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@ARTICLE{Fawcett:873764,
      author       = {Fawcett and Panigada and Tagliabue and Boschetti and
                      Celesti and Evdokimov and Biriukova and Colombo and
                      Miglietta and Rascher, Uwe and Anderson},
      title        = {{M}ulti-{S}cale {E}valuation of {D}rone-{B}ased
                      {M}ultispectral {S}urface {R}eflectance and {V}egetation
                      {I}ndices in {O}perational {C}onditions},
      journal      = {Remote sensing},
      volume       = {12},
      number       = {3},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2020-00978},
      pages        = {514 -},
      year         = {2020},
      abstract     = {Compact multi-spectral sensors that can be mounted on
                      lightweight drones are now widely available and applied
                      within the geo- and environmental sciences. However; the
                      spatial consistency and radiometric quality of data from
                      such sensors is relatively poorly explored beyond the lab;
                      in operational settings and against other sensors. This
                      study explores the extent to which accurate
                      hemispherical-conical reflectance factors (HCRF) and
                      vegetation indices (specifically: normalised difference
                      vegetation index (NDVI) and chlorophyll red-edge index
                      (CHL)) can be derived from a low-cost multispectral
                      drone-mounted sensor (Parrot Sequoia). The drone datasets
                      were assessed using reference panels and a high quality 1 m
                      resolution reference dataset collected near-simultaneously
                      by an airborne imaging spectrometer (HyPlant). Relative
                      errors relating to the radiometric calibration to HCRF
                      values were in the 4 to $15\%$ range whereas deviations
                      assessed for a maize field case study were larger (5 to
                      $28\%).$ Drone-derived vegetation indices showed relatively
                      good agreement for NDVI with both HyPlant and Sentinel 2
                      products (R2 = 0.91). The HCRF; NDVI and CHL products from
                      the Sequoia showed bias for high and low reflective
                      surfaces. The spatial consistency of the products was high
                      with minimal view angle effects in visible bands. In
                      summary; compact multi-spectral sensors such as the Parrot
                      Sequoia show good potential for use in index-based
                      vegetation monitoring studies across scales but care must be
                      taken when assuming derived HCRF to represent the true
                      optical properties of the imaged surface.},
      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:000515393800173},
      doi          = {10.3390/rs12030514},
      url          = {https://juser.fz-juelich.de/record/873764},
}