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Utility of coarse and downscaled soil moisture products at L‐band for hydrologic modeling at the catchment scale
Author(s) -
Mascaro Giuseppe,
Vivoni Enrique R.
Publication year - 2012
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2012gl051809
Subject(s) - downscaling , environmental science , data assimilation , satellite , scale (ratio) , remote sensing , drainage basin , hydrological modelling , meteorology , atmospheric sciences , climatology , precipitation , geology , physics , quantum mechanics , engineering , aerospace engineering , cartography , geography
Demonstrating the utility of satellite‐based soil moisture ( θ ) products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground θ data collected during the SMEX04 experiment in Sonora (Mexico) to compare two downscaling frameworks using C‐band and L‐band sensors. We show that the L‐band framework, which mimics the disaggregation of SMAP products, has considerably better performance than the C‐band framework simulating the downscaling of AMSR‐E. Disaggregated L‐band θ fields are able to characterize with reasonable accuracy the θ variability at multiple extent scales, including the SMAP footprint and the catchment scale, and along an elevation transect. We then test the utility of coarse and downscaled C‐ and L‐band θ estimates for hydrologic simulations through data assimilation experiments using a distributed hydrologic model. Results reveal that the model prognostic capability is significantly enhanced when using L‐band θ fields at the SMAP scale and, to a greater extent, when downscaled L‐band θ fields are assimilated. L‐band data assimilation leads to higher model fidelity relative to ground data as well as more realistic soil moisture patterns at the catchment scale. This study indicates the potential value of satellite‐based L‐band sensors for hydrologic modeling when coupled with a statistical downscaling algorithm.