Traditional satellite drought monitoring becomes computationally prohibitive when scaling to larger areas and high-resolution sensors. We developed a statistical method that approximates temperature-vegetation relationships using daily summaries rather than processing millions of individual measurements. Testing in New Zealand across multiple years proved the approach maintains strong performance while significantly reducing compute, processing time and data storage requirements.

Trees outside forests; in savannas, farmlands, and drylands are largely invisible in global carbon maps. We developed a model that learns to spot trees in satellite photos and estimate how much carbon they store, trained only on rough existing maps without any manual labeling. Our maps turned out far more accurate than the maps it learned from, showing that smarter analysis of satellite imagery can reveal the hidden carbon stored in the world's overlooked trees.

InSAR can map coastal sinking at fine scales critical for flood-risk. A recent paper claimed this technology is unreliable for slow subsidence, potentially undermining its policy use. We show that this conclusion stems from methodological flaws: original data were coarsened before analysis, validation used an unrepresentative GNSS network, and uncertainty threshold is self-contradictory. We confirm InSAR performs robustly and technology remains a vital tool for protecting coastal communities.

Satellites that observe Earth must agree with each other so their data can be combined into long, reliable records. We compared two major missions, Landsat-9 and Sentinel-2, using a third satellite, EMIT, as a bridge. By matching images of the same locations worldwide, we found the two missions agree to within a few percent across most spectral bands. This approach avoids problems that affect direct comparisons and can be applied to future, more accurate calibration satellites.

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.