0.10 open-earth-monitor-global-workshop-2025 2025-09-17 2025-09-19 3 00:05 https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/schedule/ UTC 2025-09-17T09:00:00+00:00 09:00 00:30 Aula Magna open-earth-monitor-global-workshop-2025-387-introduction-to-the-oemc-project-by-tomislav-hengl https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/GE9KCU/ false Keynote lecture en The OEMC project in a nutshell by the project coordinator, Tom Hengl, Director of Opengeohub Foundation Tom Hengl (OpenGeoHub) 2025-09-17T09:30:00+00:00 09:30 00:30 Aula Magna open-earth-monitor-global-workshop-2025-373-evolving-fair-and-open-earth-observations-in-the-technology-science-policy-nexus https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/WDHNZE/ false Keynote lecture en The presentation will study some recent EO implications of EU Green Deal policies and the discussion around their evolution, also in light of the Copernicus expansion missions and “new space” developments. Earth Observation (EO) data have become indispensable for understanding and managing environmental change. The OpenEarthMonitor project (and other efforts) have highlighted that the societal value of EO can only be realized when data is not only open but also FAIR—Findable, Accessible, Interoperable, and Reusable. Use cases and demonstrations are already showcasing evolving frameworks and technologies for enhancing the FAIRness and openness of EO systems, and narrowing the gap between data production, scientific utility, and policy and service application. The presentation will study some recent EO implications of EU Green Deal policies and the discussion around their evolution, also in light of the Copernicus expansion missions and “new space” developments. We will illustrate several examples on how technology, science and policies interlink and how co-designed EO infrastructures—rooted in open science principles—can align with policy priorities and accelerate progress toward their implementation and performance assessment. Situating FAIR and open EO within the broader technology-science-policy nexus, this work underscores the transformative potential of data-driven environmental governance in the face of a changing role and perception of critical environmental issues in policy and society. Martin Herold 2025-09-17T11:00:00+00:00 11:00 00:20 Aula Magna open-earth-monitor-global-workshop-2025-346-adapting-the-planetary-health-index-framework-to-sub-national-scale-for-europe https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/BMLFHF/ false Oral talk en The Planetary Health Index (PHI) framework has been proposed as an innovative tool to summarize and analyze complex data about the state of the planet. The idea is to create an index composed of three separate interpretable axes, each representing one of the domain "spheres" of interest (atmosphere, biosphere and socio-economy). The resulting framework allows one to identify how one sphere affects another for a given region during a given time frame. The statistical method behind is a 3-way canonical correlation analysis (CCA). A first global prototype was demonstrated at global level combining yearly world bank data and the Earth System Data Cube (ESDL) gridded at quarter degree spatial resolution. However, this spatio-temporal configuration may be too coarse to properly characterize the complexity of global interlinkages between atmosphere, biosphere and socio-economy. We have thus ported the PHI framework to a finer spatio-temporal resolution by testing it at European level, leveraging on the daily 5km data cube of input and outputs of FLUXCOM-X-BASE along with EUROSTAT socio-economic data. We will present first results exploring whether we can use this framework to answer questions pertaining to implications of nature degradation on price inflation. Gregory Duveiller 2025-09-17T11:20:00+00:00 11:20 00:20 Aula Magna open-earth-monitor-global-workshop-2025-359-evolution-of-the-copernicus-land-monitoring-service-evoland-project-results-and-public-dissemination https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/H9SJ8C/ false Oral talk en The Horizon Europe EvoLand - Evolution of the Copernicus Land Service Portfolio - project aims to enhance the Copernicus Land Monitoring Service (CLMS) by advancing innovative methods, algorithms, and prototypes for monitoring land use/land cover dynamics and land surface characteristics at high spatial and temporal resolutions. EvoLand targets the development of eleven next-generation CLMS product candidates across Forest, Agriculture, Water, Urban and General land cover domains, leveraging cutting-edge methods such as data fusion, machine learning and continuous monitoring alongside novel Earth Observation (EO) and in-situ data sources. The project emphasizes aligning its prototype services with policy, data, and infrastructure requirements by engaging closely with Entrusted Entities and key Copernicus Land stakeholders and users. These requirements guide all methodological developments, ensuring relevance and impact. The methods include i) the integration of relevant novel EO datasets; ii) the acquisition of fit-for-purpose in-situ and training data; as well as the development of algorithms for iii) Weakly Supervised Learning; iv) improved spatial, temporal and thematic resolution; v) continuous monitoring (i.e. change detection and continuous land cover mapping) and vi) biomass mapping. EvoLand designs, tests, and implements algorithms on open-source, modular, and scalable platforms, using representative test sites both within Europe and globally. In the demonstration phase, these candidate services were deployed over larger regions, addressing critical thematic areas. To ensure continuous improvement, the candidate services are systematically assessed for their innovation potential, policy relevance, technical excellence, and operational readiness. EvoLand also proposes a strategy for transitioning these services to operational use. The project’s ultimate goal is to support Entrusted Entities by delivering research-driven, tangible advancements to the CLMS. This includes enhancing the information content, quality, and timeliness of services, thereby enabling evidence-based decision-making and demonstrating the potential for the ongoing evolution of the CLMS. EvoLand is developing and testing new and innovative methods in support of the evolution of the Copernicus Land Monitoring Service. It integrates novel Earth Observation, in-situ data and the latest Machine Learning techniques to continuously monitor the status, dynamics and biomass of the land surface. The project focuses on five key thematic domains: agriculture, forest, water, urban, and general land cover. Across these themes, we are developing 11 prototype services that could potentially be part of the future CLMS baseline. These will be operationally benchmarked and qualified as candidate CLMS services with a TRL5-7, which will have the potential to be taken up and integrated into CLMS by the Entrusted Entities from 2026 onwards. Within this talk we will give a general overview of the project, the 11 prototype services and how one can explore and use the open-source project results using the EvoLand results portal. Evoland is funded by the European Union through the Horizon programme, and is undertaken by a consortium of 10 European EO companies coordinated by VITO. Daniel Thiex 2025-09-17T11:40:00+00:00 11:40 00:20 Aula Magna open-earth-monitor-global-workshop-2025-354-assessing-climate-change-risk-for-the-private-sector-a-geospatial-approach-using-openearthmonitor-in-the-european-reinsurance-sector https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/XDAQCZ/ false Oral talk en The risks of climate change impacting the private sector is a dire reality for any community, anywhere, considered at a larger or smaller scale (local, national, regional) and at all of its different levels, be it with respect to the public sector, the private sector and down to every citizen. Consequences of such impacts are already discernible, especially in the case of the (re)insurance industry, also due to the immediate catastrophic consequences that, more and more often, involve human losses as well. Given the fundamental characteristic of the private sector - making a profit - overall results of extensive internal analysis of the financial impact of extreme weather events have been made public by various reinsurance companies. Furthermore, there is a significant body of knowledge to define, characterize and monitor climate change risks with consideration to the financial impact towards different sectors of the markets and, as well, proposed mitigation measures and resilience building strategies. In these endeavours, the geospatial component, encompassing data, technology, methodologies, are essential. In this talk, the authors propose a simplified methodology dedicated to the use of the generated OpenEarthMonitor Cyberinfrastructure geospatial products relevant for assessing the risk for the European private sector, with an emphasis on the reinsurance sector. The work presented will also pinpoint on the difficulties that arise from the complexity of accurately defining the geographical extent of the impact chain, as well as for the geographic footprint of the particular assets to be analysed. For interoperability reasons, in the assessment the Global Exposure Database for All schema will be considered because it is aligned with the Risk Data Hub as well as with the OpenStreetMap tags, both datasets being considered in the simplified methodology. Codrina Maria Ilie 2025-09-17T13:30:00+00:00 13:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-347-a-unified-tool-to-access-flux-towers-data-worldwide-the-fluxnet-shuttle https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/KPNRVV/ false Oral talk en With this contribution we present a unified system for accessing and downloading the datasets of the FLUXNET network independently on their station of origin, named the FLUXNET Shuttle. FLUXNET is a global consortium of regional networks monitoring greenhouse gas exchanges at the ecosystem level, coordinated by three regional hubs – ICOS, AmeriFlux, OzFlux. With the participation of several hundreds of stations, it represents the most widespread effort of