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<p>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. TVDI was obtained from Landsat 8/9 imagery and validated by UAV-based data, demonstrating good agreement. Mutual information (MI) analysis revealed scenario-dependent lag times of 0-6 months between SPI and TVDI, facilitating lag-based prediction. A hybrid Wavelet-ANFIS/FCM model predicted TVDI using SPIs as inputs with high accuracy (RMSE &lt; 0.08 in training and test phases). These 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Mohammad Zare,
  • Marius Hobart,
  • Michael Schirrmann

摘要

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. TVDI was obtained from Landsat 8/9 imagery and validated by UAV-based data, demonstrating good agreement. Mutual information (MI) analysis revealed scenario-dependent lag times of 0-6 months between SPI and TVDI, facilitating lag-based prediction. A hybrid Wavelet-ANFIS/FCM model predicted TVDI using SPIs as inputs with high accuracy (RMSE < 0.08 in training and test phases). These 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.