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000874488 1001_ $$0P:(DE-Juel1)180165$$aTewes, Andreas$$b0$$eCorresponding author
000874488 245__ $$aHow Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?
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000874488 500__ $$aGrant Agrarsysteme der Zukunft: DAKIS —Digitales Wissens- und Informationssystem für die Landwirtschaft, Teilprojekt FFörderkennzeichen: 031B0729F
000874488 520__ $$aThe combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.
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000874488 7001_ $$00000-0001-9811-5065$$aHoffmann, Holger$$b1
000874488 7001_ $$0P:(DE-HGF)0$$aNolte, Manuel$$b2
000874488 7001_ $$0P:(DE-HGF)0$$aKrauss, Gunther$$b3
000874488 7001_ $$0P:(DE-HGF)0$$aSchäfer, Fabian$$b4
000874488 7001_ $$0P:(DE-HGF)0$$aKerkhoff, Christian$$b5
000874488 7001_ $$00000-0002-5820-2364$$aGaiser, Thomas$$b6
000874488 773__ $$0PERI:(DE-600)2513863-7$$a10.3390/rs12060925$$gVol. 12, no. 6, p. 925 -$$n6$$p925 -$$tRemote sensing$$v12$$x2072-4292$$y2020
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