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@ARTICLE{Heinemann:865974,
      author       = {Heinemann, Sascha and Siegmann, Bastian and Thonfeld, Frank
                      and Muro, Javier and Jedmowski, Christoph and Kemna, Andreas
                      and Kraska, Thorsten and Muller, Onno and Schultz, Johannes
                      and Udelhoven, Thomas and Wilke, Norman and Rascher, Uwe},
      title        = {{L}and {S}urface {T}emperature {R}etrieval for
                      {A}gricultural {A}reas {U}sing a {N}ovel {UAV} {P}latform
                      {E}quipped with a {T}hermal {I}nfrared and {M}ultispectral
                      {S}ensor},
      journal      = {Remote sensing},
      volume       = {12},
      number       = {7},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2019-05236},
      pages        = {1075 -},
      year         = {2020},
      abstract     = {Land surface temperature (LST) is a fundamental parameter
                      within the system of the Earth’s surface and atmosphere,
                      which can be used to describe the inherent physical
                      processes of energy and water exchange. The need for LST has
                      been increasingly recognised in agriculture, as it affects
                      the growth phases of crops and crop yields. However,
                      challenges in overcoming the large discrepancies between the
                      retrieved LST and ground truth data still exist. Precise LST
                      measurement depends mainly on accurately deriving the
                      surface emissivity, which is very dynamic due to changing
                      states of land cover and plant development. In this study,
                      we present an LST retrieval algorithm for the combined use
                      of multispectral optical and thermal UAV images, which has
                      been optimised for operational applications in agriculture
                      to map the heterogeneous and diverse agricultural crop
                      systems of a research campus in Germany (April 2018). We
                      constrain the emissivity using certain NDVI thresholds to
                      distinguish different land surface types. The algorithm
                      includes atmospheric corrections and environmental thermal
                      emissions to minimise the uncertainties. In the analysis, we
                      emphasise that the omission of crucial meteorological
                      parameters and inaccurately determined emissivities can lead
                      to a considerably underestimated LST; however, if the
                      emissivity is underestimated, the LST can be overestimated.
                      The retrieved LST is validated by reference temperatures
                      from nearby ponds and weather stations. The validation of
                      the thermal measurements indicates a mean absolute error of
                      about 0.5 K. The novelty of the dual sensor system is that
                      it simultaneously captures highly spatially resolved optical
                      and thermal images, in order to construct the precise LST
                      ortho-mosaics required to monitor plant diseases and drought
                      stress and validate airborne and satellite data.},
      cin          = {IBG-2},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000537709600025},
      doi          = {10.3390/rs12071075},
      url          = {https://juser.fz-juelich.de/record/865974},
}