EGUsphere

Copernicus Publications

Göttingen, Germany

10.5194/egusphere-2026-5000

Detecting soil moisture drought impacts on ecosystem physiology from earth observation data

El-Mejjaouy

Yousra

¹ ² Hufkens

Koen

³ Stocker

Benjamin D.

https://orcid.org/0000-0003-2697-9096

¹ ²

Oeschger Centre for Climate Change Research, Universityof Bern, Bern, Switzerland

Institute of Geography, University of Bern, Bern, Switzerland

BlueGreen Labs (bv), 9120 Melsele, Belgium

01 09 2026

2026 1 27

2026

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5000/

The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5000/egusphere-2026-5000.pdf

Satellite remote sensing is widely used to monitor vegetation drought impacts, yet water stress triggers both structural changes and physiological adjustments, whose relative importance varies across space and time. Structural responses, captured by multispectral vegetation indices are well documented, while the extent to which readily available satellite data can detect physiological drought effects remains insufficiently quantified. Here, we assess how much information on drought-induced physiological responses is contained in multispectral reflectance, land surface temperature (LST), and climate reanalysis data. We combine MODIS reflectance and thermal data with an ERA5-Land potential cumulative water deficits metric (PCWD), and train a machine-learning model using eddy covariance data. The target variable represents drought-induced reductions in light use efficiency (fLUE), explicitly separating physiological regulation from structural canopy changes. Our resulting data-driven model captures variations in drought-related physiological response more accurately than previously documented indices commonly applied for drought monitoring (R² = 0.64, RMSE = 0.112, spatial cross-validation). Application across central Europe during two recent summers demonstrates that the model detects drought impacts on photosynthesis earlier and more sensitively than NDVI, particularly in evergreen ecosystems where structural signals are muted. Our results show that a systematically trained, data-driven integration of multispectral reflectance, thermal signals, and climate data can extract a substantial portion of the physiological drought response from Earth observation data. This approach enables a more confident and spatially consistent assessment of drought impacts on photosynthesis using the full combined information content of satellite and climate datasets, beyond what structural vegetation indices alone can provide.