Assessing agricultural drought risk under CMIP6 scenarios using hybrid AI models and satellite-derived TVDI

This study focuses on predicting the temperature vegetation dryness index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana, using climate projections and satellite imagery. The nonlinear cross-correlation between the standardized precipitation Index (SPI) and TVDI was examined to comprehend the impact of meteorological drought on agricultural drought. Historical precipitation data and CMIP6 projected data (2015–2050) from 35 climate models across four Shared Socioeconomic Pathway (SSP) scenarios were subjected to bias correction and utilized to compute SPI.

The findings demonstrate a progressive increase in the frequency and duration of agricultural drought under higher-emission SSP scenarios, underscoring the escalating future risk and emphasizing the necessity for an agricultural drought early warning system and climate adaptation planning. The proposed framework provides an AI-driven solution for predicting agricultural drought, supporting sustainable development goals (SDGs) related to food security and climate action.