• DocumentCode
    2241885
  • Title

    Estimating terrestrial Vegetation Primary Productivity using satellite SAR data

  • Author

    Gao, Shuai ; Niu, Zheng ; Wu, Mingquan ; Liu, Chenzhou

  • Author_Institution
    State Key Lab. of Remote Sensing Sci., Inst. of Remote Sensing Applic., Beijing, China
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    6467
  • Lastpage
    6470
  • Abstract
    A new GPP/NPP model driven by the satellite SAR data was introduced in this paper. It was based on the light use efficiency theory and was referred to the MODIS model for the value of the maximum light use efficiency of different vegetation types. The model was testified in the HEIHE area with ENVISAT-SAR data and showed its feasibility to estimate GPP/NPP. Firstly, the driving factors such as PAR, T_scalar, W_scalar were calculated based on the algorithm and meteorological observation data. Then, the LUT algorithm using the MIMICS model was introduced and validated for its effectiveness to estimate LAI. Finally, the GPP was obtained based on the model and compared with the ground flux observation and MODIS product. The results reveal a potential possibility that the satellite SAR data could be used for the GPP/NPP estimation.
  • Keywords
    remote sensing by radar; synthetic aperture radar; vegetation; vegetation mapping; ENVISAT-SAR data; GPP/NPP estimation; GPP/NPP model; HEIHE area; LUT algorithm; MIMICS model; MODIS model; MODIS product; PAR; T_scalar; W_scalar; ground flux observation; light use efficiency theory; maximum light use efficiency; meteorological observation data; satellite SAR data; terrestrial vegetation primary productivity; vegetation types; Agriculture; Biological system modeling; MODIS; Productivity; Remote sensing; Synthetic aperture radar; Vegetation mapping; ENVISAT/ASAR; FLUX; GPP; LAI;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
  • Conference_Location
    Munich
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4673-1160-1
  • Electronic_ISBN
    2153-6996
  • Type

    conf

  • DOI
    10.1109/IGARSS.2012.6352744
  • Filename
    6352744