• Bailey, S., , and Werdell J. , 2006: A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sens. Environ., 102, 1223.

    • Search Google Scholar
    • Export Citation
  • Bamler, R., , and Eineder M. , 1996: ScanSAR processing using standard high precision SAR algorithms. IEEE Trans. Geosci. Remote Sens., 34, 212218.

    • Search Google Scholar
    • Export Citation
  • Baup, F., , Mougin E. , , de Rosnay P. , , Timouk F. , , and Chênerie I. , 2007: Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data. Remote Sens. Environ., 109, 473481.

    • Search Google Scholar
    • Export Citation
  • Bissett, W., , Arnone R. , , Davis C. , , Dickey T. , , Dye D. , , Kohler D. , , and Gould R. , 2004: From meter to kilometers: A look at ocean-color scales of variability, spatial coherence, and the need for fine-scale remote sensing in coastal ocean optics. Oceanography, 17, 3243.

    • Search Google Scholar
    • Export Citation
  • Chen, F., , and Dudhia J. , 2001: Coupling an advanced land surface hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., , and Wood E. F. , 2003: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Adv. Water Resour., 26, 137149.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., , Koster R. D. , , Reichle R. H. , , and Sharif H. O. , 2005: 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
  • Draper, C. S., , Mahfouf J.-F. , , and Walker J. P. , 2009: An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme. J. Geophys. Res., 114, D20104, doi:10.1029/2008JD011650.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., 2007: Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF’s Integrated Forecast System and the TMI soil moisture data set. J. Geophys. Res., 112, D03102, doi:10.1029/2006JD007478.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., , Wood E. F. , , and Gao H. , 2005: Observation operators for direct assimilation of TRMM microwave imager retrieved soil moisture. Geophys. Res. Lett., 32, L15403, doi:10.1029/2005GL023623.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State–NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 14931513.

    • Search Google Scholar
    • Export Citation
  • Dunne, S., , and Entekhabi D. , 2005: An ensemble-based reanalysis approach to land data assimilation. Water Resour. Res., 41, W02013, doi:10.1029/2004WR003449.

    • Search Google Scholar
    • Export Citation
  • Dunne, S., , and Entekhabi D. , 2006: Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plain 1997 field experiment. Water Resour. Res., 42, W01407, doi:10.1029/2005WR004334.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704716.

  • Escorihuela, M. J., , Chanzy A. , , Wigneron J. P. , , and Kerr Y. H. , 2010: Effective soil moisture sampling depth of L-band radiometry: A case study. Remote Sens. Environ., 114, 9951001.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 2006: Data Assimilation: The Ensemble Kalman Filter. Springer Press, 279 pp.

  • Famiglietti, J. S., , Rudnicki J. W. , , and Rodell M. , 1998: Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas. J. Hydrol., 210, 259281.

    • Search Google Scholar
    • Export Citation
  • Francois, C., , Quesney A. , , and Ottlé C. , 2003: Sequential assimilation of ERS-1 SAR data into a coupled land surface–hydrological model using an extended Kalman filter. J. Hydrometeor., 4, 473487.

    • Search Google Scholar
    • Export Citation
  • Fung, A. K., , Li Z. , , and Chen K. S. , 1992: Backscattering from a randomly rough dielectric surface. IEEE Trans. Geosci. Remote Sens., 30, 356369.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , and Avissar R. , 1997: Representation of heterogeneity effects in earth system modeling: Experience from land surface modeling. Rev. Geophys., 35, 413438.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., , Dudhia J. , , and Staufer D. , 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-380+STR, 138 pp.

  • Hong, S. Y., , and Pan H. L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 23222339.

    • Search Google Scholar
    • Export Citation
  • Houser, P. R., , Shuttleworth W. J. , , Famiglietti J. S. , , Gupta H. V. , , Syed K. H. , , and Goodrich D. C. , 1998: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res., 34, 34053420.

    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., and Coauthors, 2010: Validation of Advanced Microwave Scanning Radiometer soil moisture products. IEEE T. Geosci. Remote, 48, 42564272.

    • 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 (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 17291735.

    • Search Google Scholar
    • Export Citation
  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247267.

    • Search Google Scholar
    • Export Citation
  • Koster, D. D., , Guo Z. , , Yang R. , , Dirmeyer P. A. , , Mitchell K. , , and Puma M. J. , 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335.

