An initial assessment of SMAP soil moisture retrievals using high‐resolution model simulations and in situ observations

At the end of its first year of operation, we compare soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission to simulations from a land surface model with meteorological forcing downscaled from observations/reanalysis and in situ observations from sparse monitoring networks within continental United States (CONUS). The radar failure limits the duration of comparisons for the active and combined products (~3 months). Nevertheless, the passive product compares very well against in situ observations over CONUS. On average, SMAP compares to the in situ data even better than the land surface model and provides significant added value on top of the model and thus good potential for data assimilation. At large scale, SMAP is in good agreement with the model in most of CONUS with less‐than‐expected degradation over mountainous areas. Lower correlation between SMAP and the model is seen in the forested east CONUS and significantly lower over the Canadian boreal forests.

[1]  J. Kong,et al.  Theory for passive microwave remote sensing of near‐surface soil moisture , 1977 .

[2]  Thomas J. Schmugge,et al.  Microwave Remote Sensing Of Soil Moisture , 1984, Other Conferences.

[3]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[4]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[5]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[6]  D. Lettenmaier,et al.  Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification , 1996 .

[7]  Maurice Borgeaud,et al.  A study of vegetation cover effects on ERS scatterometer data , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[9]  Jeffrey P. Walker,et al.  A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index , 2001, IEEE Trans. Geosci. Remote. Sens..

[10]  J. D. Tarpley,et al.  Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project , 2003 .

[11]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[12]  E. Wood,et al.  Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off‐line simulation of the terrestrial hydrologic cycle , 2007 .

[13]  Y. Kerr,et al.  L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields , 2007 .

[14]  T. Jackson,et al.  The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN) , 2007 .

[15]  Eric F. Wood,et al.  An efficient calibration method for continental‐scale land surface modeling , 2008 .

[16]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[17]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[18]  Eric F. Wood,et al.  Impact of Accuracy, Spatial Availability, and Revisit Time of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  M. Ek,et al.  Comparative analysis of relationships between NLDAS‐2 forcings and model outputs , 2012 .

[20]  John Kochendorfer,et al.  U.S. Climate Reference Network Soil Moisture and Temperature Observations , 2013 .

[21]  Rolf H. Reichle,et al.  Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS , 2013, Surveys in Geophysics.

[22]  Christopher Ruf,et al.  Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[23]  A. Sahoo,et al.  Improving soil moisture retrievals from a physically-based radiative transfer model , 2014 .

[24]  Yann Kerr,et al.  Assessment of the SMAP Passive Soil Moisture Product , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Wade T. Crow,et al.  Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.