Citizen science for Earth Observation (Citzens4EO): understanding current use in the UK

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.

Understanding global patterns of functional diversity is essential for exploring ecosystem functioning, yet our current knowledge is limited to specific regions and geographically restricted datasets.. Meanwhile, rapidly growing citizen science initiatives, such as iNaturalist or Pl@ntNet, have generated millions of ground-level species observations across the globe. Despite citizen science species observations being noisy and opportunistically sampled, previous studies have shown that integrating them with large functional trait databases enables the creation of global trait maps with promising accuracy. However, aggregating citizen science data only allows for the generation of relatively sparse and coarse trait maps, e.g. at 0.2 to 2.0 degree spatial resolution.Here, by using such citizen science data in concert with vegetation surveys and high-resolution Earth observation data, we extend this approach to model the relationships between functional traits and their structural and environmental determinants, providing global trait maps with globally continuous coverage and high spatial resolution (up to 1km). This fusion of ground-based citizen science and continuous satellite data allows us not only to map more than 30 ecologically relevant traits but also to derive crucial functional diversity metrics at a global scale. These metrics—such as functional richness and evenness—provide new opportunities to explore the role of functional diversity in ecosystem processes, particularly in areas previously lacking in data availability.Our approach presents a scalable framework to advance understanding of plant functional traits and diversity, opening the door to new insights on how ecosystems may respond to an increasingly variable and extreme climate.

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.

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.

A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth

The Copernicus Data Space Ecosystem (CDSE), a pivotal initiative of the European Space Agency (ESA) and funded by the European Commission, represents a groundbreaking advancement in Earth Observation (EO). It is an integral part of the ambitious Copernicus Programme, which has revolutionized EO data access and utilization across various sectors. The CDSE is more than a mere data repository; it is a collaborative platform where knowledge, expertise, and resources are shared and enhanced. This ecosystem approach is transforming the EO landscape, encouraging a more inclusive and participatory model where researchers, policymakers, businesses, and citizen scientists contribute to and benefit from the wealth of EO data. The CDSE stands as a beacon of international cooperation and a driver for sustainable development, setting new standards for leveraging EO data in addressing some of the most pressing global challenges.Central to the CDSE is its commitment to federation and cooperation, offering a flexible framework for integration and participation to data providers, remote sensing experts, and application developers. This approach fosters innovation and collaboration, ensuring that the ecosystem benefits from a diverse range of contributions. Data providers can upload their data to the ecosystem’s object storage, making it available to CDSE users under free or commercial conditions. This not only enhances the CDSE’s data repository but also expands the providers' reach. Remote sensing experts contribute their algorithms and workflows to the openEO Algorithm Plaza, a collaborative space for sharing and enhancing EO technologies and applications. Application developers can utilize CDSE's infrastructure and data to create solutions for specific needs like agricultural monitoring, demonstrating the ecosystem’s potential for application development.The CDSE's principle of federation extends beyond individual contributors to collaborations with other platform providers, enhancing the range of services and capabilities available to users. These partnerships are crucial for the ecosystem's growth, ensuring access to the best tools and data. The governance of the Data Space Ecosystem incorporates several third-party commercial providers and is designed to grow with additional contributions. It is based on European values of data sovereignty and privacy, ensuring trust, collaboration, and sustainability. The ecosystem maintains a clear distinction between publicly funded services and third-party offerings, both free and commercial. This open and federated approach makes CDSE a comprehensive solution for EO needs and solidifies its position as a leader in the EO community.

<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>

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices.

Citizen science has extensively been reported as a useful tool for scientists gathering large quantities of information (Silvertown et al. 2011; Dickinson et al. 2010; Bonney et al. 2009). Citizen Science has been broadly defined in previous literature. We employ the following for purposes of this paper: partnerships between scientists and the public where data are collected, shared, and analyzed (Jordan et al. 2012). These partnerships result in projects that range in participation capacity, participation design, and project goals. On one hand, there are participatory action research projects (Cooper et al. 2007), initially defined by Caren Cooper as projects initiated by non-scientist members of the public and developed hand-in-hand with scientists or scientists hired by the project as consultants (see also Shirk et al. 2012). On the other hand there are crowdsourced or contributory citizen science projects (Bonney et al. 2009), which allow researchers to gather large amounts of data without any input or participation of the public on project development. These large contributory projects such as eBird (www.eBird.org) and Evolution MegaLab have had success in collecting large-scale data sets from participants for long-term trend analysis in the U.S. and Europe respectively. Crowdsourcing citizen science projects such as FoldIt (www.fold.it) and Galaxy Zoo (www.galaxyzoo.org), have capitalized on large numbers of participants to do analysis that computers are not adept at. Co-created action type citizen science projects represent the middle ground, where partnerships between local citizens and scientists have been successful in preservation and restoration efforts of local waterways in New Jersey and Pennsylvania (e.g., ReClam the Bay (www.reclamthebay.org) and Shermans Creek Conservation (www.shermanscreek.org)). Regardless of the project type, all citizen science programs have participants engaged in authentic scientific research and resulted in successful data collection for scientists. While there is no official count of all the citizen science projects active at any given time, anecdotally there are a large number citizen science projects in ecology. Recent findings on patterns and use of citizen science in research found that the bulk of published citizen science based research was ecological/biological in nature (Follett and Strezov 2015). What has been little studied is the role and impact of the professional scientist who is engaging the citizen scientists. Two areas are of particular interest in this research: (1) who and what disciplines use citizen science and (2) the perceived trust and reliability of citizen science gathered data in academia. Research about the ecologists, and scientists writ-large, engaging participants in their research is limited. It is important to know more about the role and use of citizen science in academia because of its increased use in certain fields not just as a research tool but a method of engagement, and engendering broader support for science within the public sphere. If there is indeed a skewed representation of particular disciplines or individuals, there may be implications for broader citizen science outcomes. Additionally, variation in trust and use, or willingness to use, citizen science or citizen science collected data because of academic practice or identity frames may hinder further gains made in the field of citizen science as * Amanda E. Sorensen amasoren@rutgers.edu

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.

