the download to their desktop computer. Instead, cloudnative geospatial approaches enabled by standards like Cloud-Optimized GeoTIFF (COG) and 

SpatioTemporal Asset Catalogs (STAC) enable users to process the data in place, with far more 

computing horsepower than they ever imagined for their desktop systems. Furthermore, the 

cloud-computing platform also facilitates standardization and community commons for 

innovations by algorithm sharing and social coding. The copies of the missions on different 

clouds and diverse machine learning models, however, raise the concerns about data 

provenance, data authenticity and integrity, as well as confidence and trust in algorithms and 

models. In its leadership role, USGS could greatly influence the establishment of standards to 

assure authoritative data and cloud-computing workflows as it asserts responsibility there at 

that water’s edge. The process for developing and sharing open source algorithms, models, and 

1 https://www.usgs.gov/core-science-systems/nli/landsat/landsat-science-products?qtscience_support_page_related_con=2#qt-science_support_page_related_con
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code should ascribe to international standards and best practices with process improvement for 

establishing emerging standards in the Earth Observation ecosystem.

Figure 1: The petabytes (PB) of Earth Observation (EO) data being collected have grown significantly and are

projected to continue growing. 

The second transformative trend includes huge strides in making imagery easier to work with. 

Traditional remote sensing workflows would require a user to perform atmospheric correction, 

align each image in a time series so the pixels match, mask out clouds, etc. Landsat has 

established a community leading model with ‘Analysis Ready Data’, which takes the preprocessing steps everyone used to do on their own and puts them in a deliverable product 

ready to be confidently used to address myriad needs and issues. Analysis Ready Data broadens 

societal applications and expands the value of remote sensing imagery, which would otherwise 

have limited use outside environmental or biological sciences. A subsequent phase of this initial 

pre-processing aims to ‘harmonize’ multiple sensors so their radiometry becomes compatible, 

enabling the same algorithms to work on any data gathered from any of the input sensors. The 

two leading efforts on this ARD Harmonization (ARD-H) front have been Harmonized-LandsatSentinel (HLS) and Sen2Like, both bringing together Landsat and Sentinel 2 into a single stack of 

Analysis Ready Data. The Sen2Like product takes the best of Landsat and Sentinel 2 data and 

fuses it into a data product that is at the Sentinel 2 spatial resolution (10m) and higher cadence 

with the combination of both missions. The potential of harmonization and fusion, to transform 

the expanding remote sensing landscape, cannot be overstated, as it enables any satellite with 

similar capabilities to participate in the shared mission of global monitoring combining the best 

qualities of each sensor. This will establish the foundation for an even greater revolution in 

making remotely sensed information accessible to a wider group of users, since the harmonized 
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ARD layer can enable fusing key biophysical and land classification measurements to synthesize 

information products as well as monitoring changes of our planet. These processes can extract 

from all the complexities of remote sensing the needed and valued data products that provide 

answers and drive decisions.

4.0 Landsat and the ‘Global Virtual Constellation’

Landsat has provided the longest continuous scientific record of global earth observations, with 

its sensors providing a ‘gold-standard’ reference that many other missions rely upon.2 The 

latest developments of harmonization see new missions like the Copernicus program’s Sentinel 

satellites not ‘competing’ with Landsat, but instead enhancing the global earth observation 

scientific record with sub-weekly 10-meter observations. The software advances that power 

this harmonized Analysis Ready Data (ARD-H) enable us to use diverse spectral and spatial 

resolutions to get a more complete picture of what is happening on earth. As a matter of 

scientific practice, the resulting multi-sensor ARD-H data products hold the promise of enabling 

the scientific community and Landsat data users to spend more time conducting research into 

climate change and other scientific priorities, rather than data cleaning and standardization 

procedures. Both Landsat OLI and Sentinel MSI sensors have unique spectral bands that add to 

the diversity of any product derived from the harmonized and/or fused sources that include 

Landsat.

This enables a true “commons”, where any new public dataset can add to the overall 

measurement of what is happening on earth. This portends a future where any satellite can 

contribute to Landsat’s mission - providing the longest continuous record of earth observation 

data - as long as its data meet certain calibration standards to enable them to be harmonized. 

