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Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data

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Abstract

Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 m3/m3, and root-mean-square-error (RMSE) = 0.047 m3/m3, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 m3/m3, and RMSE = 0.152 and 0.103 m3/m3, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 m3/m3, and RMSE = 0.051 m3/m3 excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products.

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Acknowledgements

We appreciate the critical and helpful comments from anonymous reviewers and editor. This work was supported by (1) “Development of Hydrology, wildfire, and statistical Applications” project, funded by Electronics and Telecommunications Research Institute (ETRI), which is a subproject of “Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)” program funded by National Meteorological Satellite Center (NMSC) of Korea Meteorological Administration (KMA), (2) Korea Environment Industry & Technology Institute (KEITI) through Advanced Water Management Research Program, funded by Korea Ministry of Environment (MOE) (Grant 83079), and (3) KMA Research and Development Program under Grant KMI2018-05710.

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Authors and Affiliations

  1. Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdongro, Gwangjin-gu, Seoul, 05006, South Korea

    Young-Joo Kwon, Sumin Ryu & Sungwook Hong

  2. Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute, Incheon, 21990, South Korea

    Young-Joo Kwon

  3. Department of Applied Plant Science, Chonnam National University, Gwangju, 61186, South Korea

    Jaeil Cho

  4. Department of Spatial Information Engineering, Pukyong National University, Busan, 48513, South Korea

    Yang-Won Lee

  5. Department of Geoinformatic Engineering, Inha University, Incheon, 22212, South Korea

    No-Wook Park

  6. National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon-gun, 27803, South Korea

    Chu-Yong Chung

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  1. Young-Joo Kwon
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  2. Sumin Ryu
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  3. Jaeil Cho
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  4. Yang-Won Lee
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  7. Sungwook Hong
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Correspondence to Sungwook Hong.

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Kwon, YJ., Ryu, S., Cho, J. et al. Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data. Asia-Pacific J Atmos Sci 56, 275–289 (2020). https://doi.org/10.1007/s13143-020-00174-6

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  • DOI: https://doi.org/10.1007/s13143-020-00174-6

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