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@ARTICLE{Ali:188562,
      author       = {Ali, Muhammed and Montzka, Carsten and Stadler, Anja and
                      Menz, Gunter and Thonfeld, Frank and Vereecken, Harry},
      title        = {{E}stimation and {V}alidation of {R}apid{E}ye-{B}ased
                      {T}ime-{S}eries of {L}eaf {A}rea {I}ndex for {W}inter
                      {W}heat in the {R}ur {C}atchment ({G}ermany)},
      journal      = {Remote sensing},
      volume       = {7},
      number       = {3},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2015-01917},
      pages        = {2808-2831},
      year         = {2015},
      abstract     = {Leaf Area Index (LAI) is an important variable for numerous
                      processes in various disciplines of bio- and geosciences. In
                      situ measurements are the most accurate source of LAI among
                      the LAI measuring methods, but the in situ measurements have
                      the limitation of being labor intensive and site specific.
                      For spatial-explicit applications (from regional to
                      continental scales), satellite remote sensing is a promising
                      source for obtaining LAI with different spatial resolutions.
                      However, satellite-derived LAI measurements using empirical
                      models require calibration and validation with the in situ
                      measurements. In this study, we attempted to validate a
                      direct LAI retrieval method from remotely sensed images
                      (RapidEye) with in situ LAI (LAIdestr). Remote sensing LAI
                      (LAIrapideye) 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
                      LAIrapideye obtained from vegetation indices with red-edge
                      band showed better correlation with LAIdestr (r = 0.88 and
                      Root Mean Square Devation, RMSD = 1.01 $\&$ 0.92). This
                      study also investigated the need to apply
                      radiometric/atmospheric correction methods to the
                      time-series of RapidEye Level 3A data prior to LAI
                      estimation. Analysis of the the RapidEye Level 3A data set
                      showed that application of the radiometric/atmospheric
                      correction did not improve correlation of the estimated LAI
                      with in situ LAI.},
      cin          = {IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / 255 - Terrestrial Systems: From Observation to
                      Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000353685200023},
      doi          = {10.3390/rs70302808},
      url          = {https://juser.fz-juelich.de/record/188562},
}