Agricultural drought is a complex natural hazard involving multiple variables and has garnered increasing attention for its severe threat to food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, previous studies on agricultural drought often focused on precipitation or evapotranspiration, overlooking other potential drivers related to crop drought stress. Additionally, macro-level analyses of drought-driving mechanisms struggle to reveal the underlying contexts of varying drought intensities. Northern Italy is one of the most important agricultural regions in Europe and is also a hotspot affected by extreme climate events in the world. In the summer of 2022, an extreme drought struck Europe once again, causing significant damage to the agricultural regions of Northern Italy. However, no studies to date have revealed the potential impacts and extent of extreme drought on this crucial agricultural area at a regional scale. Therefore, a comprehensive understanding of agricultural drought still requires further clarification and differentiated driver analysis. This study proposed a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index (IADI) with remote sensing-related data including meteorology, soil, geomorphology, and vegetation conditions. Additionally, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicated that the proposed explainable ensemble machine learning model with multi-source remote sensing products could effectively depict the evolution of agricultural drought with spatially continuous maps on an 8day scales. The SHAP analysis demonstrated that the extreme and severe agricultural drought in the summer of 2022 was closely related to meteorological indicators especially precipitation and land surface temperature, which contributed 68.88% to the drought. Moreover, the new findings also highlighted that soil properties affected the agricultural drought with a contribution of 28.3%. Specifically, in the case of moderate and slight drought conditions, higher clay and soil organic carbon (SOC) content contribute to mitigating drought effects, while sandy and silty soils have the opposite effect, and the contributions from soil texture and SOC are more significant than precipitation and land surface temperature. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation.

Landslides pose significant threats to mountainous regions, causing widespread damage to both property and human lives. This study seeks to enhance landslide prediction in the Aqabat Al-Sulbat Asir region of Saudi Arabia by integrating deep neural networks (DNNs), 1D convolutional neural networks (CNNs), and a combined DNN and CNN ensemble (DCN) with explainable artificial intelligence (XAI) techniques. These XAI techniques enhance the interpretability of these complex deep learning models, thereby facilitating better decision-making strategies. Furthermore, the DNN model is employed to incorporate game theory principles, assessing the individual impact of variables on landslide prediction. Our findings indicate high and very high landslide susceptibility zones covering 35.1-41.32 km2 and 15.14-16.2 km2, respectively. The DCN model boasts the highest area under the curve (AUC) at 0.97, followed by CNN (0.94) and DNN (0.9), showcasing DCN's superiority. XAI analysis exposes significant residuals in CNN's posterior despite its high AUC. Notably, precipitation, slope, soil texture, and line density emerge as pivotal parameters for accurate landslide prediction. Game theory results highlight line density's preeminence, trailed by topographic wetness index, curvature, and slope in landslide occurrence. By incorporating deep learning models, XAI, and game theory, this study presents a holistic approach to landslide management. This comprehensive framework equips authorities and stakeholders with valuable tools for informed decision-making in landslide-prone areas, delivering accurate predictions and insights into crucial parameters.