• Bindlish, R., Jackson T. J. , Wood E. F. , Gao H. , Starks P. , Bosch D. , and Lakshmi V. , 2003: Soil moisture estimates from TRMM Microwave Imager observations over the southern United States. Remote Sens. Environ., 85 , 507515.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108 .8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Cosh, M. H., Jackson T. J. , Bindlish R. , and Prueger J. H. , 2004: Watershed scale temporal persistence of soil moisture and its role in validating satellite estimates. Remote Sens. Environ., 92 , 427435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crow, W. T., Bindlish R. , and Jackson T. J. , 2005a: The added value of spaceborne passive microwave soil moisture retrievals for forecasting rainfall-runoff partitioning. Geophys. Res. Lett., 32 .L18401, doi:10.1029/2005GL023543.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., Koster R. D. , Reichle R. H. , and Sharif H. O. , 2005b: Relevance of time-varying and time-invariant retrieval error sources on the utility of spaceborne soil moisture products. Geophys. Res. Lett., 32 .L24405, doi:10.1029/2005GL024889.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123 , 11281145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drusch, M., Wood E. F. , and Jackson T. J. , 2001: Vegetation and atmospheric corrections for the soil moisture retrieval from passive microwave remote sensing data: Results from the 1997 Southern Great Plains Hydrology Experiment. J. Hydrometeor., 2 , 181192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Famiglietti, J. S., and Coauthors, 1999: Ground-based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains (SGP97) Hydrology Experiment. Water Resour. Res., 35 , 18391851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., Wood E. F. , Drusch M. , Crow W. T. , and Jackson T. J. , 2004: Using a microwave emission model to estimate soil moisture from ESTAR observations during SGP99. J. Hydrometeor., 5 , 4963.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., Wood E. F. , Drusch M. , Jackson T. , and Bindlish R. , 2006: Using TRMM/TMI to retrieve soil moisture over the southern United States from 1998 to 2002. J. Hydrometeor., 7 , 2338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Groisman, P. Y., and Legates D. R. , 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc., 75 , 215227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Morrissey M. M. , Bolvin D. T. , Curtis S. , Joyce R. , McGavock B. , and Susskind J. , 2001: Global precipitation at one-degree daily resolution from mulitisatellite observations. J. Hydrometeor., 2 , 3650.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., Le Vine D. M. , Hsu A. Y. , Oldak A. , Starks P. J. , Swift C. T. , Isham J. , and Haken M. , 1999: Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment. IEEE Trans. Geosci. Remote Sens., 37 , 21362151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacobs, J. M., Mohanty B. P. , Hsu E. C. , and Miller D. , 2004: Field scale variability and similarity of soil moisture. Remote Sens. Environ., 92 , 436446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., Waldteufel P. , Wigneron J-P. , Martinuzzi J-M. , Font J. , and Berger M. , 2001: Soil moisture retrieval from space: The soil moisture and ocean salinity mission (SMOS). IEEE Trans. Geosci. Remote Sens., 39 , 17291735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leese, J., Jackson T. J. , Pitman A. , and Dirmeyer P. , 2001: GEWEX/BAHC international workshop on soil moisture monitoring, analysis and prediction for hydrometeorological and hydroclimatological applications. Bull. Amer. Meteor. Soc., 82 , 14231430.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhee, J., and Margulis S. , 2005: Validation and error characterization of GPCP-1DD precipitation product over the contiguous United States. J. Hydrometeor., 6 , 441459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Njoku, E. G., Jackson T. J. , Lakshmi V. , Chan T. , and Nghiem S. V. , 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41 , 215229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Koster R. D. , 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys. Res. Lett., 32 .L02404, doi:10.1029/2004GL021700.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., McLaughlin D. B. , and Entekhabi D. , 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130 , 103114.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 118 79 1
PDF Downloads 36 20 1

A Novel Method for Quantifying Value in Spaceborne Soil Moisture Retrievals

View More View Less
  • 1 Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, Maryland
Restricted access

Abstract

A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher-quality rain gauge observations. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely sensed soil moisture datasets along two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies varies geographically.

Corresponding author address: W. T. Crow, Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Rm. 104, Bldg. 007, BARC-W, Beltsville, MD 20705. Email: wcrow@hydrolab.arsusda.gov

Abstract

A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher-quality rain gauge observations. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely sensed soil moisture datasets along two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies varies geographically.

Corresponding author address: W. T. Crow, Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Rm. 104, Bldg. 007, BARC-W, Beltsville, MD 20705. Email: wcrow@hydrolab.arsusda.gov

Save