In addition, two ARD products that are not harmonized, but are interoperable (e.g., can be 

corrected to the same ground geometry) can be conjoined to add value. This could result in a 

‘data cube’ that increases in spatial, temporal, and spectral resolution as more sensors come 

online, strengthening the time series and heightening the reliability of any predictive 

assessment.

3 As the number of launches increase because the costs of building spacecraft and 

launching them decreases, diverse nations, research organizations and companies will find it 

easier to contribute to the anticipated and desired Global Land Imaging Analysis Ready Data 

“commons.” That concept opens the possibility of disaggregated sensors, where thermal, multispectral, hyperspectral or RADAR sensors are flown on government or commercial satellites, 

with software able to combine all readings of the earth into a coherent, consistent record of 

change. It could even be possible to incorporate higher resolution imagery from planes, highflying drones, and balloons into this harmonized global data commons.

2 https://www.fgdc.gov/ngac/meetings/october-2020/ngac-paper-landsat-data-community-standard-for.pdf

3 See recommendations made by the Landsat Advisory Group in 2018.

 https://www.fgdc.gov/ngac/meetings/april-2018/ngac-landsat-data-cube-feasibility-for-forecasting.pdf 
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5.0 Evolving the Role of the National Land Imaging Program

In this future where many are contributing raw pixels to the Global Land Imaging Analysis Ready 

Data record, the role of the National Land Imaging Program should evolve to better leverage its 

unique strengths in service of the needs of its community of users. The two pillars of the USGS 

Landsat operations program have been serving as the ‘gold’ reference for calibration, and 

stewarding the longest record of earth observation. These should remain the pillars of what the 

USGS does, but that agency must continue to transform from being the sole ‘source’ of 

medium-resolution earth-observing data and must effectively advance into a trusted developer 

of quality methods and standards and an advocate for the global commons of monitoring 

sensors, in service of all its direct and indirect users. The core product focus should be the ‘gold’ 

reference implementation of interoperable earth information ARD products. As a matter of 

scientific practice, this direction holds the promise of enabling the scientific community and 

Landsat data users to spend more resources and cultivate expertise conducting research into 

climate change and other environmental, social, and public good priorities. Rather than being 

beset by data provenance concerns, data quality checking and cleaning, and seeking 

standardization procedures from an expanding array of sensors, USGS should strategically 

emerge from the avalanching pixels of data as an active leader and advocate in the creation and 

adoption of needed standards to harness the plethora of new sensors and platforms. 

USGS should also encourage delivery of cloud-based, deep-learning processing software tools 

that openly enable operating within the expanse of the highest quality global virtual 

constellation. An open source policy that shares algorithms, models, and codes for data 

productions and analytical workflows on Landsat projects by USGS scientists and sponsored 

researchers can enhance USGS productivity in two ways. First, open algorithms and codes allow 

users to produce customized data products. Second, users can contribute to algorithm 

improvements and novel modeling techniques for new Landsat and multi-sourced products and 

analytics. The resulting data output will upgrade and enhance the Global Land Imaging Analysis 

Ready Data to ever improving spatial, temporal, and spectral resolutions as more sensors come 

online. 

These standards development and instantiation efforts should be actively supported by 

government agencies, which can promulgate them and then embed their required use in 

procurement decisions. This approach would follow in the path of initiatives like IETF and 

ICANN where the government funds an appropriate neutral mission-driven organization to 

facilitate the global standards-creation process, while serving as a key leader and stakeholder in 

the effort. The resulting effort enabled a growing, dynamic community of government, 

academic and commercial actors to build the foundation for the internet to become a public 

utility and a new domain for commerce. In the geospatial community, the Open Geospatial 

Consortium is a likely choice for a forum to build consensus on critical science and technological 

infrastructure issues. Bringing actors together to create candidate standards through ISO will 

allow the global and national standards bodies to set the fundamental standards to ensure full 

global compliance. Instantiation of a successful suite of earth observation standards demands 

ensuring a robust ecosystem of software tools that support the standards. The US Government 
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is already leading in these domains, such as with NOAA’s/NLI’s involvement in the US Group on 

Earth Observations (USGEO) and NASA’s role in the Open Data Cube4 internationally. NOAA 

may also consider taking on an increased diplomatic role to develop synergies between Landsat 

(and other U.S. Earth Observation assets), the European Sentinel program, and EO datasets 

from other programs including JAXA and ISRO at the Committee on Earth Observation Satellites 

(CEOS). These flagship efforts are increasingly utilized in combination by the global EO user

community, and NOAA is uniquely positioned to broker conversations around standards and 

user experience and help guide the roadmap of global sensors in service of a coherent record of 

earth information to be used by all.

