• DocumentCode
    3056057
  • Title

    On the assessment of SMOS salinity retrieval by using Support Vector Regression (SVR)

  • Author

    Sabia, R. ; Marconcini, Mattia ; Katagis, T. ; Fernandez-Prieto, Diego ; Portabella, Marcos

  • Author_Institution
    ESA-ESRIN, Eur. Space Agency, Frascati, Italy
  • fYear
    2013
  • fDate
    21-26 July 2013
  • Firstpage
    1555
  • Lastpage
    1558
  • Abstract
    A sounding of the capabilities of a novel salinity retrieval strategy by means of Support Vector Regression (SVR) has been performed. SMOS brightness temperatures measurements and additional auxiliary parameters have been co-located with salinity data collected by ARGO buoys, which represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR are in good agreement with the ground-truth, suggesting that the chosen approach can be promising, despite its robustness and versatility are under further assessment over wider areas and time lags, and in various combinations of SMOS features.
  • Keywords
    regression analysis; remote sensing; salinity (geophysical); ARGO buoys; SMOS brightness temperatures measurements; SMOS features; SMOS salinity retrieval assessment; auxiliary parameters; ground-truth; salinity data; salinity fields; salinity retrieval strategy; support vector regression; Extraterrestrial measurements; Robustness; Sea measurements; Sea surface salinity; Support vector machines; Training; Ocean Salinity; Regression; SMOS; Support Vector Machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
  • Conference_Location
    Melbourne, VIC
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4799-1114-1
  • Type

    conf

  • DOI
    10.1109/IGARSS.2013.6723085
  • Filename
    6723085