• DocumentCode
    3316391
  • Title

    Sensitivity analysis of snow parameters inversion procedure to the passive microwave mixed-pixel patterns

  • Author

    Zhao, Tianjie ; Zhang, Yongpan ; Jiang, Lingmei ; Zhang, Lixin

  • Author_Institution
    State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing, China
  • fYear
    2010
  • fDate
    25-30 July 2010
  • Firstpage
    2386
  • Lastpage
    2389
  • Abstract
    The snow coverage and physical parameters play a special role in the global water and energy budget study. The passive microwave brightness temperatures of snowpack depend not only on the snow depth or snow water equivalent, but also the snow fraction and possible vegetation canopy. In this paper, we established a mixed model for simulating the dry snow radiation, based on the advancements of recent years. Through simulation analysis, it is found that the underestimation of snow fraction will cause the snow depth or snow water equivalent to be overestimated. And the error increases with the increase of snow depth.
  • Keywords
    geophysical signal processing; hydrological techniques; inverse problems; microwave measurement; remote sensing; snow; dry snow radiation; global energy budget; global water budget; inversion procedure sensitivity analysis; passive microwave mixed pixel patterns; snow coverage; snow depth; snow fraction; snow parameter inversion procedure; snow physical parameters; snow water equivalent; snowpack passive microwave brightness temperatures; vegetation canopy; Analytical models; Brightness temperature; Mathematical model; Microwave radiometry; Scattering; Snow; Soil; AMSR-E; Snow; mixed pixel; passive microwave;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
  • Conference_Location
    Honolulu, HI
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4244-9565-8
  • Electronic_ISBN
    2153-6996
  • Type

    conf

  • DOI
    10.1109/IGARSS.2010.5650453
  • Filename
    5650453