Towards Standardization of the Earth Observation Data Product Supply Chain – Are OCI Artifacts the Key to Ubiquitous and Scalable EO Data Handling?

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

The growing availability of Earth Observation (EO) data enables monitoring of terrestrial ecosystems at unprecedented spatio-temporal resolutions. In practice, however, effective use of EO data remains constrained by substantial technical barriers. Working with raw, multi-modal EO imagery requires specialised domain expertise, large data transfers, access to high-performance computing infrastructure, and advanced machine learning (ML) skills. These requirements limit the accessibility of EO-based analytics for many downstream applications.Geospatial foundation models (GFMs) provide a promising alternative by learning general-purpose representations from large volumes of unlabeled EO data. By decoupling representation learning from downstream task modelling, GFMs allow users to exploit expressive features from modern deep learning models with limited EO or deep learning expertise and modest computational resources.In this work, we investigate embedding-based GFM workflows for forest disturbance monitoring, where timely inference and regional customisation are often more critical than maximising absolute predictive accuracy. Forest disturbances such as logging, windthrows, fires, pests, and diseases can occur abruptly and require rapid detection to support conservation, policy-making, and risk-management efforts.Machine learning methods for forest disturbance detection from EO data are well established and have shown strong performance on regional benchmarks. However, much of this work remains confined to academic demonstrations and is rarely translated into operational monitoring systems. Existing forest monitoring tools, including those aggregated by Global Forest Watch, typically rely on region- and sensor-specific models with limited feature expressivity. These systems may benefit from the rich multi-modal and spatio-temporal representations learned by GFMs, provided such embeddings can be accessed through scalable and practical deployment pipelines.We build on a pipeline designed to deliver on-demand, location- and time-specific geospatial embeddings as a service. Embeddings are generated server-side from raw EO data, compressed, and distributed as lightweight representations. End users interact only with these embeddings, which can be analysed using simple models such as linear probes or small decoders. This approach removes the need for the user to manipulate raw EO data, download large multi-modal datasets, or train and deploy large deep learning models, enabling rapid adaptation to local contexts with limited annotations and modest computational resources.We present preliminary results demonstrating the feasibility of this approach for forest disturbance detection and discuss its strengths and limitations relative to bespoke, fully supervised image-based models. While GFMs may not be optimal for applications with abundant annotations and stringent accuracy requirements, embedding-based services are particularly well suited to time-sensitive and regionally adaptive monitoring scenarios. Overall, this work illustrates how releasing geospatial embeddings as a product or service can lower barriers to EO-based forest monitoring and support faster, more inclusive environmental decision-making.

Models of daily runoff from seasonal snowpacks and glaciers require knowledge or assumptions about the decline in snow covered area (SCA). Some semi-distributed models rely on satellite data as an input in addition to meteorological data but general purpose hydrological models with a snow component do not normally use earth observation (EO) data. EO data have the potential to verify or update SCA predictions generated by these models, but comparison is hampered by the unrealistic assumption in most models of spatially uniform snow water-equivalent (SWE) within entire zones, SO that SCA decline is stepped. Two possible solutions are either to allow a stepped SWE distribution within a sub-area, or to assume uniform melt over a non-uniform snowpack within a sub-area. In both approaches melt is converted into a reduction in SCA as well as SWE allowing snowpack depletion to be compared directly with EO data. Two examples are given in which EO data is used to verify (and in one case update) SCA. The HBV model is applied to a basin in Arctic Sweden and a recently developed glacier runoff model is applied to a basin in the Swiss Alps. Landsat TM data of both basins revealed considerably less snow than simulated by the models. TM data for the Swedish basin show that only glacier zones were 100% snow covered. Despite over-predicting SCA both models achieved very good discharge fits. It is argued that runoff models should correctly simulate the hydrological system state variables if they are to be transferred to different environments or new climate scenarios with confidence, and that EO data can play a valuable role in this.

