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Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China

Author

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  • Cheng, Minghan
  • Li, Binbin
  • Jiao, Xiyun
  • Huang, Xiao
  • Fan, Haiyan
  • Lin, Rencai
  • Liu, Kaihua

Abstract

An accurate regional estimate of soil moisture content (SMC) is important for water management and drought monitoring. Traditional ground measurement methods of SMC are limited by the disadvantages of high cost and small scale. The development of remote sensing (RS) technology provides a cost-effective tool for estimating SMC at regional scale. However, the estimation of SMC by using the combination of multiple sensors has yet to be thoroughly discussed. Furthermore, the way in which vegetation types, fraction of vegetation coverage (FVC) and soil layer depth affect the SMC-estimation performance remains unclear. Therefore, the objectives in this study are to (1) evaluate the SMC-estimation performance provided by Landsat-8 data and random forest regression (RFR) algorithm; (2) discuss the accuracy of RS-based method for SMC estimation at different soil layer depths, and (3) explore how vegetation types and FVC affect the performance of SMC-estimation. The results can be summarized as: (1) the SMC estimation performance of multispectral (MS)- and thermal infrared (TIR)-based indices single used were comparable, in which TIR-based indices performed better in shallow soil layer while MS-based indices performed better in deep soil layer. Generally, MS- and TIR-based indices jointly used outperformed the index single used. (2) the accuracy of the proposed method for estimating SMC decreased with soil depth. (3) the proposed method performed greatest in grassland with relatively low height among the three vegetation types. Moreover, the SMC estimation in moderate vegetation coverage (FVC ranged from 0.3 to 0.5) was best. These results indicate that RS-based multimodal data combined with RFR could provide relatively repeatable and accurate SMC estimation. This approach can thus be used for the regional SMC monitoring and water resources management.

Suggested Citation

  • Cheng, Minghan & Li, Binbin & Jiao, Xiyun & Huang, Xiao & Fan, Haiyan & Lin, Rencai & Liu, Kaihua, 2022. "Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China," Agricultural Water Management, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:agiwat:v:260:y:2022:i:c:s0378377421005758
    DOI: 10.1016/j.agwat.2021.107298
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    References listed on IDEAS

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    1. Akhtar, Kashif & Wang, Weiyu & Khan, Ahmad & Ren, Guangxin & Afridi, Muhammad Zahir & Feng, Yongzhong & Yang, Gaihe, 2019. "Wheat straw mulching offset soil moisture deficient for improving physiological and growth performance of summer sown soybean," Agricultural Water Management, Elsevier, vol. 211(C), pages 16-25.
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    Cited by:

    1. Tailin Li & Massimiliano Schiavo & David Zumr, . "Seasonal variations of vegetative indices and their correlation with evapotranspiration and soil water storage in a small agricultural catchment," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 0.
    2. Zhang, Siyao & Li, Jianzhu & Zhang, Ting & Feng, Ping & Liu, Weilin, 2024. "Response of vegetation to SPI and driving factors in Chinese mainland," Agricultural Water Management, Elsevier, vol. 291(C).
    3. Zhangxin Liu & Haoran Ju & Qiyun Ma & Chengming Sun & Yuping Lv & Kaihua Liu & Tianao Wu & Minghan Cheng, 2024. "Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China," Agriculture, MDPI, vol. 14(4), pages 1-15, April.
    4. 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).
    5. Tailin Li & Massimiliano Schiavo & David Zumr, 2023. "Seasonal variations of vegetative indices and their correlation with evapotranspiration and soil water storage in a small agricultural catchment," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(4), pages 246-268.
    6. 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).

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