Abstract. The exponential growth of Earth Observation (EO) data generated by satellites demands scalable, efficient, and interoperable methods not only for storing and managing, but also for distributing data packages tailored to diverse use cases. The Open Container Initiative (OCI) registries, together with the OCI artifact specifications, present a promising framework for packaging, exchanging, and managing EO-derived and combined datasets. Originally developed for software containers, OCI registries offer key capabilities such as content-addressable storage, data integrity verification, cryptographic attestation, layered packaging, and version control. A notable advantage is their ability to act as access gateways—enforcing access control at the artifact level without requiring direct exposure of the underlying storage backends (e.g., S3, GCS, Azure Blob, NFS, Ceph, IPFS). The ubiquity of OCI registries—spanning public platforms, managed enterprise services, and open-source deployments—makes them a practical foundation for distributing EO data across heterogeneous environments without custom infrastructure. This paper investigates the applicability of today’s OCI registry ecosystem to EO data pipelines, evaluating both strengths and current limitations in handling large, complex, and dynamic datasets. We explore design conventions and layout strategies to align EO products with the OCI artifact model, with a focus on metadata representation, access efficiency, and storage reuse. By comparing self-contained data packages with modular, layered asset stores, we highlight trade-offs in retrieval performance, interoperability, and client complexity. Recent trends in machine learning model distribution further underscore the growing relevance of OCI-based artifacts for scientific and geospatial workflows. Ultimately, this research positions OCI artifacts as a viable foundation for scalable, standards-aligned, and interoperable EO data handling—paving the way toward more streamlined and resilient data supply chains in the EO domain.

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.

Citizen science represents an important opportunity for school students to make real-world connections with science through context-based learning with the potential to increase their engagement, enjoyment and understanding of science. However, to date, citizen science has not experienced wide uptake in school settings and there is a paucity of information about its implementation in the classroom. Here we present a mixed-method approach investigating teachers' knowledge and use of citizen science in Australian classrooms. We explored teachers' experience and perceptions of citizen science, and opportunities and barriers to incorporate citizen science as an educational approach through an online questionnaire. Among the teachers surveyed, 45% (n = 295) had personally participated in citizen science outside of school and 41% (n = 283) had incorporated citizen science projects in classroom lessons. Teachers (45%, n = 295) reported participating in citizen science initiatives multiple times. Also, most projects that teachers were involved in (77%, n = 292) were related to ecological studies, such as species monitoring. Citizen science was reported to be a relatively new approach; used by teachers for less than a year on average. The main challenges included a lack of knowledge, time, confidence, and clarity regarding citizen science project alignment with the Australian curriculum. Additionally, 92% of respondents said they would be more encouraged to use citizen science in classrooms if projects were aligned to the curriculum. Identifying ways to increase teachers' openness to incorporate citizen science in their classrooms is crucial to its successful widespread, long-term, and meaningful implementation. Encouraging broader participation of teachers in citizen science based on their previous experiences could address their expectations and increase their confidence and feeling of ownership. These research findings suggest that meaningful and applicable citizen science programs could be co-created by addressing resource limitations and curriculum alignment challenges. Implementing solutions to these barriers is likely to contribute to the development of sustainable school-inclusive citizen science projects, with potential to positively impact science education in the long-term.

\n The use and sustainable development of cropland soils requires the continuous monitoring and promotion of good practices that support soil quality and the provision of its several functions. As the soil quality and functioning can be affected by several factors and interventions, resulting in changes at the temporal and spatial scales, Earth observation (EO) systems become an sound alternative for monitoring soils due to ability in providing data in a timely manner, covering large geographical areas, and revisiting the same place in Earth in short periods of time. Furthermore, as the availability of detailed information about cropland soils is still a challenge in most countries, and recent literature has been supporting the proposition that collections of EO data are a valuable source for environmental studies, this study aimed at exploring the collection of satellite images for mapping and monitoring cropland soils over large geographical areas. For this, we developed the routines for processing big EO data within a high-performance cloud-based platform. With the combination of extracted features from EO data, legacy soil datasets, and machine learning algorithms, we performed the medium-resolution mapping of cropland soils over the geographical extents of Europe and Brazil. We demonstrated in this study that the collection of Landsat images is a valuable source for extracting spectral features useful for mapping and monitoring cropland soils. The bare soil composite based on the median of 37 years of Landsat imagery allowed the prediction of clay and calcium carbonates with moderate performance in Europe. In addition to that, using the Google Earth Engine, we developed and made publicly available a package to calculate terrain attributes customized to different spatial resolutions, which can be scaled up to the global extent. This package was particularly important for preparing additional information for mapping the cropland soils in Brazil. The spectral and terrain features extracted from EO data allowed the calibration of prediction models of clay, sand, soil organic carbon (SOC) content, and SOC stock with satisfactory accuracy across the Brazilian cropland soils. With the resulting maps, we were able to estimate the total SOC stock and identify some aspects related to the distribution of soil attributes regarding the main agricultural regions. Therefore, this study supports the proposition that EO data is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion in Europe and Brazil.\n

In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.