IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v260y2022ics0378377421005758.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421005758
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107298?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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).

    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. Abdul Waheed & Chuang Li & Murad Muhammad & Mushtaq Ahmad & Khalid Ali Khan & Hamed A. Ghramh & Zhongwei Wang & Daoyuan Zhang, 2023. "Sustainable Potato Growth under Straw Mulching Practices," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    2. Kaur, Lovepreet & Kaur, Anureet & Brar, A.S., 2021. "Water use efficiency of green gram (Vigna radiata L.) impacted by paddy straw mulch and irrigation regimes in north-western India," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Kader, M.A. & Nakamura, K. & Senge, M. & Mojid, M.A. & Kawashima, S., 2019. "Soil hydro-thermal regimes and water use efficiency of rain-fed soybean (Glycine max) as affected by organic mulches," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    4. Tang, Zijun & Lu, Junsheng & Xiang, Youzhen & Shi, Hongzhao & Sun, Tao & Zhang, Wei & Wang, Han & Zhang, Xueyan & Li, Zhijun & Zhang, Fucang, 2024. "Farmland mulching and optimized irrigation increase water productivity and seed yield by regulating functional parameters of soybean (Glycine max L.) leaves," Agricultural Water Management, Elsevier, vol. 298(C).
    5. Xuemei Lan & Shouxi Chai & Jeffrey A. Coulter & Hongbo Cheng & Lei Chang & Caixia Huang & Rui Li & Yuwei Chai & Yawei Li & Jiantao Ma & Li Li, 2020. "Maize Straw Strip Mulching as a Replacement for Plastic Film Mulching in Maize Production in a Semiarid Region," Sustainability, MDPI, vol. 12(15), pages 1-26, August.
    6. Song, Fang & Liu, Ming & Zhang, Zhongxue & Qi, Zhijuan & Li, Tiecheng & Du, Sicheng & Li, Ao & Liu, Jie, 2024. "No-tillage with straw mulching increased maize yield and nitrogen fertilizer recovery rate in northeast China," Agricultural Water Management, Elsevier, vol. 292(C).
    7. Alfonso, C. & Barbieri, P.A. & Hernández, M.D. & Lewczuk, N.A & Martínez, J.P. & Echarte, M.M. & Echarte, L., 2020. "Water productivity in soybean following a cover crop in a humid environment," Agricultural Water Management, Elsevier, vol. 232(C).
    8. Li, Yue & Chen, Hao & Feng, Hao & Dong, Qin’ge & Wu, Wenjie & Zou, Yufeng & Chau, Henry Wai & Siddique, Kadambot H.M., 2020. "Influence of straw incorporation on soil water utilization and summer maize productivity: A five-year field study on the Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 233(C).
    9. Fang, Qin & Wang, Yanzhe & Uwimpaye, Fasilate & Yan, Zongzheng & Li, Lu & Liu, Xiuwei & Shao, Liwei, 2021. "Pre-sowing soil water conditions and water conservation measures affecting the yield and water productivity of summer maize," Agricultural Water Management, Elsevier, vol. 245(C).
    10. Wenguang Chen & Bangbang Zhang & Xiangbin Kong & Liangyou Wen & Yubo Liao & Lingxin Kong, 2022. "Soybean Production and Spatial Agglomeration in China from 1949 to 2019," Land, MDPI, vol. 11(5), pages 1-17, May.

    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:eee:agiwat:v:260:y:2022:i:c:s0378377421005758. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.