An initial assessment of SMOS derived soil moisture over the continental United States

Ming Pan, Alok K. Sahoo, Eric F. Wood, Ahmad Al Bitar, Delphine Leroux, Yann H. Kerr

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Service's (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well.

Original languageEnglish (US)
Article number6211458
Pages (from-to)1448-1457
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume5
Issue number5
DOIs
StatePublished - Jun 13 2012

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences
  • Atmospheric Science

Keywords

  • L-band
  • passive microwave remote sensing
  • soil moisture

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