A Novel Standardized Optimal Synthesized Drought Index (SOSDI) for Agro-Drought Monitoring Using Multisource Remote Sensing Data

Drought has been putting enormous pressure on agriculture and food security, especially for African countries with inadequate monitoring. However, there is still a lack of standardization in the synthesized drought index, which could better monitor drought than normalized indices, as well as insufficient attention to the geographical backgrounds of weights. We aim to develop a standardized optimal synthesized drought index (SOSDI) that will improve drought monitoring and reflect dominant factors. The standardization transforms the input variable distribution functions, with the constraint optimization to compute the weight. Weight matrices are suggested to examine the geospatial heterogeneity of the input variable. The assessment of SOSDI from several dimensions was implemented. SOSDI was applied to monitor the periodic drought in East Africa, where rain-fed agriculture was susceptible to seasonal alternation. The results confirmed that standardization was more effective than normalization in the synthesized drought index, and the average correlation between SOSDI with the existing indices, soil moisture, and crop yield reached about 0.6, 0.7, and 0.7, respectively. The cyclical drought is well captured on the seasonal and annual scales, mostly during the long-dry season. SOSDI could capture drought evolution and had good resistance to disturbances. Overall, SOSDI demonstrated a strong capability in drought monitoring and enhanced the drought expression derived from vegetation and temperature more effectively, rather than just precipitation.