Abstract. Being able to accurately estimate parameters characterising land surface interactions is currently a key scientific priority due to their central role in the Earth's global energy and water cycle. To this end, some approaches have been based on utilising the synergies between land surface models and Earth observation (EO) data to retrieve relevant parameters. One such model is SimSphere, the use of which is currently expanding, either as a stand-alone application or synergistically with EO data. The present study aimed at exploring the effect of changing the atmospheric sounding profile on the sensitivity of key variables predicted by this model assuming different probability distribution functions (PDFs) for its inputs/outputs. To satisfy this objective and to ensure consistency and comparability to analogous studies conducted previously on the model, a sophisticated, cutting-edge sensitivity analysis (SA) method adopting Bayesian theory was implemented on SimSphere. Our results did not show dramatic changes in the nature or ranking of influential model inputs in comparison to previous studies. Model outputs examined using SA were sensitive to a small number of the inputs; a significant amount of first-order interactions between the inputs was also found, suggesting strong model coherence. Results showed that the assumption of different PDFs for the model inputs/outputs did not have an important bearing on mapping the most responsive model inputs and interactions, but only the absolute SA measures. This study extends our understanding of SimSphere's structure and further establishes its coherence and correspondence to that of a natural system's behaviour. Consequently, the present work represents a significant step forward in the global efforts on SimSphere verification, especially those focusing on the development of global operational products from the model synergy with EO data.

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Urbanization as a global phenomenon is a multifaceted process, affecting sustainable development in and around urban areas.Exploring the multi-characteristics of urbanization and their environmental effects is crucial for achieving Sustainable Development Goals (SDGs) in global cities.Most existing research monitored urbanization and urban sustainability from a single perspective with limited temporal and/or spatial coverage.Therefore, the primary objective of this thesis is to enhance a comprehensive understanding of global urbanization and sustainability as well as their nexus at the grid/neighborhood level, by integrating multisource Earth Observation (EO) data and social data.Chapter 2 provides a comprehensive characterization of global multifaceted urbanization in terms of population, built-up structure, and greenness per 5 × 5 km 2 inhabited grid from 1975 to 2015.The Global Human Settlement Layer and MODIS/GIMMS NDVI were used to detect long-term dynamics of built-up patches and greenness.Results emphasize the multifaceted nature of urbanization that varies greatly across regions and times.The urbanization types, identified by a self-organizing map algorithm, reveal diverse patterns across continents and urban-suburban-rural settlements.Urbanization processes became less intense, more compact, and "greener" over the recent period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) compared to previous periods .This chapter offers a comprehensive application of multi-source EO data to the understanding of global multifaceted urbanization with many spatial and temporal variations.Chapter 3 presents global monitoring of the land-energy-air nexus in terms of land use efficiency (SDG 11.3.1),energy efficiency (SDG 7.3.1),and air quality (SDG 11.6.2) at 1 km resolution from 2000 to 2015.Multi-source EO and statistical data were integrated to assess the grid-level SDGs.For instance, statistical energy consumption was downscaled by statistical models based on gridded night-time lights and population data.Our maps highlight distinct urban-rural gradients and the Global North-South differences.Urban areas are more sustainable than rural areas in the Global South but are less sustainable than suburbs in the Global North.Trade-offs in the land-energy-air nexus are mostly related to energy efficiency versus air quality in urban areas.This chapter demonstrates how EO data can support contextspecific and win-win-win strategies for achieving land-energy-air SDGs from a global perspective but in high spatial detail.Chapter 4 develops a multi-level approach and supply-demand concept to explore urban green inequality and its mismatches with human demand across neighborhoods in New York, Amsterdam, and Beijing in 2020.The ESA World Cover, Open Street Map, and neighborhoodlevel census were combined to assess green supplies.The land surface temperature, PM2.5, income, and age structure were used to quantify human demands.Results show that the urban green inequality based on Gini coefficients and supply-demand relations vary by metrics and cities.Low supply-high demand neighborhoods are mainly scattered around the city center in study cities.This chapter stresses the importance of assessing demand-based inequities in urban green research, and can guide neighborhood-specific strategies for urban green allocation towards demand-oriented equity in study cities.Chapter 5 explores the effects of urban-rural interactions on regional developments of population, employment, and GDP per capita at the NUTS-3 level in Europe between 2003 and 2018.We used geographical proximity to measure potential interactions between urban, intermediate, and rural regions.Results show that urban-rural interactions are generally related to benefits for both urban and rural regions, although the effects differ between periods and between different dimensions of regional development.Initial regional performance is significantly associated with regional development.This chapter emphasizes the importance of considering urban-rural interactions in understanding the overall health of regional systems and the potential for urban-rural synergies.Chapter 6 unravels global nexuses between urbanization and environmental livability in terms of greenness, summertime temperature, and air quality per 5 × 5 km 2 inhabited grid between 2000 and 2020.Multi-source EO data were used to quantify multifaced 4D urbanization and green-climate-air livability trends.The Geographically Weighted Regression models were adopted to analyze the urbanization-green-climate-air nexus by accounting for both the global, general effect, and local, various effects.Results present large inter-and intra-city variations in urbanization effects on green-climate-air trends with opposite directions across continents.This chapter can provide global and local insights into which urbanization process should be intervened to promote urbanization into a sustainable solution rather than a threat.This PhD thesis contributes to the data revolution of integrating Earth Observation and social data for urban science, enabling a comprehensive understanding of global urbanization and critical sustainability trends with local variations and characteristics.By providing actionable information for context-specific strategies instead of a 'one-size-fits-all' policy, this thesis builds a science-policy bridge towards a sustainable and livable urban future.

