• DocumentCode
    2224077
  • Title

    The retrieval and monitoring of vegetation parameters from COSMO-SkyMed images

  • Author

    Santi, E. ; Fontanelli, G. ; Montomoli, F. ; Brogioni, M. ; Macelloni, G. ; Paloscia, S. ; Pettinato, S. ; Pampaloni, P.

  • Author_Institution
    Res. Area of Florence, IFAC, Florence, Italy
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    7031
  • Lastpage
    7034
  • Abstract
    The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in this paper. SAR data from COSMO-SkyMed were collected on two agricultural areas in Italy in 2010 at different dates during the vegetation cycle. The performances of X-band data have been compared with accurate ground truth measurements of soil and vegetation carried out simultaneously to satellite passes. Experimental data have been compared with model simulations obtained with a discrete element radiative transfer model. Moreover, an inversion algorithm, based on an Artificial Neural Network and trained by using AIEM and the radiative transfer model, has been applied to retrieve the plant water content of wheat and sunflower crops and to generate the corresponding plant water content maps.
  • Keywords
    crops; geophysical signal processing; inverse problems; neural nets; radiative transfer; remote sensing by radar; vegetation mapping; AD 2010; AIEM training; COSMO SkyMed images; Italy; SAR data; X-band data performance; artificial neural network; discrete element radiative transfer model; ground truth measurements; inversion algorithm; plant water content; sunflower crop; vegetation biomass estimation; vegetation cycle; vegetation parameter monitoring; vegetation parameter retrieval; wheat crop; Agriculture; Backscatter; Biological system modeling; Biomass; Data models; Market research; Vegetation mapping; ANN inversion algorithm; Cosmo-SkyMed; X-band; discrete element radiative transfer model; sensitivity to biomass; vegetation parameters;
  • 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.2012.6351952
  • Filename
    6351952