Developing a satellite-based aridity index: case study of Northern Benin

Abstract

To facilitate decision-making processes, the development of an integrated agricultural drought monitoring index that integrates multiple input variables into a single index is crucial. To overcome the limitations of existing agricultural drought assessment methods in Benin, which rely on a single input parameter such as precipitation and are based on sparsely located in-situ measurements, this study aims to develop an integrated agro-climatological drought monitoring approach specifically for Northern Benin (ACDI_B). The main goal of the approach is to increase the accuracy of drought monitoring in Northern Benin. The research assessed input parameters from satellites on a weekly timescale from 2001 to 2021, including Precipitation (PCP), Land Surface Temperature (LST), Soil Moisture (SM), and Normalized Difference Vegetation Index (NDVI). To assess the relative importance of each input parameter, an analysis based on principal component analysis (PCA) was conducted to determine the grid-based percentage contribution. The four input parameters were resampled to 5 km resolution and standardized before performing the PCA analysis. The ACDI_B was evaluated using an independent dataset, including the crop yield for the 27 districts in the study area and ground-based observed Standardized Precipitation Index (SPI) for 6 selected stations, using 21-years of data (from 2001 to 2021). The ACDI_B maps obtained depict mild to extreme drought cases in the historic drought years of 2001, 2006, 2011, 2013, 2014, 2015, and 2021. This study found an increase in drought intensity and drought frequency over the 2001-2021 period. The ACDI_B, using a PCA generated weights, exhibited a strong correlation (r > 0.50) with the yields of cotton, maize, and sorghum in some areas. Although the ACDI_B and 6-SPI correlated well in some stations (r > 0.6), its performance was somehow underrated in some of the stations. ACDI_B effectively captures historic drought patterns and can be used for agricultural drought monitoring and early warning in Northern Benin.