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@ARTICLE{Tewes:888276,
author = {Tewes, Andreas and Montzka, Carsten and Nolte, Manuel and
Krauss, Gunther and Hoffmann, Holger and Gaiser, Thomas},
title = {{A}ssimilation of {S}entinel-2 {E}stimated {LAI} into a
{C}rop {M}odel: {I}nfluence of {T}iming and {F}requency of
{A}cquisitions on {S}imulation of {W}ater {S}tress and
{B}iomass {P}roduction of {W}inter {W}heat},
journal = {Agronomy},
volume = {10},
number = {11},
issn = {2073-4395},
address = {Basel},
publisher = {MDPI},
reportid = {FZJ-2020-04802},
pages = {1813 -},
year = {2020},
abstract = {The Sentinel-2 (S2) Toolbox permits for the automated
retrieval of leaf area index (LAI). LAI assimilation into
crop simulation models could aid to improve the prediction
accuracy for biomass at field level. We investigated if the
combined effects of assimilation date and corresponding
growth stage plus observational frequency have an impact on
the crop model-based simulation of water stress and biomass
production. We simulated winter wheat growth in nine fields
in Germany over two years. S2 LAI estimations for each field
were categorized into three phases, depending on the
development stage of the crop at acquisition date
(tillering, stem elongation, booting to flowering). LAI was
assimilated in every possible combinational setup using the
ensemble Kalman filter (EnKF). We evaluated the performance
of the simulations based on the comparison of measured and
simulated aboveground biomass at harvest. The results showed
that the effects on water stress remained largely limited,
because it mostly occurred after we stopped LAI
assimilation. With regard to aboveground biomass, we found
that the assimilation of only one LAI estimate from either
the tillering or the booting to flowering stage resulted in
simulated biomass values similar or closer to measured
values than in those where more than one LAI estimate from
the stem elongation phase were assimilated. LAI assimilation
after the tillering phase might therefore be not necessarily
required, as it may not lead to the desired improvement
effect},
cin = {IBG-3},
ddc = {640},
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)16},
UT = {WOS:000592966600001},
doi = {10.3390/agronomy10111813},
url = {https://juser.fz-juelich.de/record/888276},
}