GHG flux monitoring at the global scale. Output files are harmonised across all the stations contributing to FLUXNET with the definition of a common set of variables – made possible by the use of the same processing software (ONEFlux). Despite that, some differences among the networks exist, especially in terms of data repositories accessibility, while the FLUXNET portal provides access to the latest complete data release (FLUXNET2015). The FLUXNET Shuttle is an API-based Python tool for querying FLUXNET datasets from any participating station in the world, thus overcoming these differences. To be used both by command line and via graphical interface, its technical implementation is a cornerstone of the FLUXNET Data System Initiative, which aims at extending the spatial coverage of FLUXNET stations and at surmounting the current system of fixed releases, in favour of a more dynamic one based on continuous updates. We expect this initiative to improve usability and discoverability of FLUXNET datasets, facilitating the users and increasing the FAIRness of the data, also in the context of the OEMC project. With the definitive version expected for December 2025, here we demonstrate the main features of the FLUXNET Shuttle in its current development, including accessing data of the three regional hubs, implemented search criteria (e.g. geographical areas, time periods, station names), and example of applications. Simone Sabbatini 2025-09-17T13:50:00+00:00 13:50 00:20 Aula Magna open-earth-monitor-global-workshop-2025-351-monitor-eo-an-online-tool-for-monitoring-and-evaluating-impacts-on-land-resources-and-ecosystems-from-restoration-activities https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/DRGTVD/ false Oral talk en Abstract We monitor the impact of 320 nature-based climate solutions (NBS) implemented through carbon offset projects across 55 countries using a standardized methodology based on Earth Observations (EO) Big Data. Our objective is to demonstrate the feasibility for using free and open EO data at high and low moderate spatial resolutions to support M&E. We identify current gaps and provide recommendations both for technical enhancements and for the design of restoration policies. Finally, we deploy a Google Earth Engine app called ‘’Monitor-EO’’, which allows the end user to perform the analysis described in the paper, using a graphic user interface (GUI). We employ an ecosystem-based approach to assess impacts, by simultaneously monitoring three key ecosystem variables such as vegetation cover, land surface temperature and soil moisture. We assume that a positive outcome from a restoration measure would lead to increased vegetation levels, increased soil moisture, and decreased temperature of the soil surface. Comparison areas are defined for each project restoration site using a 2km buffer around each restoration area and one additional comparison area which i) is randomly created within a radius of 3 kilometres from the restoration area, and ii) has equal area than the restoration site?. We measure across restoration and control sites three variables, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Land Surface Temperature (LST), three years before the restoration activity was implement and then for the years following until 2024. We use different EO products from MODIS data (NDVI and NDWI at 500 meters spatial resolution and the LST at 1000 meters spatial resolution) For each variable we measure the Difference-in-Difference (DiD), and we perform a trend analysis. We run a series of statistical tests to ascertain the statistical significance of changes in order to infer causality of the project interventions. Finally, for selected project sites, we perform a Spatial Autocorrelation Analysis to assess the degree of spatial clustering of positive changes (indicative of restoration success) compared to a random distribution. The Monitor-EO application identified significant trends in at least one environmental indicator (NDVI, LST, or NDWI) in 62% (199) of the 320 projects analysed, covering all regions except Europe. Globally, NDVI exhibited predominantly positive trends, with 92% of projects showing increases, particularly in North America, Asia, Latin America and the Caribbean (LAC), and Africa, indicating favourable environmental changes. In contrast, LST displayed decreasing trends in 54% of the projects, with the most pronounced reductions observed in Africa and Asia. NDWI, however, exhibited declining trends in the majority of projects, with only 19% showing increases, primarily in Africa and North America. Projects demonstrating the highest rates of change were initiated in 2011 and are projected to extend for over 20 years. Smaller projects (less than 1,000 hectares) exhibited more pronounced trends compared to larger projects, while longer monitoring periods (exceeding 10 years) were associated with more substantial and statistically significant trends. Keywords: earth observations, monitoring and evaluation, landscape restoration, ecosystem health, climate change adaptation, food security, /media/open-earth-monitor-global-workshop-2025/submissions/DRGTVD/MONITOREO_jVssI2X.png Lorenzo De Simone 2025-09-17T14:10:00+00:00 14:10 00:20 Aula Magna open-earth-monitor-global-workshop-2025-326-transfer-learning-as-a-solution-for-the-large-areas-classification-dilemma https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/KFU7BK/ false Oral talk en This study explores transfer learning in the Brazilian Amazon over the period from 2015 to 2022. We use a time-series Random Forest model taking samples from 2022 to classify yearly date cubes up to 2015. The classification achieved an agreement of 89.20% with the reference map for 2022. Over the years, the agreement showed a cumulative decline of 2%. Our results suggest that the transferability of a machine-learning model is highly correlated with a set of highly representative training samples. Gilberto CamaraFelipe Carvalho 2025-09-17T14:30:00+00:00 14:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-357-eurogeo-green-deal-data-space-action-group-interoperability-of-forest-monitoring-data-at-regional-national-european-and-global-scales https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/KT7B8A/ false Oral talk en The EuroGEO Green Deal Data Space Action Group (GDDS-AG) is a forum to coordinate the activities of the projects contributing on research and development towards the realization of the GDDS. The key recommendation for the GDDS-AG is to monitor emerging technologies that can be beneficial for the GEO infrastructure to support technical and semantic interoperability between the GDDS datasets, with GEOSS, Destination Earth, Copernicus Data Space Ecosystem, INSPIRE, and European Open Science Cloud platforms. Highlighting the GDDS-AG member projects such as OEMC (Open-Earth-Monitor Cyberinfrastructure) and SAGE (Sustainable Green Europe Data Space), use cases in forest monitoring are presented to enhance data interoperability by integrating remote sensing, in-situ observations, and biodiversity indicators, which are key to decision-making for forest management and biodiversity conservation. The main challenges and opportunities of data integration at different spatial and temporal scales are addressed in relation to the implementation of the GDDS. Kaori OtsuImma Serra 2025-09-17T15:30:00+00:00 15:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-327-a-framework-of-federal-global-ensemble-digital-terrain-model https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/8NFGGH/ false Oral talk en GEDTM30 (github.com/openlandmap/GEDTM30) is a open source global digital terrain model at 1 arc second. It is the first permissive license 1 arc-second terrain model of the world (under CC-BY license). Upon this model, we are presenting a framework to merge national, state-based or individual digital terrain model to improve GEDTM30 data quality locally. Due to permissive license, GEDTM30 can be used as a base layer to create derive products. By merging local lidar DTMs with GEDTM30, it opens the gate to federation of data, sharing the common goods but keeps the best interests for individuals. This framework will be tested by GEDTM30 with lidar data from several countries (e.g. Romania, Italy, the Netherlands, Brazil, etc), and the land surface variables are included to assess the merged GEDTM30 quality. In the end, the code and framework will be open to serve any stakeholders to improve DTM quality of their area of interest and have freedom to decide for contribution. Yu-Feng Ho 2025-09-17T15:50:00+00:00 15:50 00:20 Aula Magna open-earth-monitor-global-workshop-2025-358-global-soil-carbon-and-soil-ph-predictions-for-2000-2024-at-30-m-based-on-spatiotemporal-machine-learning-and-harmonized-legacy-soil-samples-and-observations https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/QQGPVH/ false Oral talk en OpenLandMap-soildb (https://doi.org/10.5194/essd-2025-336) contains global dynamic predictions of soil organic carbon content, soil organic carbon density, bulk density, soil pH in H2O, soil texture fractions (clay, sand and slit) and USDA subgroup soil types (USDA soil taxonomy subgroups) at 30 m spatial resolution based on spatiotemporal Machine Learning (Quantile Regression Random Forest with output predictions showing the mean plus the lower and upper prediction intervals of 68 % probability). Predictions are provided at 3 standard depth intervals 0-30, 30-60 and 60-100 cm and for 5-year intervals. Data is available via STAC.OpenLandMap.org and via Google Earth Engine under the CC-BY license. This is the first ever global 30-m spatial resolution soildb that can be used to serve various land monitoring projects and was specifically created to support the UNCCD's Land Degradation Neutrality programme and similar international programmes where focus is on improving soil health, increasing SOC and decreasing soil degradation (soil erosion, loss of soil biodiversity, compaction, salinization and similar). The most important variables for predicting soil organic carbon density (kg m-3) were: soil depth, Landsat-based uncalibrated Gross Primary Productivity (GPP), Normalized Difference Vegetation Index (NDVI) and CHELSA bioclimatic indices. The global distribution of soil pH can