6.0 Leading Global Land Imaging Analysis Ready Data

The following products and initiatives demonstrate how this shift would further USGS’s

leadership role in a Global Land Imaging ARD effort.

6.1 Calibration Standards, Tools, and Leadership

A key component of making Landsat the ‘gold’ standard is an array of ground calibration sites 

that enable precise measurements against established and well-defined targets to enable 

continued correction of sensors in space. Many diverse commercial and governmental 

organizations already leverage NASA calibration resources such as the Goddard Aeronet 

Network and many of the methods developed for Landsat to do their own calibration. The 

instantiation of EROS Calibration Center of Excellence (ECCOE) is largely providing these 

services today. Improved calibration methodologies, working with university and international 

cal/val sites and with participation/leadership in community-of-interest organizations. The 

leadership role in setting standards and building tools for ground calibration should extend in 

the future, to enable any country or university to contribute more high-quality calibration sites 

to help any sensor reach the quality required to be part of the Global Land Imaging Virtual 

Constellation. One could imagine a ‘radcal site in a box’, that brings the tools, processes, and 

training to enable anyone to create a radcal site that becomes part of the global sensor 

calibration commons. These calibration sites are critical to enable interoperable data collections 

and calibrate biophysical properties and measurements needed for the various levels of data 

products. Consider the impact to the global community for various opportunities to collaborate 

by new actors and the timely impact to the science missions of organizations like USGS and the 

issues associated with the global climate environment.

USGS should aim to set the ‘gold’ reference implementation of a sensor that meets the NLI 

standard of excellence. With the proliferation of new earth observation satellites in space, 

USGS could certify these sensors and data products, or explore the best way to help the virtual 

constellation with a ‘reference satellite’. This could be a smaller instrument with lower 

resolution that focuses on providing crossovers for others to validate against, perhaps using a 

hyperspectral sensor. Crossovers for calibration and data continuity is not a novel concept. The 

4 https://www.opendatacube.org
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most recently launched Landsat 9 included crossovers with Landsat 8 to confirm quality in 

positional and spectral accuracies.

6.2 Global Land Imaging Analysis Ready Data

As mentioned above, the primary product focus should be on the ‘gold’ reference development 

and implementation of a standardized product suite which enables cross-sensor/observation 

exploitation by regular end-users and not only highly-skilled remote sensing scientists. With the 

introduction of Collection 2, USGS has made Landsat data across missions consistent and 

analysis-ready, enabling end users to exploit with ease. With this leadership, USGS shall 

encourage other earth observation data collection, storage, and exploitation users across the

US government to be interoperable and analysis-ready. Leaning further into a standard-setting 

mode would require collaboration with NASA and ESA to seek a framework that can sustain 

robust HLS products, leading cross-sensor harmonization and standardization, and ensuring the 

entire Landsat data archive is compatible with global analysis-ready data standards to enable 

multi-sensor harmonization for an end user. This should serve as the global reference 

implementation that meets the quality requirements of the Landsat constellation users. This 

should ideally be able to harmonize the entire Landsat historical record with the modern 

analysis-ready data spectral targets, like those of HLS. Though the data product from any 

remote sensing collection itself is important, even more important will be the standards 

defining the quality standards that a ‘gold’ harmonized Global Land Imaging ARD data set must 

meet, including metadata tracing for scientists to understand the ‘error bars’ of diverse source 

data. The future of virtual constellations should not rely on every new sensor being able to 

meet the high standards set by Landsat and Sentinel 2, but there must be comprehensive 

metadata with provenance tracking to all source data so that users can understand exactly 

what appropriate use of the data entails. What are the reproducible and/or repeatable 

scientifically credible processes, anyone can expect from data use? The CEOS CARD4L 

specification (with ‘target’ metadata implementation) is an effort to define the metadata 

content which improves source interoperability and provenance.

