Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district

This study focuses on the Shenwu Irrigation Area, situated in the upstream section of the Hetao Irrigation District, within the context of water conservation retrofitting. We establish soil sampling sites in this region, employ Google Earth Engine algorithms to develop models for soil salinity inver...

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Main Authors: Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Smart Agricultural Technology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772375525003752
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author Yi Zhao
Shuya Yang
Haibin Shi
Haoqi Han
Yunlei Dong
Xianyue Li
Jianwen Yan
Yan Yan
Xu Dou
Feng Tian
Qingfeng Miao
author_facet Yi Zhao
Shuya Yang
Haibin Shi
Haoqi Han
Yunlei Dong
Xianyue Li
Jianwen Yan
Yan Yan
Xu Dou
Feng Tian
Qingfeng Miao
author_sort Yi Zhao
collection DOAJ
description This study focuses on the Shenwu Irrigation Area, situated in the upstream section of the Hetao Irrigation District, within the context of water conservation retrofitting. We establish soil sampling sites in this region, employ Google Earth Engine algorithms to develop models for soil salinity inversion and land use classification, and analyze the change patterns of land salinization and land use types. The results showed that the Blue, Green, Red, NIR, SWIR1 and SWIR2 bands and the SI, SI1, SI2, SI3 and SI4 spectral indices were the ones that had correlation coefficients greater than 0.55 between the various parameters and soil salinity in 2015 and 2022. The soil salinity inversion model constructed using Random Forest demonstrates higher R2 values and lower MAE and RMSE compared to the Support Vector Machine and Gradient Boosting Tree, establishing it as the optimal model for soil salinity inversion. After the water conservation retrofit, non-saline soil increased by 326.5 km2, indicating improved soil conditions. The overall accuracy of the Gradient Boosting Tree classification model is 95.00 % and the Kappa coefficient is 0.93, which is the optimal model for land use type classification. After water conservation retrofits, the water body and wasteland area decreases and the building land and cropland area increases. This research offers a theoretical basis for alleviating soil salinization in irrigated area and improving the ecological circumstances of these locales.
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spelling doaj-art-167ce0776d6540ddbd6240c7a6ee7a252025-08-20T03:13:11ZengElsevierSmart Agricultural Technology2772-37552025-12-011210114310.1016/j.atech.2025.101143Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation districtYi Zhao0Shuya Yang1Haibin Shi2Haoqi Han3Yunlei Dong4Xianyue Li5Jianwen Yan6Yan Yan7Xu Dou8Feng Tian9Qingfeng Miao10College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China; Regional Collaborative Innovation Center for Comprehensive Management of Water Resources and Water Environment in the Inner Mongolia Section of the Yellow River Basin, Hohhot 010018, China; Corresponding authors.Inner Mongolia Autonomous Region Farmland Construction Service Center, Hohhot, 010011, ChinaInner Mongolia Autonomous Region Farmland Construction Service Center, Hohhot, 010011, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China; State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaCollege of Agriculture, Inner Mongolia Agricultural University, Hohhot 010019, ChinaInstitute of Resources, Environment and Detection Technology, Inner Mongolia Academy of Agriculture and Animal Husbandry Sciences, Hohhot 010031, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China; Corresponding authors.This study focuses on the Shenwu Irrigation Area, situated in the upstream section of the Hetao Irrigation District, within the context of water conservation retrofitting. We establish soil sampling sites in this region, employ Google Earth Engine algorithms to develop models for soil salinity inversion and land use classification, and analyze the change patterns of land salinization and land use types. The results showed that the Blue, Green, Red, NIR, SWIR1 and SWIR2 bands and the SI, SI1, SI2, SI3 and SI4 spectral indices were the ones that had correlation coefficients greater than 0.55 between the various parameters and soil salinity in 2015 and 2022. The soil salinity inversion model constructed using Random Forest demonstrates higher R2 values and lower MAE and RMSE compared to the Support Vector Machine and Gradient Boosting Tree, establishing it as the optimal model for soil salinity inversion. After the water conservation retrofit, non-saline soil increased by 326.5 km2, indicating improved soil conditions. The overall accuracy of the Gradient Boosting Tree classification model is 95.00 % and the Kappa coefficient is 0.93, which is the optimal model for land use type classification. After water conservation retrofits, the water body and wasteland area decreases and the building land and cropland area increases. This research offers a theoretical basis for alleviating soil salinization in irrigated area and improving the ecological circumstances of these locales.http://www.sciencedirect.com/science/article/pii/S2772375525003752Google earth engineMachine learningSoil salinity inversionLand use type extraction
spellingShingle Yi Zhao
Shuya Yang
Haibin Shi
Haoqi Han
Yunlei Dong
Xianyue Li
Jianwen Yan
Yan Yan
Xu Dou
Feng Tian
Qingfeng Miao
Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
Smart Agricultural Technology
Google earth engine
Machine learning
Soil salinity inversion
Land use type extraction
title Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
title_full Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
title_fullStr Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
title_full_unstemmed Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
title_short Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district
title_sort analysis of soil salinization and land use change under water conservation retrofit in the hetao irrigation district
topic Google earth engine
Machine learning
Soil salinity inversion
Land use type extraction
url http://www.sciencedirect.com/science/article/pii/S2772375525003752
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