ArtEO is a non-profit initiative established with the simple aim of bringing Earth observation (EO) and other environmental data closer to artists and creatives. The mission of ArtEO is to facilitate easier access to data and imagery for artists, musicians, storytellers and creative professionals who wish to engage with climate change and environmental subjects. In the first phase of the initiative (‘Pioneer Phase’), ArtEO has worked with a diverse range of multidisciplinary artists who have produced work in many genres, ranging from large scale sculpture to immersive projections, movement-based performance art to video work, software-based sonification to powerful musical composition. These works have been showcased in a number of major art-science events, and in climate communications and innovation contexts, including the Ars Electronica Festival, MTFLabs, EGU, IAC and elsewhere.ArtEO takes a specific approach to supporting artistic and creative engagement with EO data that is often distinct from other forms of science communication, data visualization and infographics. However, there are some overlaps and they share a common goal of achieving greater reach, visibility and impact with the data. There is an opportunity for cross-learning between all of these areas and this session will provide an insight into the process and methodologies undertaken by artists working with EO in the ArtEO Pioneer Phase, and what this has revealed about the challenges for the wider public of engaging with EO data and imagery. and about ArtEO’s aim is to achieve significant public reach with both the artworks themselves and the data utilised.

RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Evaluating the ability of a hydrodynamic ecosystem model to capture inter- and intra-annual spatial characteristics of chlorophyll- a in the north east Atlantic

Earth observation (EO) data cubes have gained significance for their potential in providing comprehensive insights into dynamic earth phenomena. However, a significant challenge in their utilization is the interoperability among EO data cubes with varying dimensionalities. Existing efforts have primarily focused on enhancing analysis ready data cubes, yet cross-dimensional adaptivity remains relatively unexplored. This study proposes a novel approach to address this issue. The proposed method is based on a 3D adaptive data cube model constructed in a 3D adaptive space. The main objective is to achieve interoperability between EO data cubes of different dimensions while adhering to common geospatial standards. The core methodology involves cross-dimensional mapping, considering a 3D adaptive space containing spatial axes and adaptive axes. Importantly, the method applies to data cubes with the same coordinate reference system, resolution, and data type. To enable cross-dimensional mapping, a key requirement is the existence of a mapping function between the multidimensional space domain and the 3D adaptive space domain. When these conditions are met, data cubes with three or more dimensions can be interoperable. This becomes feasible by applying suitable serialization algorithms. This study demonstrates that data cubes can be successfully mapped into the proposed 3D adaptive data cube, achieving interoperability under specific conditions. This finding has significant implications in the field of EO data analysis, enabling seamless interaction between data cubes with different dimensions. The method not only facilitates cross-dimensional adaptability but also aligns with mainstream geospatial service standards. It is particularly suitable for multidimensional geospatial raster services based on the OGC WCS 2.0 standard. In conclusion, this study addresses a critical challenge in EO data analysis by proposing a 3D adaptive data cube model that promotes interoperability between data cubes of varying dimensions.

