Predictive Modeling of soil salinity integrating remote sensing and soil variables: An ensembled deep learning approach

作者：Sana Arshad, Jamil Hasan Kazmi, Endre Harsányi, Farheen Nazli, Waseem Hassan, Saima Shaikh, Main Al-Dalahme, Safwan Mohammed · 发表于：Energy Nexus · 年份：2025 · DOI：10.1016/j.nexus.2025.100374 · 被引用次数：22 · 研究领域：Soil Geostatistics and Mapping、Geochemistry and Geologic Mapping、Remote Sensing in Agriculture

• Electrical Conductivity (EC) of the soil samples ranged between 0.57dS/m and 11.5 dS/m. • Soil EC was highly positively correlated with the pH, elevation, and salinity indices. • The ensemble of the improved FFNN and LSTM architectures with regularization outperformed (R 2 = 0.84, RMSE = 1.38, MAE = 1.01) in accurate salinity predictions. • SHAP revealed that elevation, pH, NDVI, SI-I, and CRSI had a high impact on salinity predictions. Accurate predictions of soil salinity can significantly contribute to achieving the UN- Sustainable Development Goal (SDG-2) of ensuring ‘zero hunger.’ From this perspective, the current research aimed to predict soil electrical conductivity (EC) from remote sensing and soil data using advanced deep learning (DL) architectures. A total of 109 soil samples were analyzed for agricultural land use in the Middle Indus Basin of Pakistan. Seven salinity indices (SI-1 to SI-7) were derived from the 10m to 20m wavelength bands of Sentinel-2, along with vegetation and topographic covariates. Initially, Recursive Feature Elimination was implemented as a feature-selection method to select the most effective predictors. Subsequently, deep learning architectures, including a Feedforward Neural Network (FFNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), were employed to predict soil salinity. Research findings showed that EC ranged between 0.57dS/m to 11.5 dS/m in the study area. The evaluation metrics of the DL models revealed that...