Monitoring of Soil Salinization and Analysis of Driving Factors in the Oasis Zone of South Xinjiang

Jiahao Zhao, Yanmin Fan, Junwei Xuan, Mingjie Shi, Dejun Wang, Hongqi Wu (), Yanan Bi and Yunhao Li
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Jiahao Zhao: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yanmin Fan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Junwei Xuan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Mingjie Shi: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Dejun Wang: Institute of Western Agriculture, CAAS, Changji 831100, China
Hongqi Wu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yanan Bi: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yunhao Li: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China

Land, 2025, vol. 14, issue 4, 1-23

Abstract: Soil salinization significantly jeopardizes agricultural productivity and ecological stability in southern Xinjiang’s oasis regions, highlighting the urgent need to examine its spatial–temporal trends and driving mechanisms for improved resource management. Utilizing soil salinity measurements collected in 2010 and 2023, the current research applied multiple environmental variables processed via the Google Earth Engine (GEE) platform to evaluate the predictive capability of four machine learning algorithms—random forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Tree (CART), and Support Vector Machine (SVM)—for accurate large-scale salinity mapping. Subsequently, a piecewise structural equation model (piecewiseSEM) was employed to quantitatively analyze the driving factors of soil salinization. Correlation analysis revealed seven critical variables—Red, NDSI, kNDVI, SDI, ET, elevation, and SM—as the most influential among the 41 environmental factors assessed for their impact on soil salinity. The performance evaluation ranked the models as follows: RF > GBDT > SVM > CART, with RF achieving the highest predictive accuracy (R 2 = 0.756, RMSE = 2.265 g·kg −1 , MAE = 1.468 g·kg −1 ). Between 2010 and 2023, soil salinization severity in the region exhibited a slight overall decrease; however, the extent of this reduction was relatively modest. The proportion of moderately and severely salinized areas declined, accompanied by reduced spatial variability, whereas the extent of mildly salinized soils increased markedly. These findings imply that soil salinity primarily experiences internal redistribution within the surface layers, with limited downward leaching. Evapotranspiration (ET) and soil moisture (SM) were identified as the dominant drivers affecting salinity dynamics during both periods, with the influence of SM becoming more pronounced over time. This trend highlights that in conditions of limited natural variability, human-induced irrigation practices have emerged as the primary regulator of soil salinity levels. The findings of this study provide novel methodologies and data support for the monitoring and prevention of soil salinization in arid regions.

Keywords: soil salinization; machine learning; time–space variation; driving factor (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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