Satellite-based detection of agricultural flash droughts and associated vegetation responses in southeastern South America

Abstract This study evaluates the suitability of the European Space Agency Climate Change Initiative Combined Root-Zone Soil Moisture product (ESA CCI COM RZSM) for detecting agricultural flash droughts (AFDs) across southeastern South America (SESA) and assesses how satellite-based indicators capture their evolution and agricultural impacts. We identify AFDs using two complementary approaches based on RZSM percentiles and the Soil Water Deficit Index (SWDI). We compare AFD detection from ESA CCI COM RZSM against the fifth-generation European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis RZSM over 1979–2022. To assess satellite-based representations of AFD evolution and impacts, we analyze satellite-derived RZSM, evapotranspiration (EVT), and three vegetation indicators—land surface water index, fraction of absorbed photosynthetically active radiation, and gross primary productivity—for two representative events. ESA CCI COM RZSM reproduces the main spatial patterns and seasonal cycles of AFD depicted by ERA5. However, it shows smoother temporal variability, delayed drying, and lower absolute RZSM, which may stem from its climatological rescaling and exponential filtering that propagates surface signals into deeper layers. Detection outcomes are highly sensitive to both methodology and dataset choice. The percentile-based approach tends to over-detect events in persistently wet or dry regimes. The SWDI-based method preserves regional hydroclimatic patterns and provides a more physically constrained representation of plant water stress. Satellite indicators capture the AFD progression, linking rapid RZSM depletion and reduced EVT to declines in vegetation productivity. The intensity and extent of impacts depend on antecedent SM and land cover, confirming the causal propagation of stress through the soil–plant–atmosphere system. Overall, the results demonstrate that ESA CCI COM RZSM, when combined with physically based indices and vegetation metrics, provides a robust, process-oriented foundation for AFD monitoring and early warning in SESA, where in situ observations are scarce.