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Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China

Author

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  • Shiqin Li

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Ilyas Nurmemet

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Jumeniyaz Seydehmet

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830017, China
    Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Xinjiang Normal University, Urumqi 830017, China)

  • Xiaobo Lv

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Yilizhati Aili

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Xinru Yu

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

Abstract

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions.

Suggested Citation

  • Shiqin Li & Ilyas Nurmemet & Jumeniyaz Seydehmet & Xiaobo Lv & Yilizhati Aili & Xinru Yu, 2024. "Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China," Land, MDPI, vol. 13(11), pages 1-23, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1941-:d:1523218
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    References listed on IDEAS

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    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    2. Jing Zhao & Ilyas Nurmemet & Nuerbiye Muhetaer & Sentian Xiao & Adilai Abulaiti, 2023. "Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    3. Lin Bai & Jia Zhou & Jinming Luo & Hongshuang Dou & Ye Zhang, 2023. "Analyzing Driving Factors of Soil Alkalinization Based on Geodetector—A Case in Northeast China," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
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