    • Search Google Scholar
    • Export Citation
  • Lee, J. S., , and Pottier E. , 2009: Polarimetric Radar Imaging: From Basics to Application. CRC Press, 398 pp.

  • Lee, J. S., , Jurkevich L. , , Dewaele P. , , Wambacq P. , , and Oosterlinck A. , 1994: Speckle filtering of synthetic aperture radar images: A review. Remote Sens. Rev., 8, 313340.

    • Search Google Scholar
    • Export Citation
  • Löw, A., , Ludwig R. , , and Mauser W. , 2006: Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agricultural areas. IEEE Trans. Geosci. Remote Sens., 44, 889899.

    • Search Google Scholar
    • Export Citation
  • Masson, V., , Champeaux J.-L. , , Chauvin F. , , Meriguet C. , , and Lacaze R. , 2003: A global database of land surface parameters at 1-km resolution in meteorological and climate models. J. Climate, 16, 12611282.

    • Search Google Scholar
    • Export Citation
  • Mattikalli, N. M., , Engman E. T. , , Ahuja L. R. , , and Jackson T. J. , 1998: Microwave remote sensing of soil moisture for estimation of profile soil property. Int. J. Remote Sens., 19, 17511767.

    • Search Google Scholar
    • Export Citation
  • McLaughlin, D., 2002: An integrated approach to hydrologic data assimilation: Interpolation, smoothing, and filtering. Adv. Water Resour., 25, 12751286.

    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., , Crow W. T. , , and Cosh M. H. , 2010: Estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations. J. Hydrometeor., 11, 14231429.

    • Search Google Scholar
    • Export Citation
  • Mladenova, I., , Lakshmi V. , , Walker J. P. , , Panciera R. , , Wagner W. , , and Doubkova M. , 2010: Validation of the ASAR Global Monitoring mode soil moisture product using the NAFE’05 data set. IEEE Trans. Geosci. Remote Sens., 48, 24982508.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., , Taubman S. J. , , Brown P. D. , , Iacono M. J. , , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Montaldo, N., , and Albertson J. D. , 2003: Temporal dynamics of soil moisture variability: 2. Implication for land surface model. Water Resour. Res., 39, 1275, doi:10.1029/2002WR001618.

    • Search Google Scholar
    • Export Citation
  • Ni-Meister, W., , Walker J. P. , , and Houser P. R. , 2005: Soil moisture initialization for climate prediction: Characterization of model and observation errors. J. Geophys. Res., 110, D13111, doi:10.1029/2004JD005745.

    • Search Google Scholar
    • Export Citation
  • Ni-Meister, W., , Houser P. R. , , and Walker J. P. , 2006: Soil moisture initialization for climate prediction: Assimilation of scanning multifrequency microwave radiometer soil moisture data into a land surface model. J. Geophys. Res., 111, D20102, doi:10.1029/2006JD007190.

    • Search Google Scholar
    • Export Citation
  • Owe, M., , De Jeu R. A. M. , , and Holmes T. R. H. , 2008: Multi-sensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res., 113, F01002, doi:1029/2007JF000769.

    • Search Google Scholar
    • Export Citation
  • Pathe, C., , Wagner W. , , Sabel D. , , Doubkova M. , , and Basara J. B. , 2009: Using ENVISAT ASAR Global Mode data for surface soil moisture retrieval over Oklahoma, USA. IEEE Trans. Geosci. Remote Sens., 47, 468480.

    • Search Google Scholar
    • Export Citation
  • Pauwels, V. R. N., , Hoeben R. , , Verhoest N. E. C. , , De Troch F. P. , , and Troch P. A. , 2002: Improvement of TOPLATS-based discharge predictions through assimilation of ERS-based remotely sensed soil moisture values. Hydrol. Processes, 16, 9951013.

    • Search Google Scholar
    • Export Citation
  • Pauwels, V. R. N., , Balenzano A. , , Satalino G. , , Skriver H. , , Verhoest N. E. C. , , and Mattia F. , 2009: Optimization of soil hydraulic model parameters using synthetic aperture radar data: An integrated multidisciplinary approach. IEEE Trans. Geosci. Remote Sens., 47, 455467.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., 2008: Data assimilation methods in the earth sciences. Adv. Water Resour., 31, 14111418.

