A New Index to Better Detect and Monitor Agricultural Drought in Niger Using Multisensor Remote Sensing Data

Abstract

In this study, we propose a new remote sensing–based drought index, the agricultural drought condition index (ADCI), for agricultural drought monitoring in the agricultural area of Niger. It is defined as a first principal component analysis (PCA) of precipitation condition index (PCI), vegetation condition index (VCI), temperature condition index (TCI), and evapotranspiration condition index (ETCI). ADCI integrates multisource remote sensing data from Climate Hazards group Infrared Precipitation with Station (CHIRPS) and moderate resolution imaging spectro-radiometer (MODIS) and it synthesizes precipitation deficits, vegetation growth status, soil thermal stress, and crop water stress in the drought process. A series of validation tests have been implemented using a one-month standardized precipitation index (SPI-1), the crop yield and the vegetation health index (VHI) during the crop growth period (June–October) from 2003 to 2017. The results show that ADCI is not only strongly correlated with SPI-1, but also with the variation of crop yield and the VHI. When tested against VHI, the ADCI performed better than VHI. Thus, it was proven that this index is a full drought monitoring indicator and it can not only contain the meteorological drought information, but also reflect drought influence on agriculture.