Abstract

Demonstrating the utility of satellite-based soil moisture () products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground 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 fields are able to characterize with reasonable accuracy the 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 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 fields at the SMAP scale and, to a greater extent, when downscaled L-band 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.

Original languageEnglish (US)
Article numberL10403
JournalGeophysical Research Letters
Volume39
Issue number10
DOIs
StatePublished - 2012

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soil moisture
downscaling
ultrahigh frequencies
catchment
data assimilation
products
sensor
modeling
AMSR-E
C band
footprint
aircraft
assimilation
transect
experiment
simulation
product
sensors
Mexico
data simulation

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Geophysics

Cite this

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title = "Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale",
abstract = "Demonstrating the utility of satellite-based soil moisture () products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground 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 fields are able to characterize with reasonable accuracy the 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 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 fields at the SMAP scale and, to a greater extent, when downscaled L-band 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.",
author = "Giuseppe Mascaro and Enrique Vivoni",
year = "2012",
doi = "10.1029/2012GL051809",
language = "English (US)",
volume = "39",
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T1 - Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale

AU - Mascaro, Giuseppe

AU - Vivoni, Enrique

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N2 - Demonstrating the utility of satellite-based soil moisture () products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground 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 fields are able to characterize with reasonable accuracy the 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 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 fields at the SMAP scale and, to a greater extent, when downscaled L-band 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.

AB - Demonstrating the utility of satellite-based soil moisture () products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground 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 fields are able to characterize with reasonable accuracy the 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 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 fields at the SMAP scale and, to a greater extent, when downscaled L-band 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.

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