Successful development of Global Land Imaging Analysis Ready Data can empower diverse 

commercial operators, academic programs, and nation states to credibly contribute to the 

global commons of earth observation data. A clear target will be to establish standards for data 

holders to clean and harmonize their data to the ‘gold’ standard, and ideally there are also 

standards that set lower quality levels to provide a path forward for any sensor to work 

towards the ‘gold’ target. The CEOS ARD certification process provides an appropriate baseline 

to build upon here, but more resources or a “lighter” and/or more automated approach is 

needed for lower-level certification. It is important that the standards are sensor agnostic, 

working at a high enough level that any new sensor meeting the appropriate calibration targets 

will be easily incorporated into the global commons of analysis-ready earth observation data. 

For example, the original Planet Labs’ Dove sensor red channel was different from the Planet 

Labs’ Super Dove and Sentinel 2’s red channels, which cover a wider spectral range. This 

doesn’t mean that it should be excluded from the global land imaging record - any 

measurement in the passive optical EM spectrum is relevant to earth's changes over time. So, 
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an Ideal standard would be able to accommodate any spectral information, and appropriately 

leverage its unique spectral characteristics. Thus, HLS should become HLS + X, where X is 

virtually any other sensor that shares the same mission, easily incorporating diverse spectral 

characteristics into a harmonized output. Achieving this clearly requires a non-trivial amount of 

work, but the benefit to the world is immense, enabling more users to get insight from remote 

sensing than do today.

An expanded role for USGS to lead the global commons of earth observation harmonized data, 

here at the "water's edge" of so many emerging technologies and global environmental needs, 

USGS can lead the global community with multi-national and public-private-partnerships. Those 

innovative partnerships will enable the purchase of data and services from commercial 

providers for government, academic, commercial, and public use to form the Global Land 

Imaging ARD commons. This could even include higher spatial resolution (and likely lower 

temporal resolution) aerial, drone, and tasked high resolution satellites. It should be possible to 

include national aerial programs like NAIP in the overall harmonized dataset, even serving as a 

target for upsampling images, as long as the processing error potential is clearly communicated 

to users. 

Beyond just setting the standards for the data itself, leadership on data accessibility must be a 

component of this USGS expanding role. The Open Geospatial Consortium has committed itself 

to the FAIR principles for data: Findable, Accessible, Interoperable, and Reusable. The user 

community commends the Landsat program for the accomplishments in making its collections 

incredibly accessible, embracing cloud-native geospatial approaches like COG and STAC, and 

enabling copies of its archive on every major cloud and in higher level tools like Google Earth 

Engine and Microsoft Planetary Computer. Continuing leadership and standard setting in this 

area cannot become lax.

6.3 ARD Harmonization Software Tools

One of the most important parts of making a standard usable and widely accepted is to ensure 

that the software that enables its creation and use is flexible, robust, and well-maintained. 

There are numerous algorithms used to create HLS, including atmospheric correction, cloud 

masking, spatial co-registration, BRDF5 normalization, band pass adjustment and more. Each 

algorithm should continue to be refined, upgraded, documented, and released in readilyaccessible libraries. These composable, trusted algorithms can be usable in a wide variety of 

open or proprietary tools, so it’s easy for anyone to create a new harmonized product or 

workflow. The software should also evolve to make it possible to incorporate other data 

sources into the harmonized analysis-ready data output. This ideally is a nice plug-in system, 

where every new data source can be coded as a ‘plugin’ after the addition of a couple of data 

sources past the core Landsat and Sentinel. Then an open source ecosystem can be encouraged 

to build up the harmonization transforms for diverse data sources.

5 Bidirectional Reflectance Distribution Function
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This software should be the same as that used in production environments to create the ‘gold’ 

standard reference product, so it is not just the libraries available to the global community but 

scientifically accurate and composable building blocks of a processing pipeline. Taking a 

leadership role in this means not just releasing the libraries but building up the open source 

ecosystem of diverse contributors, so the software becomes a part of the global commons, to 

allow new sensors to easily join the Global Land Imaging Analysis-Ready data cube.

