Tahmouresi Mohammad Sadegh, Niksokhan Mohammad Hossein, Ehsani Amir Houshang
Faculty of Environment, University of Tehran, Tehran, Iran.
Sci Rep. 2024 Oct 26;14(1):25454. doi: 10.1038/s41598-024-77050-0.
Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m/m and a coefficient of determination (R) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble's predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.
土壤湿度（SM）是影响各种环境过程的关键变量，但传统的微波传感器往往缺乏局部尺度研究所需的空间分辨率。本研究开发了一种新颖的堆叠集成学习框架，以将卫星衍生的SM数据的空间分辨率提高到伊朗乌尔米耶湖盆地的1公里，该地区面临严重的水资源短缺。我们整合了原位SM测量数据（使用时域反射仪[TDR]获得）、土壤湿度主动被动（SMAP）和先进微波扫描辐射计2（AMSR2）的SM产品、中分辨率成像光谱仪（MODIS）的陆地表面温度和植被指数、降水记录以及地形数据。使用复杂比例评估（COPRAS）方法对10个基础机器学习模型进行了评估，并选择表现最佳的模型作为堆叠集成的基础学习器。与单个模型相比，包含随机森林、梯度提升和XGBoost的集成模型显著提高了SM估计的准确性和分辨率。XGBoost和梯度提升元模型实现了最高的准确率，在测试期间无偏均方根误差（ubRMSE）为1.23% m/m，决定系数（R）为0.97，证明了我们方法卓越的预测能力。SHapley加性解释（SHAP）分析揭示了每个基础模型对集成预测的影响，突出了组合不同模型的协同效益。本研究通过展示集成学习在提高卫星衍生SM数据的空间分辨率和准确性方面的潜力，为土壤湿度监测建立了新的基准，为环境科学和农业规划提供了关键见解，特别是在水资源紧张的地区。
