Multi-scale evapotranspiration and evaporative stress as indicators of vegetation health and drought response – an application in central Missouri, USA

Abstract

Satellite estimates of evapotranspiration (ET) based on thermal infrared retrievals of land-surface temperature enable monitoring of vegetation water use and health/stress at sub-field to global scales. The information content of these products varies with pixel size, reflecting dominant drivers of land-surface fluxes at different scales as well as tradeoffs in satellite spatial vs. temporal coverage. This study evaluates the performance and utility of multi-scale ET and Evaporative Stress Index (ESI) products at 4-km (GOES), 500-m (VIIRS-M band), and 30-m (Landsat+ECOSTRESS+VIIRS-I5 band, spatially sharpened) resolutions across a heterogeneous agricultural-ecological landscape in central Missouri, USA. At 30-m, the modeling framework combines multiple Landsat-like thermal and optical datasets to improve temporal sampling at that critical agricultural management scale. Daily ET and weekly ESI are compared with eddy covariance fluxes, PhenoCam observations, drought classifications, and crop condition data collected over the Central Missouri River Basin from 2017 to 2023, a period including significant drought events. A rotation-correction methodology applied to the 30-m ESI reduces crop-rotation artifacts and improves timing and magnitude of stress detection relative to standard pixel-based approaches. Together, the multi-scale products reveal complementary drought-response signals: coarse ESI captures regional drought signals with good temporal response, whereas 30-m ESI resolves local drivers of stress, management actions, post-disturbance recovery, and landcover-specific drought sensitivity. A strong correlation is identified between 30-m ESI and corn and soybean crop condition at harvest, suggesting potential value for yield prediction. These findings support operational implementation of 30-m ESI within a cloud platform such as OpenET and highlight the growing value of multi-sensor thermal constellations for field-scale drought monitoring and agricultural decision-support.