IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i10p1635-d1494093.html
   My bibliography  Save this article

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Author

Listed:
  • Junhao Liu

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    These authors contributed equally to this work.)

  • Zhe Hao

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China
    These authors contributed equally to this work.)

  • Jianli Ding

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    Xinjiang Institute of Technology, Aksu 843100, China)

  • Yukun Zhang

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China)

  • Zhiguo Miao

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China)

  • Yu Zheng

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China)

  • Alimira Alimu

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China)

  • Huiling Cheng

    (Xinjiang Uygur Autonomous Region Comprehensive Land Management Center, Urumqi 830063, China
    Ministry of Natural Resources Desert-Oasis Ecological Monitoring and Restoration Engineering Technology Innovation Center, Urumqi 830063, China
    Field Scientific Observatory for Soil and Water Processes and Ecological Security in Oasis of Tarim River Headwaters Area, Ministry of Natural Resources of China, Aksu 843000, China)

  • Xiang Li

    (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 moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R 2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R 2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

Suggested Citation

  • Junhao Liu & Zhe Hao & Jianli Ding & Yukun Zhang & Zhiguo Miao & Yu Zheng & Alimira Alimu & Huiling Cheng & Xiang Li, 2024. "Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China," Land, MDPI, vol. 13(10), pages 1-21, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1635-:d:1494093
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/10/1635/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/10/1635/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    2. Baozhong He & Jianli Ding & Wenjiang Huang & Xu Ma, 2023. "Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    3. He, Bohao & Jia, Biying & Zhao, Yanghe & Wang, Xu & Wei, Mao & Dietzel, Ranae, 2022. "Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 267(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng Luo & Shengwei Zhang & Lei Huang & Zhiqiang Liu & Lin Yang & Ruishen Li & Xi Lin, 2022. "Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    2. Deng, Juntao & Pan, Shijia & Zhou, Mingu & Gao, Wen & Yan, Yuncai & Niu, Zijie & Han, Wenting, 2023. "Optimum sampling window size and vegetation index selection for low-altitude multispectral estimation of root soil moisture content for Xuxiang Kiwifruit," Agricultural Water Management, Elsevier, vol. 282(C).
    3. Wenju Zhao & Fangfang Ma & Haiying Yu & Zhaozhao Li, 2023. "Inversion Model of Salt Content in Alfalfa-Covered Soil Based on a Combination of UAV Spectral and Texture Information," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
    4. Wu, Zongjun & Cui, Ningbo & Zhang, Wenjiang & Gong, Daozhi & Liu, Chunwei & Liu, Quanshan & Zheng, Shunsheng & Wang, Zhihui & Zhao, Lu & Yang, Yenan, 2024. "Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data," Agricultural Water Management, Elsevier, vol. 294(C).
    5. Haidi Qi & Dinghai Zhang & Zhishan Zhang & Youyi Zhao & Zhanhong Shi, 2024. "Influence of Soil Moisture in Semi-Fixed Sand Dunes of the Tengger Desert, China, Based on PLS-SEM and SHAP Models," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    6. Wang, Jingjing & Lou, Yu & Wang, Wentao & Liu, Suyi & Zhang, Haohui & Hui, Xin & Wang, Yunling & Yan, Haijun & Maes, Wouter H., 2024. "A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing," Agricultural Water Management, Elsevier, vol. 291(C).
    7. Kang Peng & Fang Zhang & Zhidong Shao, 2024. "Using Different Extraction Methods to Estimate Soil Salinity and Salt Type Changes and Their Effects on Soil Inorganic Carbon in Plowed Desert–Sierozem Soil," Land, MDPI, vol. 13(2), pages 1-16, February.
    8. Romeu Gerardo & Isabel P. de Lima, 2023. "Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal," Agriculture, MDPI, vol. 13(10), pages 1-18, September.
    9. Xing, Liwen & Cui, Ningbo & Liu, Chunwei & Guo, Li & Zhao, Long & Wu, Zongjun & Jiang, Xuelian & Wen, Shenglin & Zhao, Lu & Gong, Daozhi, 2024. "Estimating daily kiwifruit evapotranspiration under regulated deficit irrigation strategy using optimized surface resistance based model," Agricultural Water Management, Elsevier, vol. 295(C).
    10. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    11. Wenju Zhao & Zhaozhao Li & Haolin Li & Xing Li & Pengtao Yang, 2024. "Soil Salinity Prediction in an Arid Area Based on Long Time-Series Multispectral Imaging," Agriculture, MDPI, vol. 14(9), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1635-:d:1494093. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.