Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning

Yue Zhang, Hongqi Wu, Yiliang Kang, Yanmin Fan (), Shuaishuai Wang, Zhuo Liu and Feifan He
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Yue Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Hongqi Wu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yiliang Kang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yanmin Fan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Shuaishuai Wang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Zhuo Liu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Feifan He: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2024, vol. 14, issue 4, 1-24

Abstract: Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for monitoring soil salinity in Bachu County, Kashgar Region, Xinjiang, from 2002 to 2022. The model was established by combining multiple predictors (spectral, salinity, and composite indices and topographic factors) and the accuracy of the four models (Random Forest [RF], Partial Least Squares [PLS], Classification Regression Tree [CART], and Support Vector Machine [SVM]) was compared. The results reveal the high accuracy of the optimized prediction model, and the order of the accuracy is observed as RF > PLS > CART > SVM. The most accurate model, RF, exhibited an R 2 of 0.723, a root mean square error (RMSE) of 2.604 g·kg −1 , and a mean absolute error (MAE) of 1.95 g·kg −1 at a 0–20 cm depth. At a 20–40 cm depth, RF had an R 2 value of 0.64, an RMSE of 3.62 g·kg −1 , and an MAE of 2.728 g·kg −1 . Spatial changes in soil salinity were observed throughout the study period, particularly increased salinization from 2002 to 2012 in the agricultural and mountainous areas within the central and western regions of the country. However, salinization declined from 2012 to 2022, with a decreasing trend in salinity observed in the top 0–20 cm of soil, followed by an increasing trend in salinity at a 20–40 cm depth. The proposed method can effectively extract large-scale soil salinity and provide a practical basis for simplifying the remote sensing monitoring and management of soil salinity. This study also provides constructive suggestions for the protection of agricultural areas and farmlands.

Keywords: Google Earth Engine; soil salinization; vertical soil salinity; machine learning; spatial and temporal variability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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