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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},
}