Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework

Mengli Zhang, Xianglong Fan, Pan Gao (), Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang and Fei Tan ()
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Mengli Zhang: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Xianglong Fan: Agricultural College, Shihezi University, Shihezi 832003, China
Pan Gao: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Li Guo: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Xuanrong Huang: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Xiuwen Gao: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Jinpeng Pang: College of Information Science and Technology, Shihezi University, Shihezi 832061, China
Fei Tan: College of Information Science and Technology, Shihezi University, Shihezi 832061, China

Land, 2025, vol. 14, issue 1, 1-27

Abstract: Soil salinization affects agricultural productivity and ecosystem health in Xinjiang, especially in arid areas. The region’s complex topography and limited agricultural data emphasize the pressing need for effective, large-scale monitoring technologies. Therefore, 1044 soil samples were collected from arid farmland in northern Xinjiang, and the potential effectiveness of soil salinity monitoring was explored by combining environmental variables with Landsat 8 and Sentinel-2. The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. The results suggest that fertilizer use plays a critical role in soil salinization processes. Notably, the interpretable model KAN achieved an accuracy of 0.75 in correctly classifying the degree of soil salinity. This study highlights the potential of integrating multi-source remote sensing data with deep learning technologies, offering a pathway to large-scale soil salinity monitoring, and thereby providing valuable support for soil management.

Keywords: neural network; multi-source satellite data; interpretable deep learning; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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