The study focuses on enhancing the usability of previously published satellite-based Climate Data Record datasets. We developed a method to quantitatively evaluate the consistency between datasets retrieved from the same instruments using different algorithms. The study includes an exemplary application of the method to Aerosol Optical Depth datasets. The goal is to provide clear guidance for the user on how to optimally exploit the complementarity datasets retrieved from instruments.

We generated the annual dominant leaf type and biomass maps over Bavaria, Germany, from 2015 to 2024. We assessed leaf type and biomass changes across administrative regions and hotspots. The results provide good insights into the tree status in Bavaria and suggest a new focus for forest management policies.

This study combines earth observation indicators and farmer survey data collected in four rice-growing states in Nigeria to assess drought impacts on rainfed rice yields. We use regression models to connect meteorological indices and earth observation indicator anomalies to identify drought moments over the years 2020-2024, demonstrating that indicator anomalies are correlated to rice yield changes, particularly when anomaly values are aggregated temporally over separate rice growth stages.

Ground movement can be measured from space by comparing satellite images taken at different times using a technique known as InSAR. The InSAR Svalbard service provides baseline information showing where and how the ground is moving, supporting geohazard management, land-use planning, and climate change adaptation, and helping assess impacts on infrastructure and human activities. While such services exist elsewhere, this is the first of its kind in the Arctic.

As glaciers retreat, lakes form and grow quickly that poses flood risks in downstream populations. Using satellite imagery, we test ways in which we can improve the accuracy of tracking the changes in the areal extent of glacial lakes. By combining training and testing parameters with terrain and vegetation information we achieved overall accuracies of 94.6 % showing that our method produces a rapid way of delineating glacial lakes to support hazard planning and mitigation.

Our study shows that satellite measurements can track changes in Greenland’s ice mass every few days, revealing short‑term melt events that monthly products cannot capture. We combine the range acceleration of a gravity mission with surface-height data to improve the spatial distribution of these changes. This approach helps us better understand rapid ice‑loss patterns and should be considered for future satellite missions.

Mangrove forests are vital for climate protection and coastal communities, but their extent is often uncertain. This study uses a satellite-based artificial intelligence approach (AlphaEarth Foundations) to map mangroves across Southeast Asia and Papua New Guinea from 2017 to 2024. Achieving over 80 % accuracy, the results provide consistent maps and improved area estimates with uncertainty, supporting better monitoring and climate reporting.

San Francisco International Airport is slowly sinking. Using satellites that measure ground movement with millimeter precision, we mapped subsidence across the airport and found that areas built on thick, soft bay sediments sink fastest. This sinking adds directly to rising sea levels, threatening runways with flooding and cracking decades sooner than climate projections alone suggest. Satellite monitoring can help airport managers identify vulnerable areas before costly damage occurs.

Soil moisture (SM) plays a key role in weather, agriculture, and water management. While satellites can measure SM from space, obtaining accurate, frequent measurements throughout the day remains challenging. Here, we explore how deep learning models can improve sub-daily SM estimates. Our approach focuses on capturing spatial patterns and adapting to local conditions. Using data from the ASCAT satellite instrument, we show that this model can produce reliable SM estimates multiple times a day. 

Passive microwave (PMW) satellite data have been widely used to monitor Antarctic sea ice velocity (SIV) on a pan-Antarctic scale. We present a comprehensive evaluation of a PMW-derived Antarctic SIV by leveraging high-resolution SIV fields derived from synthetic aperture radar imagery. We found a widespread ~1 km d^-1 underestimation of ice drift speed across the Southern Ocean, caused by optimal interpolation.