Truths cross-calibration uncertainty tool

The Committee on Earth Observation Satellites (CEOS) formed an Ad Hoc Team in 2019 to coordinate the focus of space agency resources on providing information solutions to coastal issues impacting society known as the CEOS Coastal Observations Applications Services and Tools (COAST) Team. CEOS-COAST is co-chaired by NOAA and ISRO and enjoys the dedicated participation of representatives of 34 other International Earth Observation (EO) satellite agencies. Since 2020, the international CEOS COAST team has intrepidly advanced EO solutions for several pilot regions, especially the Chesapeake Bay and the Bay of Bengal. Since early 2022, regional stakeholders have been engaged in product co-development with the team - to ensure user needs are met and useful products which benefit society are created. Overall CEOS COAST progress will be reported in 5 initial thematic areas: Coastal/Inland Flooding, Coastline mapping, Coastal Bathymetry, Coastal Eutrophication and Coastal Sediments/Turbidity - with an emphasis on results in the Chesapeake Bay as a pilot region. Future work planned for the next CEOS COAST biennium and ties with the United Nations Decade on Ocean Science for Sustainable Development will be introduced.

CEOS contributions to informing energy management and policy decision making using space-based Earth observations

Heterogeneous Earth Observation (EO) satellite data is available from many agencies. In order to make best use of these data, interoperability for data access is very important. In this paper, the importance of data access interoperability and the work of CEOS (Committee on Earth Observation Satellites) WGISS (the Working Group on Information Systems and Services) to improve data access interoperability are described.

Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

This paper describes a pilot project effort under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS) /Working Group on Information Systems and Services (WGISS) and originally moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during the past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to ongoing in 2012. The pilot effort has been fostered by CEOS to facilitate international efforts to promote satellite sensor data interoperability. In particular, the group has been making use of a technology effort call SensorWeb being developed at NASA which leverages Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to facilitate various satellite and ground sensor interoperability. The group has made use of such satellites such as Earth Observing 1, Terra/Aqua MODIS and the Canadian Space Agency (CSA) Radarsat together with various ground sensors such as river gauges in Namibia and models such as Global Disaster Alert and Coordination System (GDACS) from Joint Research Center (JRC) from the European Commission. Finally, the group has been experimenting with integrating a large Cloud Computing service provided by the Open Cloud Consortium (OCC) with the SensorWeb to provide management and distribution of the large data sets for emergency workers.

Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua spacecraft have been successfully operating for nearly two decades and providing complementary observations of the Earth's land, ocean, and atmosphere. Although the two MODIS instruments view the entire Earth's surface once every 2-3 days, simultaneous views between them are limited due to their varying orbits. Therefore, the intercomparison between these two instruments has been previously performed using a transfer instrument [such as Advanced Very-high-resolution Radiometer (AVHRR)] or using lunar measurements normalized using a common model, such as the USGS Robotic Lunar Observatory (ROLO). In recent years RadCalNet, a Committee on Earth Observation Satellites (CEOS) initiative, has provided SI-traceable Top-of-Atmosphere (TOA) reflectances from a coordinated network of instrumented land-based sites. RadCalNet facilitates a unique mechanism to perform cross-calibration of instruments by minimizing the uncertainties associated with overpass time differences. In this letter, the near-simultaneous TOA reflectance measurements from the Railroad Valley (RRV), US (RVUS) are used as a transfer to compare the on-orbit observations for the Terra and Aqua MODIS reflective solar bands (RSBs). Near-nadir overpasses from January 2013 to January 2019 are processed and matched up with near-simultaneous RadCalNet measurements. Results show that the visible (VIS)/near-infrared response (NIR) bands agree to within 2%, and the short-wave infrared (SWIR) bands agree to within 5%. Also discussed in this letter are the future efforts that will be undertaken to expand this comparison to include other instruments, other sites, and both nadir- and off-nadir views after compensation for bidirectional reflectance distribution function (BRDF) effects.

