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Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

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  • Shu-Di Fan

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 10642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Yue-Ming Hu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 10642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Lu Wang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Zhen-Hua Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 10642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Zhou Shi

    (Institute of Agricultural Remote Sensing & Information System, Zhejiang University, Hangzhou 310029, China)

  • Wen-Bin Wu

    (Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Yu-Chun Pan

    (Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Guang-Xing Wang

    (Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 10642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
    Department of Geography and Environmental Resources, College of Liberal Arts, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA)

  • A-Xing Zhu

    (Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 10642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
    Department of Geography, University of Wisconsin, Madison, WI 53706, USA)

  • Bo Li

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China)

Abstract

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index ( TVDI ) using data from the Project for On-Board Autonomy ( PROBA-V ). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V , and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

Suggested Citation

  • Shu-Di Fan & Yue-Ming Hu & Lu Wang & Zhen-Hua Liu & Zhou Shi & Wen-Bin Wu & Yu-Chun Pan & Guang-Xing Wang & A-Xing Zhu & Bo Li, 2018. "Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3459-:d:172531
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    References listed on IDEAS

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    1. Yang, Yanmin & Yang, Yonghui & Moiwo, Juana Paul & Hu, Yukun, 2010. "Estimation of irrigation requirement for sustainable water resources reallocation in North China," Agricultural Water Management, Elsevier, vol. 97(11), pages 1711-1721, November.
    2. Jagannath Aryal & Didier Josselin, 2014. "Environmental Object Recognition in a Natural Image: An Experimental Approach Using Geographic Object-Based Image Analysis (GEOBIA)," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 5(1), pages 1-18, January.
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