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IEEE Transactions on Geoscience and Remote Sensing | Vol.60, Issue. | | Pages 1-10
Potential of Mapping Global Soil Texture Type From SMAP Soil Moisture Product: A Pilot Study
Soil texture and associated thermal and hydraulic parameters are key to land surface processes. Current global soil datasets are derived from limited soil samples, which are not only very costly but also prone to large uncertainties. While it is difficult to directly retrieve soil properties through satellite remote sensing, this study explores the feasibility of mapping global soil type and thereby corresponding soil texture through the Soil Moisture Active Passive (SMAP) soil moisture product without reference to soil samples. Specifically, for each grid-cell, 12 U.S. Department of Agriculture (USDA) soil types are first used to drive the Noah-MP land surface model and then the optimal one is obtained by referring to a four-year (2015–2018) SMAP soil moisture time series. The proposed scheme can reasonably map the global distribution of soil types in terms of sand/clay content and porosity that are close to the Global Soil Dataset for Earth System Models (GSDE) dataset and outperform the one used in the Global Land Data Assimilation System (GLDAS)/Noah model. The result of this pilot study is very encouraging as it purely relies on satellite data, which is especially important for remote areas where few soil samples are available and conventional soil datasets may have large biases. Further improvements may be achieved upon improved soil organic matter parameterization, through land data assimilation, and by considering additional satellite information.
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Potential of Mapping Global Soil Texture Type From SMAP Soil Moisture Product: A Pilot Study
Soil texture and associated thermal and hydraulic parameters are key to land surface processes. Current global soil datasets are derived from limited soil samples, which are not only very costly but also prone to large uncertainties. While it is difficult to directly retrieve soil properties through satellite remote sensing, this study explores the feasibility of mapping global soil type and thereby corresponding soil texture through the Soil Moisture Active Passive (SMAP) soil moisture product without reference to soil samples. Specifically, for each grid-cell, 12 U.S. Department of Agriculture (USDA) soil types are first used to drive the Noah-MP land surface model and then the optimal one is obtained by referring to a four-year (2015–2018) SMAP soil moisture time series. The proposed scheme can reasonably map the global distribution of soil types in terms of sand/clay content and porosity that are close to the Global Soil Dataset for Earth System Models (GSDE) dataset and outperform the one used in the Global Land Data Assimilation System (GLDAS)/Noah model. The result of this pilot study is very encouraging as it purely relies on satellite data, which is especially important for remote areas where few soil samples are available and conventional soil datasets may have large biases. Further improvements may be achieved upon improved soil organic matter parameterization, through land data assimilation, and by considering additional satellite information.
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