The Ceos Data Cube Portal: a User-Friendly, Open Source Software Solution for the Distribution, Exploration, Analysis, and Visualization of Analysis Ready Data

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.

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

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.

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.

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.

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.

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.

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

<p>Earth Observation (EO) data availability is drastically increasing thanks to the Copernicus Sentinel missions. In 2014 Sentinel data volumes were approximately 200 TB (one operational mission) while in 2019 these volumes rose to 12 PB (nine operational missions) and will increase further with the planned launch of new Sentinel satellites. Dealing with this big data evolution has become an additional challenge in the development of downstream services next to algorithm development, product quality control, and data dissemination techniques.</p><p>The H2020 project ‘Data Cube Service for Copernicus (DCS4COP)’ addresses the downstream challenges of big data integrating Copernicus services in a data cube system. A data cube is typically a four-dimensions object, with a parameter dimension and three shared dimensions (time, latitude, longitude). The traditional geographical map data is transformed into a data cube based with user-defined spatial and temporal resolutions using tools such as mathematical operations, sub-setting, resampling, or gap filling to obtain a set of consistent parameters.</p><p>This work describes how different EO datasets are integrated in a data cube system to monitor the water quality in the Belgian Continental Shelf (BCS) for a period from 2017 to 2019. The EO data sources are divided in four groups: 1) high resolution data with low temporal coverage (i.e. Sentinel-2), 2) medium resolution data with daily coverage (i.e. Sentinel-3), 3) low resolution geostationary data with high coverage frequency (i.e. MSG-SEVIRI), and 4) merged EO data with different spatial and temporal information acquired from CMEMS. Each EO dataset from group 1 to 3 has its own thematic processor that is responsible for the acquisition of Level 1 data, the application of atmospheric corrections and a first quality control (QC) resulting in a Level 2 quality-controlled remote sensing reflectance (Rrs) product. The Level 2 Rrs is the main product used to generate other ocean colour products such as chlorophyll-a and suspended particulate matter. Each product generated from the Rrs passed by a second QC related to its characteristic and improvements (when applied) and organized in a common data format and structure to facilitate the direct integration into a product and sensor specific. At the end of the process, these products are defined as quality-controlled analysis ready data (ARD) and are ingested in the data cube system enabling fast and easy access to these big data volumes of multi-scale water quality products for further analysis (i.e. downstream service). The data cube system grants a fast and easy straightforward access converting netCDF data to Zarr and placing it on the server. In Zarr datasets, the object is divided into chunks and compressed while the metadata are stored in light weight .json files. Zarr works well on both local filesystems and cloud-based object stores which makes it possible to use through a variety of tools such as an interactive data viewer or jupyter notebooks.</p>

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

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.

(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.

This paper describes a set of tools, algorithms and methodologies that have been developed and used in order to estimate the radiometric uncertainty achievable for a Sentinel 2/3 like sensor through in-flight cross-calibration using a well-calibrated hyperspectral SI traceable reference sensor such as the proposed TRUTHS (Traceable Radiometry Underpinning Terrestrial- and Helio- Studies) mission. This first study considers the criticality of the instrumental and observational characteristics in a fully iterative manner and uses the Libya-4 Pseudo Invariant Calibration Site (PICS) as a demonstration. The main uncertainty contributions have been studied: spectral response, reflectance heterogeneity, sun angular effect and aerosol (spatial and temporal) effects. The tool contributes to optimizing the design of TRUTHS; but will find immediate use in support of existing sensor-to-sensor cross-calibration activities being carried out under the auspices of Committee on Earth Observation Satellites (CEOS).