7.0 Going Beyond Analysis Ready Data

The ‘users’ of the National Land Imaging program have always been experts in remote sensing 

and geospatial sciences who are able to work with imagery and extract insights from it. USGS 

has the potential to greatly expand its user base and tap into a broader community of experts 

to build consistent, reliable, trusted ‘variables’ (e.g., Level 4 data products) of the state of the 

earth’s health. The requirement for such measurements is greater than ever before, as 

governments need to certify ‘green’ bonds; companies want to prove that their corporate social 

responsibility programs are having a measurable impact; financial institutions seek more 

reliable ways to provide climate risk ratings to analyze companies against ESG targets; and civil 

governments install community planning tools (such as community resilience, star community, 

climate adaptation planning, or other planning templates) and environmental planning tools 

(drought analysis, water management, etc.). These organizations don’t necessarily have – or 

need to have - remote sensing experts, but with guidance, they could describe, help design, and 

employ decision-support tools, that they are assured have been enabled by trusted data 

sources. USGS must address head-on the question of authenticity in satellite imagery by 

incorporating measures that can assure a downstream user of Landsat data that it has been 

unadulterated. Tools and procedures that expose manipulation must be readily available. 

Making the USGS and the NLI the trusted actor for how to set up trustable measurements, with 

accepted methodology, will give a clear seal of approval to reliable scientific measurements 

facilitating trust in the indicators that power novel decision support tools. The resulting metrics 

would enable a hugely expanded economic benefit for the Global Land Imaging ARD, as it 

would make the record of the earth’s changes directly available to a much wider range of users.

USGS and NASA already have substantial experience providing these types of data product 

‘variable extensions.’ As an example, MODIS data products go beyond delivering only the 

calibrated imagery. Products like snow cover, burned area, chlorophyll-a and others have 

enabled earth science MODIS data users to focus more time and expertise completing scientific 

research and to avoid time in data preparation. Just consider how a wise and selective public or 

private investment in quality-consistent and standards-compliant Level 4 products, based on 

Harmonized Sentinel Landsat and its future evolutions, could advance a significant step-change 

in Earth observation science. Building the measurements on harmonized, multi-sensor analysisready data would enable each user to confidently start with a standard credentialed input for 

resulting products, tailored to a specific – perhaps quite complex – need. As the global virtual 

constellation improves in resolution (spatial, temporal and spectral), so too would the 
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measurements. Ideally users, whether earth scientists, economists, environmentalists, 

sociologists, community planners, and so forth, would also be able to access summaries of 

change over time in key measurements for key geographies (for example: countries, states, 

counties, municipalities) so that the data easily import into spreadsheets, business intelligence 

tools and cloud data warehouses for analysis by a wider group of users. 

Beyond just biophysical measurements, like the type that MODIS provides, there is the 

opportunity for USGS and other FGDC geospatial data providers to assert a leadership role in 

land cover classification and change detection and in refining the theme classification accuracy 

standards. For instance, the definition or semantic use of ‘forest’ may vary depending on the 

country and culture, thus making data interoperability more difficult when trying to accurately 

and consistently deliver a trusted data source. Acknowledging and handling language 

connotation is fundamental to accelerate the development of new decision-making applications 

for a wide range of national and international issues that range from environmental guidelines 

to law enforcement to emergency response. Analysts need to know things like ‘how much 

deforestation has happened in X country in the past decade’ or ‘what’s the urban growth rate 

and change in urban heat index for Y city’ or ‘what activity inhibits potable water’. The USGS 

need not enter the deeper societal or political debates that build on differing interpretations of 

the core variables. By assuming a respected, collaborative, and collegial leadership role within 

the standards community, the USGS can set – and be the strongest advocate for – the 

standards for categorization codes and analytical procedures that characterize the report of 

how and why the observed status of things in place change over time, following certain 

taxonomies and methods. USGS in its expanded global commons leadership role can help the 

community certify analytics as properly following a scientifically valid protocol (USGS’s 

recognition, providing film camera calibration certification in the past, is a noteworthy parallel). 

Such validated analytics would be valued immediately for financial and regulatory compliance, 

as but one example. USGS need not be the compliance authority, but instead should lead a 

public-private partnership to develop, test, and establish a certified process and then 

encourage other agencies, like FWS or FDA, to build out compliance applications powered by 

scientifically valid and reproducible indicators. 

