000874693 001__ 874693
000874693 005__ 20220930130234.0
000874693 0247_ $$2doi$$a10.3390/agronomy10030446
000874693 0247_ $$2Handle$$a2128/24613
000874693 0247_ $$2altmetric$$aaltmetric:78349534
000874693 0247_ $$2WOS$$aWOS:000529377300132
000874693 037__ $$aFZJ-2020-01608
000874693 082__ $$a640
000874693 1001_ $$0P:(DE-Juel1)180165$$aTewes, Andreas$$b0$$eCorresponding author$$ufzj
000874693 245__ $$aNew Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations
000874693 260__ $$aBasel$$bMDPI$$c2020
000874693 3367_ $$2DRIVER$$aarticle
000874693 3367_ $$2DataCite$$aOutput Types/Journal article
000874693 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1585311796_27607
000874693 3367_ $$2BibTeX$$aARTICLE
000874693 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000874693 3367_ $$00$$2EndNote$$aJournal Article
000874693 520__ $$aThe assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.
000874693 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000874693 588__ $$aDataset connected to CrossRef
000874693 7001_ $$0P:(DE-HGF)0$$aHoffmann, Holger$$b1
000874693 7001_ $$0P:(DE-HGF)0$$aKrauss, Gunther$$b2
000874693 7001_ $$0P:(DE-HGF)0$$aSchäfer, Fabian$$b3
000874693 7001_ $$0P:(DE-HGF)0$$aKerkhoff, Christian$$b4
000874693 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b5
000874693 773__ $$0PERI:(DE-600)2607043-1$$a10.3390/agronomy10030446$$gVol. 10, no. 3, p. 446 -$$n3$$p446 -$$tAgronomy$$v10$$x2073-4395$$y2020
000874693 8564_ $$uhttps://juser.fz-juelich.de/record/874693/files/Invoice_agronomy-718883.pdf
000874693 8564_ $$uhttps://juser.fz-juelich.de/record/874693/files/Invoice_agronomy-718883.pdf?subformat=pdfa$$xpdfa
000874693 8564_ $$uhttps://juser.fz-juelich.de/record/874693/files/agronomy-10-00446-v2.pdf$$yOpenAccess
000874693 8564_ $$uhttps://juser.fz-juelich.de/record/874693/files/agronomy-10-00446-v2.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000874693 8767_ $$8agronomy-718883$$92020-03-21$$d2020-03-27$$eAPC$$jZahlung erfolgt$$pagronomy-718883$$zBelegnr. 1200151649, SAP Ausgleich 27.3.
000874693 909CO $$ooai:juser.fz-juelich.de:874693$$popenCost$$pVDB$$pVDB:Earth_Environment$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000874693 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180165$$aForschungszentrum Jülich$$b0$$kFZJ
000874693 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000874693 9141_ $$y2020
000874693 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000874693 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000874693 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bAGRONOMY-BASEL : 2017
000874693 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000874693 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000874693 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000874693 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000874693 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000874693 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000874693 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review
000874693 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences
000874693 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000874693 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000874693 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000874693 980__ $$ajournal
000874693 980__ $$aVDB
000874693 980__ $$aUNRESTRICTED
000874693 980__ $$aI:(DE-Juel1)IBG-3-20101118
000874693 980__ $$aAPC
000874693 9801_ $$aAPC
000874693 9801_ $$aFullTexts