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Remote Sens. 2015, 7(4), 3934-3965; doi:10.3390/rs70403934

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

1
Department of Geography, Ludwig-Maximilians-Universität Munich, Luisenstraße 37, 80333 Munich, Germany
2
VISTA Remote Sensing in Geosciences, Gabelsbergerstraße 51, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Yoshio Inoue and Prasad S. Thenkabail
Received: 30 September 2014 / Revised: 17 March 2015 / Accepted: 26 March 2015 / Published: 1 April 2015
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Abstract

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R² = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t∙ha−1). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management. View Full-Text
Keywords: precision agriculture; crop modeling; canopy reflectance modeling; data assimilation; winter wheat yield; SLC; PROMET precision agriculture; crop modeling; canopy reflectance modeling; data assimilation; winter wheat yield; SLC; PROMET
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hank, T.B.; Bach, H.; Mauser, W. Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe. Remote Sens. 2015, 7, 3934-3965.

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