Earth Observation (EO) data plays a crucial role in research and applications related to environmental monitoring, enabling informed decision-making. However, the continuously increasing volume and diversity of EO data, distributed across multiple platforms and varying formats, pose challenges for easy access and the development of scalable and reproducible workflows.openEO addresses these challenges by providing a community-driven, open standard for unified access to EO data and cloud-native processing capabilities. It supports researchers to develop interoperable, scalable and reproducible workflows that can be executed using various programming languages (Python, R or JavaScript).openEO has become a cornerstone technology across major initiatives in agriculture, natural capital accounting, and land-cover monitoring. In ESA’s WorldCereal project, it provides the scalable framework needed to process global Sentinel-1 and Sentinel-2 time series and integrate advanced machine-learning models, enabling dynamic 10-meter cropland and crop-type maps. It also supports the Copernicus Global Land Cover service and its tropical forestry component by delivering consistent and repeatable processing chains for annual 10-meter land-cover products, which are crucial for policy reporting and SDG monitoring. Beyond land cover, openEO supports efforts like ESA's World Ecosystem Extent Dynamics project by creating reproducible ecosystem-extent mapping and change detection maps — key elements for biodiversity and environmental management.Building on this foundation, the openEO Federation, now integrated within the Copernicus Data Space Ecosystem (CDSE), provides seamless access to distributed Earth observation data and processing resources through a single, unified interface. By connecting multiple backends, it removes the need to juggle separate accounts or APIs and enables cross-platform workflows over datasets hosted by platforms such as Terrascope and CDSE.openEO also strongly supports FAIR (Findable, Accessible, Interoperable, Reusable) principles. It exposes rich metadata, relies on standardised processes, and encourages the use of reusable workflow definitions. This promotes transparency, reproducibility, and the sharing of algorithms and data across research and operational communities. The approach has been validated in several large-scale implementations, including ESA’s WorldCereal and the JRC’s Copernicus Global Land Cover and Tropical Forestry Mapping and Monitoring Service (LCFM), demonstrating its maturity for both research and production environments.By enabling reusable, federated, and reproducible Earth observation workflows, openEO is helping to build a more interoperable and efficient computational ecosystem, one that supports scalable innovation, collaboration, and long-term operational monitoring. Therefore, in this session, we aim to spark discussion on how openEO enables federated, FAIR-compliant, and reproducible workflow approaches for large-scale Earth observation applications.

<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>

It is envisaged that the Global Earth Observation System of Systems (GEOSS) will provide data streams to decision-support tools for a wide variety of users and applications. As with the Internet, GEOSS will be a global and flexible network of content providers facilitating decision makers' access to an extraordinary range of information products at their desk. However, the challenge for the global users community is to have access to fast and reliable tools to process the data streams into actionable information. On August 31 — September 2, 2009 a workshop was held in Sinaia, Romania to discuss the current state-of-the-art in data mining tools and technologies for Earth Observation (EO) data by a group of international experts. The intent of the workshop was to lead to an understanding of and an objective assessment of the world's practice in extracting information from Earth Observation data and in developing a strategic plan for advancing the tools, as well as important open issues. This paper summarizes some of the major findings and recommendations resulting from this workshop.

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

<p>Precision Agriculture (PA) applied on a widespread basis can be a building block for reduced ecosystem degradation without compromising food security. One problem of farmers in the implementation of PA applications is the lack of high spatial resolution soil information.</p><p>The EU-funded research project ‘pH-BB: Precision liming in Brandenburg’ aims at developing innovative nutrient management strategies based on proximal soil sensing data.  For this study, the pH-BB project provided us the data of the 361 soil samples, which were taken at an 800-hectare farm in Brandenburg close to Frankfurt (Oder) and analyzed in the laboratory for the texture fractions clay, silt, and sand. We used the Google Earth Engine to process remote sensing earth observation (EO) data of 1474 Sentinel-1 (S1) SAR and 85 cloud free Sentinel-2 (S2) scenes available at the study site during the period 2016-03-01 - 2021-11-09. Vegetation and soil indices were computed with optical S2 data and backscatter in VV and VH polarization were extracted from the S1 datasets. To derive long-term persistent patterns in the EO data, simple statistical parameters such as coefficient of variation, standard deviation, maximum pixel value, etc. were calculated along the temporal domain of the EO data. Together with calculated terrain attributes, 24 covariate grids were finally available for model building. Reference samples (rs) were randomly divided into a training dataset (70% of rs) and a validation dataset (30% of rs). Pixel values of the covariate datasets at the sampling locations were added to the rs datasets.</p><p>A random forest machine learning algorithm was applied to the training dataset to train two individual models for the alr-transformed target variables silt and sand using the covariates. The developed models were then applied to the gridded datasets to predict maps for the alr-transformed target variables silt and sand. The final maps of all 3 texture fractions clay, silt, and sand were computed by back-transforming the predicted alr silt and sand grids.</p><p>In the derived models EO covariates showed the highest level of importance. Comparison of the prediction results with the validation data set showed that the spatial distribution, of the clay, silt, and sand fractions was predicted, with a root mean square error (rmse) of 6.2, 5.3, and 9.7 mass-%, respectively. A classification of the predicted maps according to the German KA5 scheme showed that especially in the sand dominated soil classes the prediction errors were lower, whereas they increased in the loamy soil classes (dependent on the clay content).</p><p>With a rmse of 5.3 - 9.7 mass-%, the performance of the approach shows good potential for surface soil texture assessments even at high resolution or for global applications or as an initial guess for soil mapping with high resolution proximal soil sensing devices.</p>