Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data

Abstract

The existing remote sensing drought indices were mainly derived from optical and infrared bands, and have been widely used in monitoring agricultural drought; however, their application in monitoring meteorological drought was limited. This study proposes a new multi-sensor microwave remote sensing drought index, the Microwave Integrated Drought Index (MIDI), for monitoring short-term drought, especially the meteorological drought over semi-arid regions, by integrating three variables: Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Advanced Microwave Scanning Radiometer for EOS (AMSR-E) derived soil moisture, and AMSR-E derived land surface temperature. Each variable was linearly scaled from 0 to 1 for each pixel based on absolute minimum and maximum values over time to relatively monitor drought. Pearson correlation analyses were performed between remote sensing drought indices and scale-dependent Standardized Precipitation Index (SPI) during the growing season (April to October) from 2003 to 2010 to assess the capability of remotely sensed drought indices over three bioclimate regions in northern China. The results showed that MIDI with proper weights of three components outperformed individual remote sensing drought indices and other combined microwave drought indices in monitoring drought. It nearly possessed the best correlations with different time scale SPI; meanwhile it showed the highest correlation with 1-month SPI, and then decreased as SPI time scale increased, suggesting that the MIDI was a very reliable index in monitoring meteorological drought. Furthermore, similar spatial patterns and temporal changes were found between MIDI and 1- or 3-month SPI in monitoring drought. Therefore, the MIDI was recommended to be the optimum drought index, in monitoring short-term drought, especially for meteorological drought over cropland and grassland across northern China or similar regions globally with the ability to work in all weather conditions.