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@ARTICLE{Briaux:19606,
      author       = {Bériaux, E. and Lambot, S. and Defourny, P.},
      title        = {{E}stimating surface-soil moisture for retrieving maize
                      leaf-area index from {SAR} data},
      journal      = {Canadian Journal of Remote Sensing},
      volume       = {37},
      reportid     = {PreJuSER-19606},
      pages        = {136 - 150},
      year         = {2011},
      note         = {This research was funded through STEREO II grant SR/00/101
                      (GLOBAM project) of the Belgian Science Policy. The ERS and
                      ENVISAT SAR data were provided by the European Space Agency
                      under the Category 1 Project. The parcel delineations were
                      made available by the Direction of Agriculture of the
                      Walloon Region. The authors also thank Allard de Wit for
                      recommending the SWAP model.},
      abstract     = {The leaf-area index (LAI) is a key parameter for coupling
                      earth-observation data with crop-growth models from the
                      perspective of crop-yield forecasting. Remote sensing is of
                      particular interest in estimating LAI over large areas. SAR
                      data, thanks to their systematic acquisition, offer an ideal
                      temporal resolution throughout the crop-growing season.
                      Nevertheless, surface soil dielectric permittivity, which is
                      strongly correlated with soil moisture, also affects the SAR
                      signal. Thus, surface-soil permittivity or moisture has to
                      be taken into account. This study tackles the issues related
                      to soil influence on the SAR signal in monitoring maize crop
                      growth. Different methods of assessing surface-soil moisture
                      or permittivity are explored in order to retrieve LAI values
                      from SAR data. The first method is based on a hydrological
                      model-the soil, water, atmosphere, and plant (SWAP)
                      model-with which the surface-soil moisture level can be
                      estimated as a function of time. This method is tested with
                      two kinds of meteorological data as inputs for the
                      hydrological model: ground meteorological data and estimated
                      meteorological data. The second method resorts to
                      ground-penetrating radar, an alternative means of estimating
                      surface-soil permittivity. This study demonstrates that both
                      soil-moisture levels estimated by the SWAP model and soil
                      permittivity measured by ground-penetrating radar can be
                      successfully used for retrieving maize LAI values from SAR
                      data using the water cloud model.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Remote Sensing},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000305249300014},
      url          = {https://juser.fz-juelich.de/record/19606},
}