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Inversion of County-Level Farmland Soil Moisture Based on SHAP and Stacking Models

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  • Hui Zhan

    (College of Science, Shihezi University, Shihezi 832003, China)

  • Peng Guo

    (College of Science, Shihezi University, Shihezi 832003, China
    Key Laboratory of Oasis Town and Basin System Ecological Corps, Shihezi 832003, China)

  • Jiaxin Hao

    (College of Science, Shihezi University, Shihezi 832003, China)

  • Jiali Li

    (College of Science, Shihezi University, Shihezi 832003, China)

  • Zixu Wang

    (College of Science, Shihezi University, Shihezi 832003, China)

Abstract

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R 2 of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions.

Suggested Citation

  • Hui Zhan & Peng Guo & Jiaxin Hao & Jiali Li & Zixu Wang, 2025. "Inversion of County-Level Farmland Soil Moisture Based on SHAP and Stacking Models," Agriculture, MDPI, vol. 15(14), pages 1-21, July.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:14:p:1506-:d:1700616
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    References listed on IDEAS

    as
    1. Arias, María & Notarnicola, Claudia & Campo-Bescós, Miguel Ángel & Arregui, Luis Miguel & Álvarez-Mozos, Jesús, 2023. "Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields," Agricultural Water Management, Elsevier, vol. 287(C).
    2. Robert J. Aumann, 1995. "Repeated Games with Incomplete Information," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011476, December.
    3. Zhang, Rui & Bao, Xin & Hong, Ruikai & He, Xu & Yin, Gaofei & Chen, Jie & Ouyang, Xiaoying & Wang, Yongxun & Liu, Guoxiang, 2024. "Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index," Agricultural Water Management, Elsevier, vol. 306(C).
    4. Qihong Da & Jixuan Yan & Guang Li & Zichen Guo & Haolin Li & Wenning Wang & Jie Li & Weiwei Ma & Xuchun Li & Kejing Cheng, 2025. "Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing," Agriculture, MDPI, vol. 15(3), pages 1-19, February.
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