There has been explosive growth in the field of remote sensing of methane plume from aircraft and satellite. The global reach and inherent spatial sampling capabilities of on-orbit instruments make them uniquely suited for consistent, repeatable surveys across regions and borders. These measurements have primarily been applied to the fossil energy and waste sectors (Cusworth et al., 2022; Thorpe et al., 2023), with current satellites typically detecting emissions exceeding ≈100 kg CH₄ per hour, while airborne platforms can observe sources as small as ≈10 kg CH₄ per hour.Despite the rapid growth in observational capacity, challenges remain. Divergent emissions estimates, opaque methodologies, and inconsistent validation approaches can erode confidence in remote sensing-based emissions data. The emergence of non-public-sector missions using proprietary methods—often without full transparency across the data chain—further highlights the need for community-accepted practices to ensure traceability, comparability, and scientific credibility.To address this need, the greenhouse gas (GHG) community—through the Committee on Earth Observation Satellites (CEOS) and National Metrology Institutes (NMIs)— developed a document in 2025 to articulate commonly accepted approaches for quantifying methane emissions based on observed plumes (Worden et al., 2025). It provides guidance spanning from Level 0/1 radiance, to Level 2 concentration, to Level 4 emissions, and includes current practices for validation and quality assessment. The focus is on emissions derived from discrete plumes, rather than from spatially diffuse sources. In this poster, we will discuss some key points of the current practices report as well as plans for next steps to perform intercomparisons and work towards a Best Practices document. This work has shifted from NIST to the Climate Data Collaborative of the Data Foundation in the US, and will be performed in collaboration with researchers and agencies across the US and Europe in 2026 including CEOS, CGMS, NPL, LLBL, UKSA, ESA, and the MEDUSA project.

Abstract. Data cube terms a multi-dimensional stack of gridded datasets aligned for analysis. Open Data Cube (ODC) is an open-source based information processing and managing platform on the viewpoint from web-based infrastructure. This open platform is for a large volume of geo-spatial information with geo-rectified coordinates, and it has been applied by non-profit international organizations such as the Committee on Earth Observation Satellites (CEOS) for an international coordination and management of space-borne missions, Global Earth Observation System of Systems (GEOSS) in the Group on Earth Observations (GEO), as an intergovernmental organization to improve the applicability, accessibility and usability of Earth observations for benefit of human society.The building of Analysis Ready Data (ARD), which means the preparation of radiometric calibration and geo-rectification, is for the data cube utilization. The platform converts large-scale satellite image data into analytic information, providing functions for time series analysis. Internationally, there has been an ever-increasing number of country-based data cube deployments with freely available satellite images, including Australia Data Cube, Vietnam Data Cube, Swiss Data Cube, and Colombia Data Cube, as a computing environment for information distribution, sharing, and analysis.However, there is no program yet to register Korea Multi-purpose satellite (KOMPSAT) optical and radar images on this platform, so this study developed the registering and ingestion script codes for KOMPSAT optical and radar image sets into ODC. Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. Thus, an ingestion process is required to add satellite data to the ODC platform and the process can be divided into three main stages in Figure 1. First, it is to define the data type in the YAML format. Then, the indexing process of datasets for metadata registration is necessary. The next step is a data ingestion process that users can be used directly data sets collected in ODC.Figure 2 shows some of the Python module results for index datasets and the process of metadata generation. The metadata YAML, which is required for indexing, has many advantages in many respects in the creation of metadata through Python modules. This is why we added Python modules to create metadata YAML. In particular, the KOMPSAT data ingestion process was designed so that all of them were possible through one module.Using script modules for these steps, the functional accuracy was tested with actual satellite data. Color composite images using RGB bands of KOMPSAT optical images were generated in the ODC environment in Figure 3. In this process, image data formats of GeoTiff and netCDF are also supported.In this study, consideration points for implementation of ODC applications are also discussed. KOMPSAT data is basically commercial-based products, unlike other freely accessible satellite images in the ODC applications. For the practical contribution for ODC-GEOSS, careful considerations for data policy are needed, because it can be applied as a reference model for other commercial satellite data for GEOSS.