be primarily explained by the CHELSA Aridity Index (long-term), annual precipitation, and salinity grade. Detecting key variables controlling dynamics of soil properties helps improve soil management for the decades to come. Tom Hengl (OpenGeoHub) 2025-09-17T16:10:00+00:00 16:10 00:20 Aula Magna open-earth-monitor-global-workshop-2025-361-high-resolution-global-maps-of-cocoa-farms-extent https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/MAHPTK/ false Oral talk en Cocoa cultivation serves as a cornerstone of many agricultural economies across the globe, supporting millions of livelihoods and contributing significantly to global cocoa production. However, accurately mapping cocoa farm locations remains a challenging endeavor due to the complex and heterogeneous nature of the landscapes where cocoa is cultivated. Traditional mapping techniques often fall short in capturing the intricate spatial patterns of cocoa farming amidst dense vegetation, varying land cover types, farming practices and growing stages (Masolele et at., 2024). Moreover, the current mapping efforts mainly focus on two major producing countries, Ivory Coast, and Ghana (Kalischek et al., 2023). Thus, little is known about the location of cocoa farms in other cocoa producing regions, posing a challenge to the sustainability and economic contributions of the cocoa crop. To address this challenge, we first present a benchmarking approach for mapping commodity crops worldwide. Here we compare different spectral, spatial, temporal and spatial-temporal methods for mapping commodity crops. The benchmarking is based on a variable combination of Sentinel-1 and Sentinel-2, locational and environmental variables (temperature and precipitation). We use a comprehensive list of reference data spanning 36 cocoa-producing countries to do this task. Higher accuracy (F1-score 87%) is obtained when using a model that employs spatial-temporal remote sensing images plus locational and environmental information, compared to other models without locational and environmental information. Secondly, for demonstration, we employ the developed deep learning methodologies to map the locations of cocoa farms across the Globe with an F1-Score of 88%. By leveraging the rich spatio-temporal information provided by Sentinel-1 and Sentinel-2 satellite data, complemented by location encodings, temperature and precipitation data, we have developed a robust and accurate cocoa mapping framework. The developed deep learning algorithm extracts meaningful features from multi-source satellite imagery and effectively identifies cocoa farming areas. The integration of Sentinel-1 and Sentinel-2 data offers a synergistic approach, combining radar and optical sensing capabilities to overcome the limitations of individual sensor modalities. Furthermore, incorporating location encodings into the modeling process enhances the contextual understanding of cocoa farm distributions within their geographical surroundings. Through this research effort, we provide the first high-resolution global cocoa map giving, valuable insights into cocoa farm locations, facilitating sustainable cocoa production practices, land management strategies, and conservation efforts across the pan-tropical forests, where cocoa farming occurs. The work aligns with recent European Union (EU) regulations to curb the EU market’s impact on global deforestation and provides valuable information for monitoring land use following deforestation, crucial for environmental initiatives and carbon neutrality goals (European Commission., 2024). Specifically, our product can support monitoring and compliance of the European Union (EU) Regulation on Deforestation-free Products (EUDR, No 2023/1115) by identifying the previous existing and current cocoa farm expansion after the cut-off date of December 31, 2020. Within the framework of the ESA funded WorldAgroCommodities project, this mapping approach is now being converted into an operational cloud-based service on the Copernicus Data Space Ecosystem, allowing easy access to these crucial tools for the National Competent Authorities in light of enforcing the EUDR regulation. Furthermore, our findings hold significant implications for cocoa farmers, agricultural policymakers, and environmental stakeholders, paving the way for informed decision-making and targeted interventions to support the resilience, sustainability and traceability of cocoa farming systems worldwide. Robert Masolele 2025-09-17T16:30:00+00:00 16:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-301-towards-the-development-of-a-serbian-ground-motion-service-gms-serbia-using-sentinel-1-insar-data-necessity-opportunities-and-future-directions https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/B3GWYZ/ false Oral talk en The development of a national ground motion monitoring system has become an essential tool of remote sensing for addressing geological hazards, urban planning, and infrastructure management. Over the past few years, several countries have successfully implemented such services, highlighting their importance in mitigating risks and supporting sustainable development. Inspired by these global trends and the advancements in satellite technology, this research proposes the creation of a Serbian Ground Motion Service (GMS-Serbia). Leveraging the Sentinel-1 mission, operational since 2014, and its advanced Interferometric Synthetic Aperture Radar (InSAR) capabilities, GMS-Serbia would provide high-resolution ground motion data to monitor subsidence, landslides, and other deformation phenomena across Serbia. GMS-Serbia will rely on advanced Differential InSAR techniques (ADinSAR), such as Persistent Scatterer and Small Baseline InSAR. The recent launch of Sentinel-1C has further enhanced data availability, offering improved coverage and revisit frequency, making this an ideal time to establish a dedicated national service. This research emphasizes the necessity of GMS-Serbia, particularly as Serbia is not covered in the European Ground Motion Service (EGMS), a regional initiative covering much of Europe. By filling this gap, GMS-Serbia would not only address national needs but also contribute to regional and global efforts in ground motion monitoring. The proposed service would provide actionable insights for disaster risk reduction, urban planning, and infrastructure development, while also fostering collaboration with existing international initiatives. This work outlines the conceptual framework, methodological approach, and future directions for GMS-Serbia, highlighting its potential to enhance Serbia's resilience to geological hazards and support sustainable development in the context of a rapidly changing environment. The importance of ground motion monitoring in the context of contemporary geospatial and environmental challenges cannot be overstated. As natural disasters, such as landslides, subsidence, and seismic activity, increasingly threaten infrastructure and communities, the ability to detect and monitor ground deformation in real-time becomes vital for disaster preparedness, urban resilience, and sustainable development. Ground motion monitoring using satellite-based remote sensing technologies, particularly InSAR, has proven to be an invaluable tool in addressing these issues across the globe. However, while many countries have already established their own national services to monitor ground motion, Serbia remains without such a system, despite the increasing need for precise and reliable data for decision-making across various sectors. This session will provide an in-depth look at the proposed GMS-Serbia service, exploring its conceptual framework, methodological approach, and potential impact on the national and regional landscape. Attendees will gain insights into the technologies behind SAR based ground motion monitoring, including the latest advancements in InSAR and the integration of Sentinel-1 data, and how these can be leveraged to address the pressing challenges faced by Serbia and its neighbors. /media/open-earth-monitor-global-workshop-2025/submissions/B3GWYZ/Session_image_8dl72lU.png Miloš Basarić 2025-09-17T13:30:00+00:00 13:30 01:30 Aula 2 (workshops) open-earth-monitor-global-workshop-2025-340-openlandmap-soildb-global-dynamic-soil-data-tutorial https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/GFBHAC/ false Workshop proposal en The workshop will provide instructions on how to access and use OpenLandMap-soildb: a global 30-m spatial resolution dynamic soil database showing distribution of soil carbon, soil pH, soil texture fractions, bulk density and soil types (USDA subgroups); soil carbon, pH are modeled as dynamic soil properties with 5--year interval; soil texture fractions, bulk density and soil type as static variables. This is the first 30-m dynamic soildb with properties mapped through depth (0-30, 30-60, 60-100) and time. Two tutorials will be provided: (1) in R, and (2) in python. In both tutorials we will show how to list available layers, retrieve values per point or polygon and how to correctly use and interpret the values. The OpenLandMap-soildb data is available from https://stac.openlandmap.org. The tutorials will be made available via https://github.com/openlandmap/soildb. To cite layers distributed via OpenLandMap-soildb please use: Hengl, T., Consoli, D., Tian, X., Nauman, T. W., Nussbaum, M., Isik, M. S., Parente, L., Ho, Y.-F., Simoes, R., Gupta, S., Samuel-Rosa, A., Zborowski Horst, T., Safanelli, J. L., and Harris, N., (2025??). OpenLandMap-soildb: global soil information at 30~m spatial resolution for 2000--2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations. Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-336, in review, Tom Hengl (OpenGeoHub) 2025-09-17T15:30:00+00:00 15:30 00:45 Aula 2 (workshops) open-earth-monitor-global-workshop-2025-324-multi-language-support-for-image-time-series-analysis-using-sits https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/YET9ZL/ false Workshop proposal en The SITS package (Satellite Image Time Series) is designed for the analysis and classification of satellite image time series using machine learning. It provides a comprehensive framework for managing, modelling, and classifying time series data derived from remote sensing imagery. In version 1.5.3, SITS supports both R and Python APIs and has included support for CDSE and OGH cloud providers. SITS supports large-scale operational analysis on data cubes, and has state-of-the-art functions for deep learning, post-processing, uncertainty estimation, and texture measures. It allows the merging of Sentinel-1 and Sentinel-2 data, as well as Landsat data with Sentinel-2, and enables the inclusion of DEM and climate data as additional bands. Gilberto Camara 2025-09-17T09:25:00+00:00 09:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-332-open-eo-based-monitoring-of-drought-flood-abrupt-alternation-in-northwestern-mediterranean-basins https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/SKHBW7/ false Poster presentation en Drought–flood abrupt alternation poses a growing threat to water security and climate resilience in the Northwestern Mediterranean region. Despite the increasing availability of Earth Observation (EO) data, there is a lack of operational, scalable tools to detect early signals of these sudden hydrological transitions. This study presents an open EO-based framework to monitor such events by fusing anomalies in soil moisture (SM), evapotranspiration (ET), and precipitation (P). Standardized monthly deviations are computed over the last 10 years using high-resolution datasets from the Digital Twin Earth (DTE) platform and Copernicus services. These anomaly patterns are processed through a cloud-based geospatial environment to capture spatiotemporal divergence across key Mediterranean basins, reflecting the complex interactions of SM, ET, and P under climate stress. The resulting study identifies zones exhibiting frequent and intense transitions, which are prioritized for detailed monitoring and early warning. The proposed approach is reproducible and scalable, offering valuable support for open monitoring systems and anticipatory water risk management in vulnerable regions. imane serbouti 2025-09-17T10:25:00+00:00 10:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-356-bridging-communities-how-open-source-geospatial-software-stays-relevant-in-science-policy-and-industry https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/98CNWA/ false Poster presentation en The open source software for geospatial is today a mature, reliable and ever-expanding ecosystem. Paramount FOSS4G projects, such as GRASS, GDAL, QGIS, Geoserver, PostGIS and many others, have been developing for decades, ever improving and adding to their functionalities, as well as a community of developers and users alike. Furthermore, given the fundamental principles of the open source paradigm, the plethora of FOSS4G is constantly increasing, following the technology trends and ever renewing requirements of users. Even so, given the economics of open source, the viability question still remains. What makes an open source for geospatial project successful, viable over time? Based on the more extensive initiative - the FOSS4G Observatory - the authors will present an in-depth analysis on the potential connections between the heart of a “health open source project” and “software metrics” in regards to the project viability over the long term. Expanding on sustainability matters in the open source, efforts have been invested in deciphering what are the elements that support the uptake of FOSS4G within operational activity, be it scientific-, policy- or commercially related activities, irrespective of its language. All of the three sectors are governed by different driving principles and best practices when it comes to addressing the development, management and use of the open source environment. Codrina Maria Ilie 2025-09-17T11:25:00+00:00 11:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-355-from-dry-to-desiccated-a-new-paradigm-for-flash-drought-monitoring-over-india https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/DGXU8Y/ false Poster presentation en The frequency of flash droughts, characterized by their sudden and intense onset, is rising globally, posing significant challenges in drought monitoring. However, consensus on whether flash droughts are becoming the new norm remains unclear, as slow-developing droughts may also be increasing. Flash droughts have transient but severe consequences on agriculture productivity, water resources, and ecosystems. Despite the urgency, researchers have not thoroughly investigated the key features of flash droughts in India, and they have not adequately addressed the mechanisms behind rapid soil moisture depletion during these events. This study proposes a framework for detecting flash droughts, which defines them based on the rapidity of soil drying at the onset of the drought and extends to its duration. The analysis further focused on flash drought characterization, i.e., frequency, mean duration, mean severity, and mean onset speed under observed climate continuous from 1981 to 2022 over India. Atmospheric aridity likely creates flash drought-prone environments. The combined effects of atmospheric aridity and soil moisture depletion increase the frequency of flash droughts. Under observed climate conditions, the frequency of regional flash droughts remained high in the core monsoon region. The north-west (NW) and central north-east (CNE) regions experienced more frequent flash droughts. The west-central (WC) and peninsular region (PR) experienced moderate to low magnitudes of flash drought events. In addition, the average length of time and severity of the events stayed high in the CNE and NW regions, while the flash droughts were very short and mild in the WC and PR regions during the adapted period. These findings emphasize the need to adapt to the increasing occurrence of rapid-onset droughts in a changing climate, which can significantly impact crop production and pose challenges for agricultural irrigation. Understanding of the characteristics of these rapid and severe drought events is essential for enhancing resilience and preparedness. Keywords: Flash droughts, Soil-moisture, Drought characterization, India. 1) The proposed framework identifies flash drought events by their rapid intensification rate and prolonged dry conditions. 2) The core monsoon region in India exhibits a high frequency of flash drought events spatially consistence with duration, severity, and onset speed. 3) Flash droughts occur more frequently due to the combined impacts of soil-moisture depletion and increased atmospheric aridity. /media/open-earth-monitor-global-workshop-2025/submissions/DGXU8Y/Flash_Drought_bQgp9n1.jpg VAIBHAV KUMAR 2025-09-17T12:25:00+00:00 12:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-353-improving-the-reconstruction-of-the-hydrological-cycle-through-satellite-observations-the-case-study-of-the-po-river-basin https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/QYZZJE/ false Poster presentation en In the context of climate change, increasing competition for freshwater use across various sectors is intensifying pressures on water resources, placing many countries at heightened risk of water scarcity. To mitigate the growing risk of water scarcity, it is imperative to reduce water usage intensity across agriculture, industry, energy production, and domestic sectors. Achieving this requires a comprehensive and detailed understanding of water consumption patterns in each sector, and estimating water storage in groundwater, reservoirs, and snowpack is essential to safeguard water availability for future generations. The Po River basin in northern Italy has experienced significant hydrological droughts in recent decades (1990-2023), highlighting the need to understand the complex interactions between climate factors and human activities. This study, conducted as part of the INTERROGATION project funded by the Italian Ministry of Universities and Research, presents an integrated approach for water resource management during drought events. The study employs a flexible conceptual hydrological model (MISDc - Modello Idrologico Semistribuito in Continuo) that incorporates both natural processes and anthropogenic influences. The model is driven by three distinct precipitation datasets: long-term (2000-2023) daily in-situ measurements, high-resolution (1.8km) reanalysis data, and high-resolution (1km) satellite precipitation data. The Bluecat tool (Montanari et al., 2022) is utilized to evaluate the uncertainty in modelled river discharge. The model's performance is validated using multiple satellite-derived observations including soil moisture, evaporation, groundwater, irrigation, and snow accumulation data developed within the framework of European Space Agency Digital Twin Earth (DTE) Hydrology Next project. The model is capable to reproduce both natural hydrological processes and anthropogenic activities such as irrigation and reservoir operations. Results demonstrate the effectiveness of combining accurate satellite observations with a well-calibrated hydrological model for capturing spatiotemporal variations in the hydrological cycle within highly anthropized basins. This integrated framework provides valuable insights for developing a decision support system to guide stakeholders in managing water resources during future drought events in the Po River basin. Sindhu Kalimisetty 2025-09-17T14:25:00+00:00 14:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-333-using-jensen-shannon-distance-to-better-understand-the-role-of-landscape-heterogeneity-in-the-relationship-between-tropomi-sif-product-and-gross-primary-production https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/STMSRS/ false Poster presentation en Sun-Induced Fluorescence (SIF) is considered to be a valuable signal detectable from space that provides direct information about Gross Primary Production (GPP). Previous studies have shown a high correlation between SIF estimated from satellite observations and GPP predicted using satellite images and machine learning techniques. Many times, SIF and GPP products are trained and validated from in-situ measurements, however, often a perfect match is assumed between the area sensed by the satellite and the area sensed in-situ. For this reason, it is important to quantify the representativeness of the in-situ observations when compared with satellite products at coarser