  • Reichle, R. H., , and Koster R. D. , 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , Entekhabi D. , , and McLaughlin D. B. , 2001a: Downscaling of radio brightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resour. Res., 37, 23532364.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , McLaughlin D. B. , , and Entekhabi D. , 2001b: Variational data assimilation of microwave radiobrightness observation for land surface hydrology applications. IEEE Trans. Geosci. Remote Sens., 39, 17081718.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , Koster R. D. , , Dong J. , , and Berg A. A. , 2004: Global soil moisture from satellite observations, land surface models, and ground data: Implication for data assimilation. J. Hydrometeor., 5, 430442.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , Koster R. D. , , Liu P. , , Mahanama S. P. P. , , Njoku E. G. , , and Owe M. , 2007: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033.

    • Search Google Scholar
    • Export Citation
  • Rodell, M, and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394.

  • Salama, M. S., , and Su Z. , 2011: Resolving the subscale spatial variability of apparent and inherent optical properties in ocean color match-up sites. IEEE Trans. Geosci. Remote Sens., 49, 26122622.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., , Peters-Lidard C. D. , , Garcia M. E. , , Mocko D. M. , , Tischler M. A. , , Moran M. S. , , and Thoma D. P. , 2007: Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed. Remote Sens. Environ., 110, 7997.

    • Search Google Scholar
    • Export Citation
  • Scipal, K., , Holmes T. , , de Jeu R. , , Naeimi V. , , and Wagner W. , 2008: A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys. Res. Lett., 35, L24403, doi:10.1029/2008GL035599.

    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., , Uijlenhoet R. , , Hurkmans R. , , Merlin O. , , Panciera R. , , Walker J. P. , , and Troch P. A. , 2007: Dry-end surface soil moisture variability during NAFE’06. Geophys. Res. Lett., 34, L17402, doi:10.1029/2007GL031001.

    • Search Google Scholar
    • Export Citation
  • Van der Velde, R., , and Su Z. , 2009: Dynamics in land-surface conditions on the Tibetan Plateau observed by Advanced Synthetic Aperture Radar (ASAR). Hydrol. Sci. J., 54, 10791093.

    • Search Google Scholar
    • Export Citation
  • Van der Velde, R., , Su Z. , , and Ma Y. , 2008: Impact of soil moisture dynamics on ASAR σo signatures and its spatial variability observed over the Tibetan Plateau. Sensors, 8, 54795491.

    • Search Google Scholar
    • Export Citation
  • Van der Velde, R., , Su Z. , , van Oevelen P. , , Wen J. , , Ma Y. , , and Salama M. S. , 2012: Soil moisture mapping over the central part of the Tibetan Plateau using a series of ASAR WS images. Remote Sens. Environ., 120, 175187.

    • Search Google Scholar
    • Export Citation
  • Vereecken, H., , Kamai T. , , Harter T. , , Kasteel R. , , Hopmans J. , , and Vanderborght J. , 2007: Explaining soil moisture variability as a function of mean soil moisture: A stochastic unsaturated flow perspective. Geophys. Res. Lett., 34, L22402, doi:10.1029/2007GL031813.

    • Search Google Scholar
    • Export Citation
  • Wagner, W., , and Scipal K. , 2000: Large-scale soil moisture mapping in western Africa using the ERS Scatterometer. IEEE Trans. Geosci. Remote Sens., 38, 17771782.

    • Search Google Scholar
    • Export Citation
  • Walker, J. P., , and Houser P. R. , 2004: Requirements of a global near-surface soil moisture satellite mission: Accuracy, repeat time, and spatial resolution. Adv. Water Resour., 27, 785801.

    • Search Google Scholar
    • Export Citation
  • Walker, J. P., , Willgoose G. R. , , and Kalma J. D. , 2001: One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: A simplified soil moisture model and field application. J. Hydrometeor., 2, 356373.

    • Search Google Scholar
    • Export Citation
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Decomposition of Uncertainties between Coarse MM5–Noah-Simulated and Fine ASAR-Retrieved Soil Moisture over Central Tibet

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  • 1 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
  • | 2 Institute of Tibetan Plateau Research (ITP/CAS), Beijing, China
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Abstract

Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.

Corresponding author address: Rogier van der Velde, Hengelosestraat 99, 7514 AE Enschede, Netherlands. E-mail: velde@itc.nl

Abstract

Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.

Corresponding author address: Rogier van der Velde, Hengelosestraat 99, 7514 AE Enschede, Netherlands. E-mail: velde@itc.nl
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