Geostationary satellite surface-air temperature difference anomaly to detect vegetation stress

Land surface temperature (Ts) can detect plant physiological stress prior to visible greenness decline in droughts via its biophysical linkage to plant transpiration. New-generation geostationary satellites offer unique opportunities to monitor sub-diurnal variations in Ts and track plant physiological processes occurring at sub-daily timescales. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method to track vegetation drought stress using the cumulative sub-diurnal difference, from late-morning to early-afternoon, between Ts from Himawari-8 geostationary satellite and gridded hourly air temperature (Ta). We used SATDA to monitor the spatio-temporal patterns of the 2017- 2019 Tinderbox Drought in southeast Australia. We analysed the skill of SATDA in forecasting visible drought-induced vegetation greenness decline, and benchmarked it against (i) conventional water availability-based indices (precipitation, soil moisture) and (ii) two satellite Ts-only indices (TCI, TRI). SATDA effectively captured a rapidly intensifying “flash drought” event at multi-week timescales (Jul to Sep 2019) embedded within the broad multi-year drought progression. SATDA showed the best vegetation greenness forecast skill in the transitional semi-arid and sub-humid climates, with forecast correlation > 0.5 at 32-day lead time. Advantage of SATDA over water availability indices was more evident in woody-dominated ecosystems than herbaceous-dominated ecosystems, likely due to the importance of physiological regulations by trees during droughts such as deeper roots and stronger stomatal control. SATDA, based on Ts-Ta, showed overall better vegetation greenness forecasts than two Ts-only indices, especially in woody vegetation. The parsimonious process-based SATDA method suits global-scale operational implementation to complement vegetation drought monitoring and early warning systems.