resolution. In the present work, we evaluated the representativeness of different eddy covariance towers footprints when compared with TROPOMI SIF ungridded product. To quantify the representativeness, we quantify the amount of information shared by the vegetation around the tower and the vegetation sensed by the Sentinel-5p satellite based on Sentinel-2 data cubes and Jensen-Shannon distance. We expect that characterizing the mismatch with this Jensen-Shannon distance will help improve the correlation between SIF from the satellite and GPP estimations from the tower. Finally, to guarantee that our analysis fulfills the FAIR principles, we will also present a general workflow to run the analysis on-demand using the Copernicus Data Space Ecosystem infrastructure. Daniel E. Pabon-Moreno 2025-09-17T15:25:00+00:00 15:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-331-incremental-steps-towards-near-real-time-enhanced-drought-monitoring-combining-remote-sensing-and-model-based-soil-moisture-products https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/KKRARH/ false Poster presentation en The initiatives to facilitate access to open data cubes and results of digital twins for Earth systems analysis and early warning are gaining momentum. Successful experiences like the Open Earth Monitor Cyberinfrastructure are leading to an increasing awareness and experience in the governance of these datasets. However, data providers (i.e. models, earth observation missions) increasingly offer data in a near-real-time basis presenting the next challenge in the comprehensive integration of datasets into open Earth cyberinfrastructures. Soil moisture is one of the crucial state variables that are currently transitioning to near-real-time data provision. In this study, we explore the potential of two soil moisture near-real-time data providers to generate end-user early warning drought monitoring capabilities. The study evaluates the feasibility of generating near-real-time (daily) merged soil moisture anomaly maps by merging the recent EUMETSAT ASCAT H122 6.25km resolution surface soil moisture product with the near-real-time outputs of GLOFAS4 modelling system from the European Flood Awareness System (EFAS) at a continental scale. Experiences gained on assessing the strengths and weaknesses of the two types of data in the framework of the Open Earth Monitor Cyberinfraestructure across scales are contrasted with the insights collected in the near-real-time workflow design for the aim of this study. In particular, from the side of data applicability, the study assesses both the coverage and consistency of near-real time anomalies ‘dynamic’ estimates compared to ‘static’ estimates from the ones generated using climate data records of the same products trying to elucidate the actual worth and capabilities of the claim near-real time capacity of the input products. The study secondarily focuses on the strengths and weaknesses of merging data from distinct data types (e.g. model-based and remote-sensing) with special attention to their suitability for identifying the different ranges of events relevant for monitoring (i.e. the progressive changes in anomalies versus those of extreme events). Therefore, the purpose of this study is to provide an outlook on the incoming opportunities and barriers of processing data at near-real-time for its integration into data cubes and digital twin systems within the framework of the accelerating community efforts to provide readily accessible and operational eErth system data for end-users. Jaime Gaona 2025-09-17T16:25:00+00:00 16:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-270-digital-public-infrastructure-for-ecological-variables-an-indian-approach-to-public-service-delivery-meets-global-best-practices-for-disseminating-climate-data https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/MXHPBU/ false Poster presentation en Digital public infrastructure (DPI) have been a recently coined term for a framework for digitally delivering delivering public services. Characterized by 3 core tenets: open data, open standards and open source software, it has already found governments across the world interested in adopting solutions for identity management, financial transactions and e-commerce. Through our research paper, we explore how these principles can be applied to disseminate data and insights collected by remote sensing and geosciences departments and how they can inform climate action strategies, such as the formulation of heat action plans. The paper highlights the current problems in the ecosystem collecting, processing and distributing this data in India today, formulates design principles that can help mitigate these challenges, and proposes the way forward. Trishal Kumar 2025-09-17T17:25:00+00:00 17:25 01:00 Aula 3 (Posters) open-earth-monitor-global-workshop-2025-390-multi-sensor-snow-cover-assessment-over-the-mediterranean-region https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/ACUVCN/ false Poster presentation en Snow cover plays a crucial role in Mediterranean water resources, serving as a natural reservoir that regulates seasonal water availability and supports hydrological processes across mountainous catchments. However, monitoring snow across this diverse and topographically complex region remains challenging due to the limited availability of in-situ observations and high spatial variability. This study analyzes long- term snow cover dynamics across the Mediterranean region and its four major river basins (Po, Tiber, Crati, and Ebro) using three satellite datasets: the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Sentinel-1 (S-1). Through a systematic comparison of Snow Cover Ground Fraction (SCGF) data, we characterized spatial patterns, temporal trends, and responses to extreme events across the Mediterranean region. Mean annual SCGF patterns exhibit distinct spatial gradients, with mountainous regions displaying the highest snow accumulation while coastal and lowland areas remain predominantly snow-free throughout the year. MODIS data, benefiting from superior spatial resolution, captures finer-scale spatial patterns compared to AVHRR observations. Anomaly analyses during extreme climatic events, including the 2005 and 2022 droughts, show spatially coherent patterns across both AVHRR and MODIS. The 2022 drought is marked by widespread negative anomalies over the Mediterranean region. Cross-sensor validation confirms a strong agreement between AVHRR and MODIS across most areas, with S-1 snow depth data further supporting the accuracy of snow detection. Performance consistency varies substantially by basin when AVHRR is compared with S-1: mountainous regions, such as the Po basin, exhibit the highest inter-sensor agreement, while smaller basins, including Crati and Tiber, show greater variability due to their topography and geographic location. Regional-scale trend analysis using AVHRR data reveals statistically significant declines in snow cover over recent decades, although basin-level trends remain obscured by pronounced interannual variability. These findings demonstrate the value of multi-sensor satellite observations for monitoring snow cover dynamics in this climatically sensitive Mediterranean region, highlighting both the complementary nature of different remote sensing platforms and the spatial heterogeneity of snow cover responses to climate variability. Mohsin Tariq 2025-09-18T09:30:00+00:00 09:30 00:30 Aula Magna open-earth-monitor-global-workshop-2025-374-preventing-catastrophic-climate-change-the-role-of-in-situ-data-and-citizen-collected-observations https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/9K3D9B/ false Keynote lecture en This presentation showcases innovative tools—from user-friendly apps for visual interpretation and machine learning–ready data collection to in-situ observation tools embedded in the Geo-Quest app—that empower citizens to contribute meaningfully to land-use monitoring and climate action. Global warming has already exceeded the critical 1.5°C threshold, and fossil fuel emissions continue to rise by approximately 2% annually—despite the urgent need for reductions of at least 8% per year. This presentation highlights key actions required to avert catastrophic climate change, with a particular focus on the terrestrial carbon sink and the critical roles of land use and land-use change. However, it must be emphasized: technological innovation alone will not be enough. What is urgently needed are mindset shifts and transformative societal changes—reaching social tipping points that can drive sustained and meaningful climate action. While data improvements may have only a limited direct effect on emissions, they are essential to inform better policies, ensure transparency, and mobilize public engagement. A deeper understanding of land-use dynamics is especially important in countries with limited capacity for regular forest inventories or land-use change monitoring. Emerging technologies—such as the new P-band radar sensors aboard Europe’s BIOMASS mission—offer promising opportunities to improve our knowledge of carbon stocks and assess the restoration potential of forests, peatlands, and other ecosystems. Today, high-resolution maps can be generated with just a few clicks, enabled by increasingly accessible algorithms and open-source tools. Yet their full potential depends on the availability of high-quality training and validation data—an area where citizen engagement can play a key role. Citizen participation in data collection not only improves coverage but also strengthens public awareness and accountability. This presentation showcases innovative tools—from user-friendly apps for visual interpretation and machine learning–ready data collection to in-situ observation tools embedded in the Geo-Quest app—that empower citizens to contribute meaningfully to land-use monitoring and climate action. Steffen Fritz 2025-09-18T10:00:00+00:00 10:00 00:30 Aula Magna open-earth-monitor-global-workshop-2025-320-ten-years-of-advancing-forest-disturbance-monitoring-with-sentinel-1-radar https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/GEDAFD/ false Keynote lecture en Over the past decade, Sentinel-1 has become vital for radar-based forest disturbance