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@PHDTHESIS{Ali:843656,
      author       = {Ali, Muhammad},
      title        = {{S}patio-{T}emporal {E}stimation and {V}alidation of
                      {R}emotely {S}ensed {V}egetation and {H}ydrological {F}luxes
                      in the {R}ur {C}atchment, {G}ermany},
      volume       = {403},
      school       = {Universität Bonn},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2018-01225},
      isbn         = {978-3-95806-287-0},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {116 S.},
      year         = {2018},
      note         = {Universität Bonn, Diss., 2017},
      abstract     = {Operational availability of spatio-temporal vegetation and
                      hydrological estimates are becoming increasingly attractive
                      for hydrologic studies from local through regional and
                      global scales, especially in remote areas and ungauged
                      basins. More advancement and versatility in satellite-based
                      remotely sensed methods towards consistent and timely
                      information for monitoring regional scale vegetation and
                      hydrological fluxes may lead to efficient and unprecedented
                      planning and management of agricultural practices and water
                      resources. This thesis develops and analyses remote sensing
                      methods for regional scale vegetation and land surface water
                      fluxes estimation. Results from this study are validated at
                      various test sites in the Rur catchment, Germany. These
                      sites are equipped with sophisticated and state-of-the-art
                      instruments for monitoring vegetation and hydrological
                      fluxes. Second chapter in this thesis explains a direct
                      retrieval method and validation of the Leaf Area Index (LAI)
                      from time-series of multispectral RapidEye images. LAI,
                      quantifying the amount of leaf material, considered as an
                      important variable for numerous processes in hydrological
                      studies that link vegetation to climate. $\textit{In situ}$
                      LAI measuring methods have the limitation of being labor
                      intensive and site specific. Remote sensing LAI
                      (LAI$_{rapideye}$) were derived using different vegetation
                      indices, namely SAVI (Soil Adjusted Vegetation Index) and
                      NDVI (Normalized Difference Vegetation Index). Additionally,
                      applicability of the newly available red-edge band (RE) was
                      also analyzed through Normalized Difference Red-Edge index
                      (NDRE) and Soil Adjusted Red-Edge index (SARE). The
                      LAI$_{rapideye}$ obtained from vegetation indices with
                      red-edge band showed better correlation with destructive
                      LAI$_{destr}$ (r = 0.88 and Root Mean Square Deviation, RMSD
                      = 1.01 \& 0.92) than LAI from vegetation indices without
                      red-edge band. This study also investigated the need to
                      apply relative and absolute atmospheric correction methods
                      to the time-series of RapidEye Level 3A data prior to LAI
                      estimation. Analysis of the RapidEye data set showed that
                      application of the atmospheric corrections did not improve
                      correlation of the estimated LAI with in situ LAI, because
                      RapidEye Level 3A data are provided with simplified
                      atmospheric corrections and the vegetation indices used for
                      LAI retrieval ware already normalized. Third chapter
                      investigates estimation of spatio-temporal latent heat using
                      an energy balance approach and simplified regression between
                      calculated latent heat (from energy balance) and downward
                      shortwave radiation data from the Spinning Enhanced Visible
                      and Infrared Imager (SEVIRI) onboard Meteosat Second
                      Generation (MSG) Satellites. Mapping the spatio-temporal
                      [...]},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2018030704},
      url          = {https://juser.fz-juelich.de/record/843656},
}