Time Varying Spatial Downscaling of Satellite-Based Drought Index

Drought monitoring is essential to detect the presence of drought, and comprehensive change drought conditions on a regional or global scale. This study used satellite precipitation data from Tropical Rainfall Measuring Mission (TRMM), but refined for in Java, Indonesia. Firstly, analysis was conducted establish standardized index (SPI) TRMM different durations. Time varying SPI spatial downscaling by selecting environmental variables, normalized difference vegetation (NDVI), land surface temperature (LST) that were highly correlated with because meteorological associated drought. time-dependent regression build relation among original SPI, auxiliary i.e., NDVI LST. Results indicated better than nonspatial (overall RMSEs: 0.25 0.46 downscaling). Spatial more suitable heterogeneous particularly transition time (R: 0.863 0.137 June 2019 models). The fine resolution (1 km) can be composed data. fine-resolution captured similar trend SPI. Furthermore, detailed maps understand spatio-temporal pattern severity.