monitoring. Its cloud-penetrating radar delivers consistent, gap-free observations every 6–12 days in the tropics and nearly daily in northern latitudes, enabling reliable near-real-time monitoring even in cloudy regions. With 10 m detail and sensitivity to vegetation structure, Sentinel-1 has transformed detection of fine-scale disturbances like small-scale farming, road building, and selective logging. We show how Sentinel-1 has advanced forest monitoring. Early efforts developed near-real-time change detection, demonstrating that frequent observations can offset C-band radar’s lower sensitivity. Open data access, combined with cloud computing and open-source tools, allowed us to scale up methods into the operational Radar for Detecting Deforestation (RADD) alerts. Updated weekly and covering 55 pan-tropical countries, RADD alerts are freely available through Global Forest Watch and support law enforcement, supply chain monitoring, and research. We also share lessons from expanding RADD to new regions, including Europe. Advances include radar texture-based detection, monitoring in temperate and boreal forests, and monthly road mapping. New developments enable tracking of forest loss drivers and intra-annual carbon loss, and continental-scale commodity mapping. Future improvements will benefit from combining Sentinel-1 with optical sensors and upcoming radar missions (NISAR, BIOMASS). Johannes Reiche 2025-09-18T11:00:00+00:00 11:00 00:20 Aula Magna open-earth-monitor-global-workshop-2025-348-open-earth-monitor-implementation-on-openeo https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/9FWUEC/ false Oral talk en OpenEO is an emerging open standard for processing large Earth Observation datasets on cloud infrastructure. The most well-known openEO backend providers are Copernicus Data Space Ecosystem and OpenEO Platform, both running on European cloud infrastructure. Several Monitors in the Open Earth Monitor Cyberinfrastructure project have been ported to openEO, which makes their workflow more transparent and accessible for a wider audience. Namely, these are the pantropical monitor of land use following deforestation, the European monitor of wet snow, and the European monitor of air quality. This presentation will go over how these use cases were implemented on openEO, the current status and remaining challenges of the openEO implementation, and future outlook. In addition, the participants will be provided with an opportunity to try out the monitors themselves. Dainius Masiliunas 2025-09-18T11:20:00+00:00 11:20 00:20 Aula Magna open-earth-monitor-global-workshop-2025-360-mapping-land-use-following-deforestation-across-the-pan-tropics-with-sentinel-data https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/PEZNQY/ false Oral talk en Tropical forests are biodiversity hotspots, providing critical ecosystem services that sustain millions of plant and animal species. However, these forests are increasingly threatened by human activities, through the expansion of commodity crops such as soy, oil palm, rubber, cocoa, coffee, corn, logging, avocado, and pasture (Masolele et al., 2022, 2024). While significant efforts have been made to monitor deforestation using satellite imagery, most initiatives stop at detecting forest loss without tracking the land use that follows (Hansen et al., 2013). Understanding post-deforestation land use is crucial for addressing deforestation's root causes and mitigating its impacts (Masolele et al., 2022, 2024). Currently, there is no global monitoring system capable of providing annual, spatially detailed updates on the land use that follows after deforestation. Existing datasets and methods frequently lack the spatial, thematic, and temporal resolution necessary to accurately map post-deforestation land uses (Curtis et al., 2018), limiting their utility for targeted rapid policy response and regulatory compliance, such as the European Union’s Deforestation Regulation (EUDR) (European Commission., 2024). This gap poses challenges for ensuring EUDR compliance, limiting the capacity to detect and mitigate deforestation linked to commodity production. Here, we present the first high-resolution (10 m) maps of land use following deforestation covering the entire pan-tropics. We utilize an extensive reference database containing 23 different land use types (including, soy, oil palm, rubber, cocoa, coffee, corn, logging, avocado, mining, cashew, corn, sugar, rice, and pasture), and employ Sentinel-1 and Sentinel-2 data combined with deep learning algorithms, to map land use following tropical deforestation from 2001 to 2023 with an F1-score of 83%. Our approach incorporates location encodings and environmental variables, such as elevation, temperature, and precipitation, to enhance the model’s ability to distinguish various land uses across diverse geographies. In general our results shows increased deforestation as a result of expansion of key commodity crops such as cocoa in Liberia, Cameroon, Ivory Coast, Ghana, Ecuador, Peru, Papua New Guinea; oil palm, in Indonesia, Malaysia; rubber in Malyasia, Thailand, Laos, Indonesia; coffee in Central America (Guatemala, Nicaragua, Costa rica), Peru, Ethiopia, Colombia, Vietnam; soy in Brazil; pasture in Paraguay, Bolivia, Mexico, Brazil, Cashew in in Cambodia, Tanzania, Mozambique, Benin and, logging in Suriname, Guyana, Papua New Guinea, Equatorial Guinea, Gabon, Republic of Congo, and Cameroon. This work directly supports the European Union’s Deforestation Regulation (EUDR), aimed at curbing the EU market’s contribution to global deforestation (European Commission., 2024). Our research offers crucial insights for monitoring land use following deforestation, aiding environmental conservation initiatives and advancing carbon neutrality goals by providing detailed, high-resolution maps on land use that follows after deforestation events. Robert Masolele 2025-09-18T11:40:00+00:00 11:40 00:20 Aula Magna open-earth-monitor-global-workshop-2025-308-advancing-high-resolution-drought-monitoring-evaluating-remote-sensing-soil-moisture-products-for-integration-in-oemc-drought-monitoring https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/ABRPXE/ true Oral talk en Drought is a natural hazard caused by a precipitation deficit and consequent hydrological imbalance (Pachauri et al., 2014; Trenberth et al., 2014), with significant economic and environmental impacts, particularly in agriculture and forests. Although, ground-based observations provide high accuracy for drought-related parameters such as precipitation, temperature, and soil moisture, they lack in coverage and cost, making them unsuitable for large-scale, high-resolution assessments. In contrast, remote sensing technologies offer a cost-effective alternative, providing continuous spatial information over large regions. This study is conducted as part of the Open Earth Monitor (OEMC) project, which aims to develop a global, high-resolution system for drought monitoring. Our research focuses on identifying and improving existing approaches to create high-resolution monthly drought maps by exploiting drought indicators from ground station meteorological data and remotely sensed soil moisture. Soil moisture plays a key role in drought monitoring and prediction, especially in water-limited ecosystems (D'Odorico et al., 2007; Moran et al., 2004; Peters-Lidard et al., 2008), such as the Ebro Basin at northeast of Spain, the study area. In terms of the available soil moisture datasets, existing datasets often lack the resolution and reliability required for an effective assessment. To address this, a thorough review was conducted, various soil moisture products provide global coverage, but their coarse spatial resolution requires downscaling techniques to improve usability at local and regional scales. Since our focus is on developing a drought monitoring system with an agricultural emphasis, we prioritized products with spatial resolution 1km. A recent review (Brocca et al., 2024) on soil moisture products in Italy demonstrated that Sentinel-1 products show good agreement in terms of drought detection. Considering that drought is a long-term phenomenon, a minimum timescale is necessary for meaningful anomalies detection. However, high-resolution soil moisture data are available for a shorter period than meteorological data, that span from 1950 till today. Based on these, we selected two high-resolution datasets: the Sentinel-1 dual-polarization SAR (DPA) with a 1 km spatial resolution (Fan et al., 2025), and downscaled SMOS soil moisture data at 1 km resolution (Escorihuela et al., 2018; Merlin et al., 2013) (*), provides a longer temporal record. Our analysis compares these data sets across timescale to determine whether soil moisture data compliment the drought monitoring approaches. (*) The SMOS dataset used in this work was produced within the ACCWA project which has received funding from the European Union's H2020-MSCA-RISE-2018 programme under grant agreement No. 823965." Eirini Trypidaki 2025-09-18T13:30:00+00:00 13:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-345-restoration-at-scale-evaluating-the-progress-of-global-restoration-efforts-using-high-spatial-resolution-time-series-information-of-vegetation-traits-and-indices https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/XJETFR/ false Oral talk en Monitoring ecosystem restoration efforts at scale remains a significant challenge, despite their critical importance for ecosystem recovery and biodiversity conservation. Publicly funded satellite missions such as Sentinel-2 and Landsat offer opportunities for global-scale monitoring, thanks to their high spatial and temporal resolution, provided these data can be meaningfully linked to ecosystem characteristics. Here, we use a variety of remote sensing time-series products developed within the scope of the OEMC project consortium, including variables that quantify vegetation traits, indices, and soil health characteristics. These datasets are available at annual intervals for a period of up to 25 years, with a spatial resolution of 30 meters or higher. This is crucial for monitoring restoration efforts of smallholder farmers, given the often sub-hectare plot sizes. We apply our methodology to three restoration project data