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Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Author

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  • Zhihui Yang

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Jun Zhao

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Jialiang Liu

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Yuanyuan Wen

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Yanqiang Wang

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

Abstract

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R 2 ) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.

Suggested Citation

  • Zhihui Yang & Jun Zhao & Jialiang Liu & Yuanyuan Wen & Yanqiang Wang, 2021. "Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12635-:d:679981
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    References listed on IDEAS

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    1. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
    2. Keshavarz, Mohammad Reza & Vazifedoust, Majid & Alizadeh, Amin, 2014. "Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data," Agricultural Water Management, Elsevier, vol. 132(C), pages 37-45.
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    Cited by:

    1. Babak Mohammadi, 2022. "Application of Machine Learning and Remote Sensing in Hydrology," Sustainability, MDPI, vol. 14(13), pages 1-2, June.
    2. Jiahe Cui & Yuchi Wang & Yantao Wu & Zhiyong Li & Hao Li & Bailing Miao & Yongli Wang & Chengzhen Jia & Cunzhu Liang, 2023. "Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    3. Yonela Mndela & Naledzani Ndou & Adolph Nyamugama, 2023. "Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    4. Sinan Wang & Wenjun Wang & Yingjie Wu & Shuixia Zhao, 2022. "Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat," Sustainability, MDPI, vol. 14(16), pages 1-15, August.

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