[569] - Calibration and evaluation of a crop model using remote sensing data

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Stadler, A., 2011. Calibration and evaluation of a crop model using remote sensing data. PhD Report, Institute of Crop Science and Resource Conservation, INRES, University Bonn, Bonn, Germany. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=569 at 2019-08-17.
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Title(s):Main Title: Calibration and evaluation of a crop model using remote sensing data
Description(s):Abstract: The interactions between soil, vegetation and atmosphere form a very complex system, the individual components of which are generally well understood. Crop models such as GECROS (Yin & van Laar 2005) try to represent an image of this complex ecosystem by simplifying the ecosystem and its physiological processes to mathematical algorithms (Hay & Porter 2006). Measurements of these different processes are used to verify that the crop model’s results match with the real plant physiology (Biernath 2011). The most important measurements for evaluating GECROS are photosynthesis, transpiration, leaf area index, biomass production and phenological development. Ecosystems, including agricultural landscapes, are often very heterogeneous. Soil heterogeneity has a considerable effect on crop physiology so that crops do not just differ from site to site but also within a field. These heterogeneity patterns have rarely been analyzed. A crop model could help to get a better understanding of them, but for this purpose the model needs to be tested with respect to its ability to present an image of a heterogeneous crop system (Hu 2011). The model’s parameters are the key to making an already well working model reproduce the crops grown. There are several ways to obtain the parameters of a model (Wallach et al. 2006). Not only can one use, the “fitting by eye” method where parameter values are taken from literature and changed by experience, but it is also possible to use model inversion with measured data for finding the different parameter values. Remote sensing data is a third possibility; after having interpreted the remotely sensed data, it can be compared with measured ground data to derive the model parameters (Ahl 2004). Remotely sensed data can eventually be very helpful for the calibration and evaluation of crop models (Houborg 2011). It will probably not substitute field experiments and measurements, but nevertheless it supports the other data and can help to understand the interrelations between soil, vegetation, and atmosphere. Moreover, remote sensing data has one important advantage over point measurements: It collects the data of the physiological processes on the whole field. Field measurements are really only done at different sample points within a field so that it is never possible to validate how the physiological processes between the sample points are proceeding (Garbulsky 2011). As far as the up-scaling of a crop model from point to field level is concerned, remote sensing data could be very helpful in understanding whether the crop model represents the whole field in a realistic way, or whether it reproduces only single samples (Garrigues 2006, Garrigues 2008).
Responsible Party
Creator(s):Author: Anja Stadler
Publisher:CRC/TR32 Database (TR32DB)
TR32 Topic:Vegetation
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:Report1_Stadler_2011.pdf
Data Type:Text
File Size:369 kB (0.36 MB)
Date(s):Available: 2011-10-30
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
Measurement Region:Ellebach
Measurement Location:Selhausen
Specific Informations - Report
Report Date:30th of October, 2011
Report Type:PhD Report
Report City:Bonn, Germany
Report Institution:Institute of Crop Science and Resource Conservation, INRES, University Bonn
Number Of Pages:4
Period of Pages:1 - 4
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Anja Stadler
Metadata Created:2013-11-28
Metadata Last Updated:2013-11-28
Funding Phase:2
Metadata Language:English
Metadata Version:V40
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