bases: (1) a controlled scientific experiment comparing the effects of different reforestation practices in Costa Rica, (2) a large global database of nature-based carbon offset projects, and (3) sites from the Restor.eco database, a global network of restoration projects. For each site, we analyze changes over time by comparing pre- and post-intervention trends and explore methods for identifying suitable control sites to isolate the effects of restoration. Altogether, this work supports global restoration tracking, empowering local farmers and smallholders by demonstrating that their efforts have an impact at a global scale. Felix Specker 2025-09-18T13:50:00+00:00 13:50 00:20 Aula Magna open-earth-monitor-global-workshop-2025-336-predicting-future-tree-species-suitability-across-europe-with-harmonized-forest-data-and-climate-ensembles https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/AV9QP3/ false Oral talk en To support climate-resilient forest planning across Europe, we are developing high-resolution suitability maps for 50 common tree species under future climate scenarios. The approach builds on a harmonized presence–absence dataset derived from over 270,000 National Forest Inventory (NFI) plots from 11 countries, complemented with publicly available records to ensure broad spatial coverage. Species–climate relationships are modeled using a suite of machine learning algorithms trained on historical climatologies and projected using bias-corrected outputs from five GCM–RCM chains within the EUR-11 domain, under RCP4.5 and RCP8.5. The modeling pipeline is designed to produce decadal projections at 1 km spatial resolution, allowing fine-scale exploration of ecological suitability from 2030 to 2100. To enhance predictive robustness, multiple algorithms are combined through ensemble methods, including stacking, using a limited set of ecologically relevant predictors. This work complements existing efforts in species distribution modeling by integrating high spatial and temporal granularity with a multi-model climate ensemble and a harmonized pan-European observational dataset. The resulting maps will be integrated into the EU reforestation planner tool, supporting long-term, spatially explicit strategies for tree species selection under changing climatic conditions. Carmelo Bonannella 2025-09-18T14:10:00+00:00 14:10 00:20 Aula Magna open-earth-monitor-global-workshop-2025-321-integration-of-radar-and-optical-data-for-identifying-tropical-forest-disturbances https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/7YX37V/ false Oral talk en This work integrates optical and radar data cubes to detect forest disturbances in tropical regions. Our method identifies initial degradation and selective logging, often precursors to deforestation, demonstrating its utility in early-warning systems. These results emphasizes the crucial role of integrating optical and radar data to improve the precision and dependability of monitoring systems, essential for sustainable forest management. These findings highlight the value of integrating multi-source data cubes to enhance precision in monitoring forest disturbances, thereby supporting more responsive and reliable environmental management. Gilberto CamaraFelipe Carvalho 2025-09-18T14:30:00+00:00 14:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-343-multi-source-fusion-framework-for-statistical-downscaling-of-global-monthly-precipitation https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/TMLCVN/ false Oral talk en This study introduces a global-scale framework for statistically downscaling monthly precipitation data to a high spatial resolution of 1 km for the period 2000–2024. We integrate satellite-derived, reanalysis-based, and in situ observational datasets using an ensemble fusion approach that leverages the strengths of multiple global products, including ERA5, CHELSA, and IMERG. Statistical downscaling methodology is implemented using ground-based meteorological station data to improve the representativeness of local precipitation patterns. The framework incorporates spatial predictors and temporal dynamics to transform coarse-resolution inputs into fine-scale monthly precipitation fields. The resulting dataset provides improved consistency and detail across diverse climatic regions and data-sparse environments. This high-resolution precipitation product is designed to support a range of applications, including hydrological modeling, drought and flood risk assessment, and climate change impact analysis. Overall, the proposed approach offers a scalable and replicable methodology for generating detailed precipitation estimates by harmonizing global datasets with in situ observations. Mustafa Serkan Isik 2025-09-18T15:30:00+00:00 15:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-381-developing-precipitation-within-digital-twin-earth-hydrology-leveraging-the-individual-strengths-of-multiple-products https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/YAQHQA/ false Oral talk en Several Digital Twins of the Earth are being developed in recent years, driven by the growing interest in integrating the latest advancements in Earth Observation (EO), modeling, artificial intelligence, and computational power to make them accessible to the scientific community and interested parties. Such platforms are highly valuable in supporting sustainability efforts and combating climate change, enabling the visualization, analysis, and prediction of the natural system - including human activities and their influence. The European Space Agency (ESA) also shown interest in this framework by launching the DTE Hydrology project, which focuses on analyzing the water cycle and its key components using the latest satellite observations and models. A critical aspect of the project involves the development of high-resolution (at least 1 km, daily) datasets for essential water cycle variables, aimed at replicating hydrological behavior and understanding interactions with human systems. Among these variables, precipitation plays a central role due to its impact on agriculture, economic stability, water resource planning, and disaster risk reduction. Globally, ground-based observation networks for precipitation monitoring are declining due to political and economic constraints, forcing many regions to rely on less accurate precipitation datasets, affected by the decreasing gauge density. In this context, satellite-derived precipitation estimates have the potential to improve precipitation estimates by filling both spatial and temporal data gaps. However, numerous precipitation products have emerged over the years, each with their own strengths and limitations, making it challenging for users to determine the most suitable product for their study area. To overcome this issue and capitalize on the individual strengths of each datasets, the DTE- Hydrology initiative has developed a combined precipitation product that merges multiple sources, including satellite-based and reanalysis datasets, into a unified, enhanced product. Specifically, precipitation estimates from IMERG-Late Run, SM2RAIN ASCAT (H SAF), and ERA5 Land are downscaled at 1 km spatial resolution and subsequently merged using pixel-based weights derived from the application of the Triple Collocation method. The final merged product was thoroughly validated and compared against a wide range of datasets—both coarse-resolution sources such as H SAF, IMERG-LR, ERA5, EOBS, PERSIANN, CHIRP, GSMAP, and fine-resolution datasets like EMO, SAIH, COMEPHORE, and MCM—demonstrating its high reliability and strong performance. Paolo FilippucciLuca CiabattaLuca BroccaChristian Massari 2025-09-18T15:50:00+00:00 15:50 00:20 Aula Magna open-earth-monitor-global-workshop-2025-352-assessing-the-impact-of-next-generation-gravity-missions-on-precipitation-estimation-over-europe https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/XD9SXB/ false Oral talk en Precipitation estimation, SM2RAIN, TWS, NGGM, MAGIC, Synthetic experiments The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide observations of terrestrial water storage (TWS) dynamics on regional to global scales. However, they lack high spatio-temporal resolution, making them challenging to interpret different hydrological fluxes. A join collaboration between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), initiated a decade ago, is known as the Mass- change And Geosciences International Constellation (MAGIC). The aim of this collaboration to launch new high resolution missions in order to improve capacity for monitoring extreme events such droughts and floods. The primary objective of this work is to examine the impact of improving the spatial-temporal resolution of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments on a European scale. The study employed SM2RAIN algorithm by inverting the soil water balance equation to estimate the rainfall accumulated between two consecutive TWS measurements (Brocca et al., 2014). Initially, the ERA5L based TWSA at daily time scale was incorporated into SM2RAIN to check reliability of the model against ERA5L precipitation with spatial resolution of 100 km over Europe with range of latitudes 30 to 60°N and longitudes 10°W to 50°E. The results shows SM2RAIN exhibited satisfactory performance at a daily temporal resolution, with mean values of R, RMSE, BIAS (0.85, 13.76, -0.29) against ERA5L precipitation. Based on statistical analysis, SM2RAIN-simulated precipitation shows good agreement across the most of Europe except in some areas of the northern Italy, northeastern states (Estonia, Latvia) and costal regions of Norway. Subsequently, synthetic experiments were developed by degrading the temporal resolution of TWS data from daily into 5-day interval and by introducing error ranging from 1.9 mm to 42 mm. The results shows that degrading temporal resolution and larger error make the model quite difficult to capture and represent meaningful rainfall patterns, as the error completely overshadows the underlying dynamics captured in the SM2RAIN-simulated rainfall. The results of the study clearly highlight the benefit of NGGM and MAGIC in improving our capability to estimate various hydrological components relying on satellite data as inputs. References Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119(9), 