ESA EO Open Science Newsletter

News from EO Open Science

May 2026

Highlights: JAXA turns satellite data into stories with EO Dashboard; Sentinel-1 InSAR Processing with openEO in Copernicus Data Space Ecosystem; ESA Science Hub Challenge 2025 participants test Foundation Models on Single-Pass, Reference-Free Wet Snow Detection; ML-TWix: a new Water Storage Dataset Co-developed at ESA Science Hub; Two New Invitation to Tenders: Adaptative Forest Management ITT & Urban Development, Liveability and Adaptation ITT.

The ESA EO Open Science Team

Highlights

JAXA TURNS SATELLITE DATA INTO STORIES WITH EO DASHBOARD

At GovMesh 4.0 in Singapure, JAXA highlighted the value of the Earth Observing Dashboard, operated jointly by ESA, JAXA and NASA, as a model for turning complex satellite data into accessible, actionable tools for citizens and policymakers. Dr. Naoko Sugita, showcased how the dashboard has grown into a platform used across 130 countries enabling community storytelling, open APIs, and a planned AI layer that lets users ask plain-language questions to turn raw data into practical tools for citizens and policymakers.

SENTINEL-1 InSAR PROCESSING WITH openEO IN CDSE

ESA's Copernicus Data Space Ecosystem now supports cloud-native Sentinel-1 InSAR processing through new openEO workflows, developed under the CLOUDInSAR project. Users can generate coherence and interferogram time series in just a few lines of code available at APEX Algorithm Catalogue, without downloading data or managing local infrastructure, opening new possibilities for glacier mapping, earthquake deformation monitoring, and beyond.

ML-TWiX: a New Water Storage Dataset Co-Developed at ESA Science Hub

A new global dataset developed in part by researchers at ESA's ESRIN Science Hub, alongside the University of Stuttgart, JPL and ETH Zurich, extends satellite-based terrestrial water storage records into the pre-GRACE era. Published in Nature Scientific Data, ML-TWiX reconstructs monthly total water storage anomalies from 1980 to 2012 by training an ensemble of three machine learning models on GRACE observations, then applying them to outputs from 13 global hydrological models. Validated against satellite laser ranging, water balance estimates and global sea level budgets, the dataset outperforms existing reconstructions across most metrics and is openly available, opening new possibilities for long-term drought, flood and climate research.

Science Hub Challenge 2025 Participants Test Foundation Models on Single-Pass, Reference-Free Wet Snow Detection

A newly published community-led story by students from the University of Edinburgh, participating in the 2025 ESA Science Hub Challenge, presents results on reference-free wet snow mapping using geospatial foundation models. The story is now featured on the ESA-NASA-JAXA EO Dashboard. Using Sentinel-1 dual-polarisation SAR data over Svalbard, the students fine-tuned a pre-trained geospatial foundation model to classify wet snow, dry snow, and bare ground without relying on a reference image, a key limitation of traditional approaches. While results remain preliminary due to the two-day challenge timeframe and small dataset, the experiment demonstrated promising potential and identified a clear roadmap for improving reference-free wet snow detection using AI and Earth Observation.

INVITATION TO TENDER (ITT): ADAPTIVE FOREST MANAGEMENT

ESA has launched an Invitation to Tender for its Adaptive Forest Management project, covering two contracts. AFM Resilience will pioneer EO methods to map how Europe's forests withstand and adapt to climate change and disturbances, while AFM Regrowth will monitor post-disturbance vegetation recovery and its role as a carbon sink, supporting both EU and international policy frameworks.

INVITATION TO TENDER (ITT): URBAN DEVELOPMENT, LIVEABILITY AND ADAPTATION

ESA has launched an Invitation to Tender for three EO-based urban R&D projects developed with end-users. The first will map and monitor informal settlements globally, the second will assess urban liveability by integrating land use, climate and socio-economic data to support city planning, and the third will analyse mobility patterns in relation to urban structure to advance sustainable transport planning.

Highlights

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