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Water Resources Research

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications


  • Yongchul Shin,

    1. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas, USA
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  • Binayak P. Mohanty

    Corresponding author
    1. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas, USA
    • Corresponding author: B. P. Mohanty, Department of Biological and Agricultural Engineering, Texas A&M University, 301 C Scoates Hall, College Station, TX 77843-2117, USA.

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[1] Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: −0.073 to −0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m × 800 m) downscaled by this approach matched well (R: 0.724 to −0.914, MBE: −0.203 to −0.169 for the LW 13; R: 0.343–0.865, MBE: −0.165 to −0.122 for the LW 21) with the in situ local scale soil moisture measurements during Southern Great Plains Experiment 1997 (SGP97). The good correspondence of observed soil water characteristics θ(h) functions (from the soil core samples) and genetic algorithm (GA) searched soil parameters at the LW 13 and 21 sites demonstrated the robustness of the algorithm. Although the algorithm is tested under limited conditions at field scale, this approach improves the availability of remotely sensed soil moisture product at finer resolution for various land surface and hydrological model applications.

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