(1993). A review of: “The Relevance of Satellite Missions to the study of the Global Environment.” By CEOS (Committee on Earth Observation Satellites, 1992) [Pp. 111.] British National Space Centre (no charge). International Journal of Remote Sensing: Vol. 14, No. 7, pp. 1418-1418.

The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.

This letter presents the improvements of an absolute calibration reference system based on simulated top-of-atmosphere bidirectional reflectance factor time series over bright desert targets. The current work highlights a case study performed over Committee on Earth Observation Satellites (CEOS) calibration target Libya4, demonstrating that it is possible to achieve a mean accuracy of 3% when simulation is compared with calibrated observations acquired by polar orbiting satellites.

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

Earth Observation (EO) services are complicated due to the number of EO satellite platforms. According to the Committee on Earth Observation Satellites (CEOS), there are over sixty EO satellite missions operating, and more than ninety missions planned within the next fifteen years.

There is an urgent need to increase the capacity of developing countries to take part in the study and monitoring of their environments through remote sensing and space-based Earth observation technologies. The Open Data Cube (ODC) provides a mechanism for efficient storage and a powerful framework for processing and analyzing satellite data. While this is ideal for scientific research, the expansive feature space can also be daunting for end-users and decision-makers who simply require a solution which provides easy exploration, analysis, and visualization of Analysis Ready Data (ARD). Utilizing innovative web-design and a modular architecture, the Committee on Earth Observation Satellites (CEOS) has created a web-based user interface (UI) which harnesses the power of the ODC yet provides a simple and familiar user experience: the CEOS Data Cube (CDC). This paper presents an overview of the CDC architecture and the salient features of the UI. In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The fully-customizable source code of the UI is available at our public repository. Interested parties can download the source and build their own UIs. The UI empowers users by providing features that assist with streamlining data preparation, data processing, data visualization, and sub-setting ARD products in order to achieve a wide variety of Earth imaging objectives through an easy to use web interface.

In recent years, Korea has sustained consistent access to remote sensed data by launching Korea Multi-Purpose Satellite-3A (KOMPSAT-3A, K3A)—an updated version of the high-resolution KOMPSAT series. This KOMPSAT-3A required calibration and validation (Cal/Val) before and after its launch to enable proper functional characterization and to maintain the veracity of data collected. The Korea Aerospace Research Institute (KARI) executed the initial prelaunch calibration in the laboratory and we performed the Cal/Val of KOMPSAT-3A during the Launch and Early Operation Phase (LEOP) in the field. Two suitable sites in Korea and Mongolia with stable weather, almost uniform terrain, and near Lambertian diffusion, provided the necessary tarp reflectance to calculate the absolute radiometric calibration coefficients. The surface reflectance was determined using 12 and four well-calibrated reference reflectance tarps employing the FieldSpec® 3(Analytical Spectral Devices Inc., Boulder, CO, USA) Spectroradiometer. Subsequently, the top of atmosphere (TOA) radiance was estimated using radiative transfer code (RTC) software based on the Atmospheric and Topographic Correction (ATCOR). In addition, cross calibration was simultaneously performed at the Libya-4 pseudo invariant calibration site (PICS) for KOMPSAT-3A TOA radiance, using the spectral band adjustment factor (SBAF) compensated Landsat 8 reflectance and the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to compute cross calibration coefficients. The results of the KOMPSAT-3A absolute calibration coefficient show that the R2 values were over 0.99, implying a significant correlation for almost all bands between the TOA radiance and the KOMPSAT-3A spectral band response at both campaign sites. However, this study reveals a difference of less than 5% calibration gains for all bands compared to the prelaunch values, while the cross calibration gain is below 5% in visible bands and above 5% in the near infrared band. An effort to optimize the reliability of the absolute calibration coefficients resorted to the rigorous quantification of uncertainties amongst atmospheric conditions, the digital number (DN), the reflectance tarp, the bidirectional reflectance distribution function (BRDF), and ozone levels. Therefore, we presumed that the total uncertainty was 4.27%, which conforms to some published results.