Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

Abstract

Drought is a complex natural phenomenon, and its impacts on agriculture are enormous. Drought has been a prevalent concern for farmers in the Lower Mekong Basin (LMB) over the last decades; thus, monitoring drought is important for water planning and management to mitigate impacts on agriculture in the region. This study explored the applicability of monthly MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data for agricultural drought monitoring in LMB in the dry season from November 2001 to April 2010. The data were processed using the temperature vegetation dryness index (TVDI), calculated by parameterizing the relationship between the MODIS NDVI and LST data. The daily volumetric surface soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM) were collected and used for verification of the results. In addition, we compared the efficiency of TVDI with a commonly used drought index, the crop water stress index (CWSI), derived from the MODIS LST alone. The results achieved from comparisons between TVDI and AMSR-E soil moisture data indicated acceptable correlations between the two datasets in most cases. There was close agreement between TVDI and TRMM precipitation data through the season, indicating that TVDI was sensitive to precipitation. The TVDI compared to CWSI also yielded close correlations between both datasets. The TVDI was, however, more sensitive to soil moisture stress than CWSI. The results archived by analysis of TVDI indicated that the moderate and severe droughts were spatially scattered over the region from November to March, but more extensive in northeast Thailand and Cambodia. The larger area of severe drought was especially observed for the 2003–2006 dry seasons compared to other years. The results achieved from this study could be important for drought warnings and irrigation scheduling.