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The Innovation Geoscience > 2024 Vol. 2 > No. 4 > 100105
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High-resolution soil moisture mapping through passive microwave remote sensing downscaling

  • 1.

    State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211100, China

  • 2.

    College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China

  • 3.

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • 4.

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China

  • 5.

    Department of Biology, Terrestrial Ecology and Center of Volatile Interactions, University of Copenhagen, Copenhagen DK–2100, Denmark

  • 6.

    State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • 7.

    Department of Remote Sensing, Helmholtz Centre for Environmental Research−UFZ, Permoserstrasse 15, 04318 Leipzig, Germany

  • 8.

    Remote Sensing Centre for Earth System Research, Leipzig University, 04103 Leipzig, Germany

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  • Corresponding author: zengjy@radi.ac.cn 
  • Received Date:  September 12, 2024
    Accepted Date:  November 16, 2024
    Published Online:  November 27, 2024
The Innovation Geoscience  2(4),  https://doi.org/10.59717/j.xinn-geo.2024.100105  |  Cite this article
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    • REFERENCES
  • [1] Zeng, J., Li, Z., Chen, Q., et al. (2015). Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sensing of Environment 163 :91-110. DOI: https://doi.org/10.1016/j.rse.2015.03.008.

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    [2] Peng, J., Loew, A., Merlin, O., and Verhoest, N.E.C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics 55 (2):341-366. DOI: https://doi.org/10.1002/2016RG000543.

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    [3] Zhao, W., Wen, F., Wang, Q., et al. (2021). Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products. Journal of Hydrology 603 :126930. DOI: https://doi.org/10.1016/j.jhydrol.2021.126930.

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    [4] Vergopolan, N., Chaney, N.W., Beck, H.E., et al. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment 242 :111740. DOI: https://doi.org/10.1016/j.rse.2020.111740.

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    [5] Huang, S., Zhang, X., Chen, N., et al. (2022). Generating high-accuracy and cloud-free surface soil moisture at 1 km resolution by point-surface data fusion over the Southwestern US. Agricultural and Forest Meteorology 321 :108985. DOI: https://doi.org/10.1016/j.agrformet.2022.108985.

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  • Cite this article:

    Meng X., Zeng J., Yang Y., et al., (2024). High-resolution soil moisture mapping through passive microwave remote sensing downscaling . The Innovation Geoscience 2(4): 100105. https://doi.org/10.59717/j.xinn-geo.2024.100105
    Meng X., Zeng J., Yang Y., et al., (2024). High-resolution soil moisture mapping through passive microwave remote sensing downscaling . The Innovation Geoscience 2(4): 100105. https://doi.org/10.59717/j.xinn-geo.2024.100105

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