This paper has mentioned the possibility of embracing public-private-partnerships, as part of 

understanding how the “Water’s Edge” might adjust in the future with cooperative, 

collaborative relationships. Public-private-partnerships (P3) are not all alike since they must 

adapt to the details of the specific partnering to address key objectives. Understanding both 

successful and flawed cases would be beneficial to the other recommendations of this paper. 

More understanding, gained by continuing to examine case studies of such partnerships, is 

necessary to meet both the public need for reliable information and the private benefit of 

delivering innovation as an on-going operational service. Previous NGAC efforts reviewing such 

partnerships for best practices should continue6 to add case studies to the 2020 work.

6 Advancing the NSDI through P3s and Innovative Partnerships December 2020 

https://www.fgdc.gov/ngac/meetings/december-2020/ngac-paper-advancing-the-nsdi-through-public.pdf
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National Geospatial Advisory Committee (www.fgdc.gov/ngac) 12

8.0 LAG Findings and Recommendations

As identified in the Executive Summary above, this paper provides the dialogue for the 

following findings:

1. The earth observation landscape is being fundamentally transformed by two major 

trends. The first is an explosion in the accessibility of earth observation data, both from 

new sensors coming online and its availability on cloud platforms. The second is the rise 

of ‘Analysis Ready Data (ARD) Harmonization’ products that promise to enable the 

scientific community and non-expert data users to spend more time conducting 

research into urgent issues in earth system science, such as climate change, foodenergy-water nexus, and other priorities, rather than data cleaning and standardization 

procedures.

2. These two trends open the possibility for a completely harmonized ‘Global Land Imaging 

Analysis Ready Data commons’. This would mean that diverse disaggregated sensors 

from different countries and companies can all contribute to a coherent, consistent 

record of change on earth. Such data commons allow integrated digital representations 

of the physical, ecological, and human environments to facilitate shared views and 

solutions to local, regional, and global challenges.

3. Harmonized ARD baseline can provide a foundation for many higher level data products 

like biophysical measurements, land classifications and change monitoring. The USGS 

science community is uniquely positioned to be able to supply these measurements of 

the state of the earth’s health, and the need for such measurements is greater than ever 

before. The massive longitudinal observations of the earth and the ever innovative 

machine learning algorithms synergize the advances in pattern mining and knowledge 

discovery of the Earth’s past, present, and future. 

The paper examines those findings and makes the following recommendations to move the 

community ahead, recognizing that the “water’s edge” of requirements and technology 

improvements will not remain static over time but that the leadership role must be a steadfast 

commitment to service users as data proliferate, new sensor systems emerge, and needs 

expand:

1. USGS should work with NIST, OGC, and ISO to create the standards that would underpin 

harmonized analysis ready data. This will likely require CEOS agencies’ resourcing, as 

well as targeted work on Cal/Val and WGISS to support international standards.

2. Standards must account for sensors of differing quality than Landsat and Sentinel 2, 

incorporating them into the global commons with clear communication of quality.

3. USGS should continue to produce higher level science products built on the harmonized 

data baseline provided to other users, and the USGS should seek out international 
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National Geospatial Advisory Committee (www.fgdc.gov/ngac) 13

government, likely in participation in CEOS, and public-private-partnerships to 

encourage a wide range of valuable measurements of our planet’s health. USGS should 

maintain open communication with the commercial remote sensing industry – both 

those that collect data and those that focus on data analytics – to ensure synergy 

between government developments, academic research, and trends in the commercial 

sector.

Acknowledgments

This paper was approved by the NGAC Landsat Advisory group (LAG) on July 28, 2022 and 

adopted by the NGAC as a whole on September 8, 2022. The LAG team developing this paper 

included Robbie Schingler, Planet Labs (Team Lead); Frank Avila, National Geospatial 

Intelligence Agency; Steve Brumby, Impact Observatory, Inc.; Roberta Lenczowski, Roberta E. 

Lenczowski Consulting; Anne Miglarese, Miglarese Consulting; Vasit Sagan, Taylor Geospatial 

Institute; and May Yuan, University of Texas-Dallas.