UAV Multi-Sensor Derived Inputs for TSEB-PT Evapotranspiration Estimation in Sugar Beet, Potato, and Winter Wheat

This dataset contains curated, analysis-ready UAV-derived raster products used as spatial inputs to the TSEB-PT evapotranspiration model for three crop growing seasons at the ICOS Selhausen agricultural research site in Germany: sugar beet (2021), potato (2022), and winter wheat (2023). The dataset includes land surface temperature (LST, K) derived from thermal infrared imagery; green area index (GAI) and fractional vegetation cover (fc) derived from multispectral imagery; plant area index (PAI) and crop height (hc, m) derived from UAV LiDAR; and fraction of LAI that is green (fg), calculated as GAI/PAI. Raster products are provided for the available UAV campaign dates and represent the processed spatial datasets used for TSEB-PT implementation and analysis in the associated study. This dataset accompanies the publication “Tracking Crop Evapotranspiration and Water Stress Across Seasons Using Multi-sensor UAV Observations.” Eddy covariance, meteorological, and ground-reference measurements used for model evaluation and calibration are available separately through ICOS and are cited in the associated publication. Citation: When using this dataset, please cite this Zenodo dataset. Please also cite the associated publication when referring to the methodology, processing, or analysis for which these data were generated:Bates, J. S., Montzka, C., Vereecken, H., Jonard, F.: Tracking Crop Evapotranspiration and Water Stress Across Seasons Using Multi-sensor UAV Observations, Biogeosciences, 2026. AcknowledgmentsThe study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2070—390732324 and the Helmholtz Association Modular Observation Solutions for Earth Systems (MOSES) Initiative.