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

Abstract. 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.