Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

Abstract

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.