5128–5141. Muhammad Usman Liaqat 2025-09-18T16:10:00+00:00 16:10 00:20 Aula Magna open-earth-monitor-global-workshop-2025-349-leveraging-earth-observation-to-monitor-the-most-impactful-yet-unknown-human-activity-on-the-water-cycle-irrigation https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/ENZYEY/ false Oral talk en Water resources are essential for agricultural, industrial, and domestic uses, ensuring high standards of living. Among these, agriculture accounts for the vast majority of global water ab-stractions, far surpassing the uses referring to other sectors. Par-adoxically, it remains one of the least understood. Detailed, ex-plicit information on irrigation practices is still largely unavaila-ble or inadequately monitored at the global scale. In recent years, Earth Observation (EO) technologies have opened up new possibilities for monitoring irrigation dynamics, both in detect-ing irrigation occurrence in space and time and in quantifying the volumes of water used. This work presents recent advances in monitoring irrigation dynamics through innovative satellite-based approaches: the TSIMAP (Temporal-Stability-derived Irri-gation MAPping) method, aimed at mapping irrigated areas us-ing satellite data, and the Soil Moisture (SM)-based inversion approach, which estimates irrigation water use. TSIMAP is a versatile methodology, successfully applied across various cli-matic regions and at different spatial resolutions. The SM-based approach, on the other hand, has enabled the creation of the first-ever high-resolution datasets of irrigation water use an im-portant step for evaluating the hydrological impact of irrigation. Recently, this method has also been implemented operationally, demonstrating its potential for building satellite-based agricul-tural water monitoring systems. Along with results from the methodologies above, this contribution will also focus on future challenges in the field of irrigation monitoring from space. Jacopo Dari 2025-09-18T13:30:00+00:00 13:30 00:45 Aula 2 (workshops) open-earth-monitor-global-workshop-2025-302-streamlining-snow-monitoring-with-openeo-and-cdse https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/Y3FBNN/ false Workshop proposal en Snow monitoring plays a crucial role in the effective management of water resources. The increasing availability of remote sensing data offers significant advantages but also introduces challenges related to data accessibility, processing, and storage. Leveraging a cloud-based platform such as the Copernicus Data Space Ecosystem (CDSE) offers an efficient solution by enabling data processing directly where the data are stored. Specificaaly, our workflows are built using the openEO API, providing a standardized interface for accessing and processing large Earth observation datasets. In this practical session, we will demonstrate fundamental yet powerful applications for snow monitoring. Participants will explore examples including snow cover classification using state-of-the-art and advanced machine learning techniques, wet snow detection, and snow albedo estimation. The exercises will highlight how various sensors and methods can be exploited to achieve desired outputs. By the end of the workshop, attendees will gain hands-on experience with openEO tools and understand how cloud-based infrastructures can streamline large-scale environmental data processing. Valentina Premier 2025-09-18T15:30:00+00:00 15:30 00:45 Aula 2 (workshops) open-earth-monitor-global-workshop-2025-344-federal-workflow-to-acess-gedtm30-and-improve-it-with-airborne-lidar https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/QZBPBM/ false Workshop proposal en This workshop provides a hands-on tutorial to merge to improve global digital terrain models (DTMs) by high-quality local LiDAR data while maintaining consistency with a standardized global framework. The session complements the oral presentation "A Framework of Federal Global Ensemble Terrain Model" and offers participants a practical workflow to empower GEDTM30 data users to improve GEDTM30 for local application. The workshop is structured in three parts: (1) Introduction and Data Access: Participants will learn to access and visualize GEDTM30 elevation data through STAC-compliant endpoints and understand its spatial structure and metadata. (2) Local LiDAR Integration: This section focuses on preprocessing local high-quality LiDAR-derived DTMs, resampling them to match the GEDTM30 30-meter grid, and incorporating them into the ensemble model to enhance local accuracy while preserving global consistency. (3) Land Surface variable Derivation and Validation: Participants will derive surface parameters (e.g., slope, aspect) from the enhanced terrain model and perform validation analyses to quantify improvements and ensure consistency with the global baseline. This workshop is intended for researchers, data scientists, and GIS professionals interested in terrain modeling, geospatial data fusion, and scalable environmental data processing workflows. Yu-Feng Ho 2025-09-19T09:30:00+00:00 09:30 00:20 Aula Magna open-earth-monitor-global-workshop-2025-350-toward-a-global-scale-runoff-estimation-through-satellite-observations-the-stream-model https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/CNVZJU/ false Oral talk en Climate change alters familiar environments and impacts our daily lives. In this circumstances it essential to monitor river discharge for a range of activities, including water resource management and flood risk reduction. However, in-situ stations have some limitations, such as low density, incomplete temporal coverage, and data access delays, which make continuous spatio-temporal monitoring of river discharge a challenging task. For this reason, researchers and space agencies have developed new satellite-based methods for estimating runoff and river discharge. Among these, the European Space Agency (ESA) has funded the STREAM (SaTellite-based Runoff Evaluation And Mapping) and STREAM-NEXT projects, which exploit satellite observations of precipitation, soil moisture, terrestrial water storage, altimetric water level, and snow cover fraction within a conceptually parsimonious model, STREAM, to estimate runoff and river discharge. Applied to more than 40 basins worldwide including the largest basins in the world (e.g., Mississippi-Missouri, Amazon, Danube, Murray-Darling, and Niger), the STREAM model has shown good ability to replicate observed river discharge, even in basins with a high degree of human pressure where flow is regulated by dams, reservoirs, or floodplains, or in heavily irrigated areas. The positive results achieved have paved the way for regionalizing the parameters of the STREAM model to make it applicable on a global scale. Through the calibration of the STREAM model on the 40 pilot catchments, it was possible to obtain a large set of parameters that were linked, through specific relationships, to various features including climate, soil characteristics, vegetation and topographic attributes. This approach yielded regionalized STREAM parameters. This study aims to evaluate the efficacy of the STREAM runoff and river discharge estimates, derived from regionalized parameters, across a diverse range of basins. To this end, a comparative analysis will be conducted between observed and simulated river discharge, as well as between simulated and modeled land surface runoff estimates. This contribution aims to demonstrate how the use of readily available information processed through a conceptual regionalized hydrological model can bring benefits in estimating river discharge and producing runoff maps, even in basins characterised by intricate interactions between natural and anthropogenic phenomena. Francesco Leopardi 2025-09-19T09:50:00+00:00 09:50 00:20 Aula Magna open-earth-monitor-global-workshop-2025-330-from-soil-grids-and-spectral-analysis-to-soil-mineral-composition-estimates https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/8Q8CVW/ false Oral talk en In response to the European Union Deforestation Regulation (EUDR), EU member states must verify that imported forest risk commodities (FRCs) such as coffee, cacao, soy, and timber are not sourced from deforested land. At Meise Botanic Garden, we have established an ICP-OES laboratory to determine the mineral composition of these commodities. In collaboration with Ghent University, we further enhance this analysis using ICP-MS and isotope ratio techniques. We connect our results to evaluation platforms and databases as those provided by World Forest ID. To further contextualize our findings and assess the plausibility of declared origins, we complement the lab work with dry lab estimations, drawing on global soil grids and satellite-derived spectral data to approximate local soil mineral compositions. We also present preliminary insights into how post-harvest processing, particularly decaffeination of coffee, alters the mineral signature of the final product and complicates provenance verification. This hybrid approach provides a valuable indication of origin in cases where our reference database is still under development. Ultimately, the integration of laboratory analysis with geospatial estimation offers a pragmatic tool for EUDR enforcement and opens new pathways for innovation in EU Green Deal-aligned services. Christophe Van Neste 2025-09-19T11:00:00+00:00 11:00 00:45 Aula Magna open-earth-monitor-global-workshop-2025-328-accessing-big-satellite-lidar-from-cloud https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2025/talk/VGPKKZ/ false Workshop proposal en Spaceborne Lidar, such as ICESat-2 and GEDI, is global missions for land surface monitor tools across terrain, vegetation and ice monitoring. The huge terabytes volume of data and non-cloud-optimized format thwarts the usage and access for the dataset. In the workshop, we are presenting an algorithm to reorganize spaceborne lidar and a STAC visualization solution. The functionalities will be demonstrated in Jupyter notebook. covering accessing STAC collections of ICESat-2 and GEDI respectively, spatial and temporal lazy-loading from DuckDB, and data exportation to desired format. The workshop will be in Python to connect various software APIs. Yu-Feng Ho