Integrated remote sensing approach to global agricultural drought monitoring

Abstract

[EN]This study explores the use of the Soil Moisture Agricultural Drought Index (SMADI) as a global estimator of agricultural drought. Previous research presented SMADI as a novel index based on the joint use of remotely sensed datasets of land surface temperature (LST) and normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) together with the surface soil moisture (SSM) from the Soil Moisture and Ocean Salinity (SMOS) mission. This study presents the results of applying SMADI at the global scale with a spatial resolution of 0.05° every 15 days. The period of the study spanned from 2010 to 2015. Three spatial scales (local, regional and global) were used to compare the agricultural drought events captured by SMADI against existing agricultural drought indices, as well as reported occurrences of drought events from dedicated databases. Results show that SMADI had good consistency with two agricultural indices in the center of the Iberian Peninsula at the local and regional scales, depicting 2012 and 2014 as the driest years in the area. A comparison of SMADI across the United States of America with the impact and intensity maps of drought from the US Drought Monitor (USDM) revealed a reasonable match with the temporal and spatial extent of the affected areas, detecting the most intense drought events. Finally, a comparison at the global scale with documented events of drought world-wide showed that SMADI was able to recognize more than 80% of these events for more than 50% of their duration. The calculation of the SMADI is simple and fast, and it relies on data that are readily available, thereby providing a rapid overview of drought-prone conditions that could enhance the present capabilities of early warning systems.