Using sub-diurnal surface-air temperature difference anomaly derived from Himawari-8 geostationary satellite and meteorological grids for early detection of vegetation drought stress: Application to Australia's 2017–2019 Tinderbox Drought

Satellite land surface temperature ( T s ) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in T s and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite T s and its derived metrics for early detection of vegetation drought stress before visible canopy changes occur has not been widely assessed. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress using the cumulative sub-diurnal difference from late-morning to early-afternoon between T s from the Himawari-8 geostationary satellite and hourly air temperature ( T a ) from meteorological grids. SATDA utilised T s − T a as the physical driving gradient for sensible heat flux ( H ) to capture anomalous sensible heating due to reduced plant transpiration. We used SATDA to monitor the spatio-temporal patterns of the 2017–2019 Tinderbox Drought in southeast Australia. We benchmarked the skill of SATDA in forecasting visible drought-induced vegetation greenness decline against both conventional water availability-based indices (i.e., precipitation and soil moisture anomalies) and existing satellite T s indices (i.e., Temperature Condition Index and Temperature Rise Index) across diverse climates and land covers. SATDA effectively captured a rapidly intensifying flash drought event at multi-week timescales (Jul to Sep 2019) embedded within the multi-year Tinderbox Drought, which contributed to detrimental impacts on agricultural production and increased wildfire risk. 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. The advantage over water availability-based 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 T s − T a , showed overall better vegetation greenness forecasts than two T s -only indices, especially in woody vegetation. Finally, SATDA showed consistently greater advantage over water availability-based and T s -only indices in forecasting visible vegetation decline as the drought intensity increased. The parsimonious process-based SATDA method suits global-scale operational implementation to complement vegetation drought monitoring and early warning systems. • Surface-air temperature difference anomaly (SATDA) tracks plant drought stress. • SATDA uses sub-diurnal Himawari T s minus gridded T a straddling mid-day. • SATDA captured a flash drought in Australia's 2017–2019 Tinderbox Drought. • SATDA better forecasted vegetation decline than water availability for woody plants. • SATDA improved early vegetation stress detection compared to T s -only indices.