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Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat

Dahms, Thorsten and Seissiger, Sylvia and Borg, Erik and Vajen, Hans-Hermann and Fichtelmann, Bernd and Conrad, Christopher (2016) Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat. Photogrammetrie Fernerkundung Geoinformation, 5-6, pp. 285-299. E. Schweizerbartsche Verlagsbuchhandlung. DOI: 10.1127/pfg/2016/0303 ISSN 1432-8364

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Abstract

High-resolution agricultural monitoring, e.g. the robust derivation of biophysical parameters through-out the cropping season and at subfield level, is gaining importance for agricultural management (pre-cision agriculture) and relies on high resolution remote sensing data (e.g. RapidEye or Sentinel-2). This data can then be utilized for regular mapping of biophysical parameters such as the fraction of absorbed photosynthetic active radiation (FPAR), the leaf area index (LAI) and the chlorophyll con-tent. Currently the development of methods for robust mapping of these biophysical parameters is matter of subject in research. At the same time, enormous data amounts will challenge processing capacities and a wise selection and reduction of data will improve the applicability of remote sensing in agriculture. Biophysical parameters were modelled with RapidEye data on winter wheat in Mecklenburg-West Pomerania, Germany, using Random Forest based on conditional inference trees. The study aims at the selection of the most important information out of spectral bands and indices for parameter predic-tion on winter wheat. In-situ and remote sensing observations were grouped into phenological phases in order to examine the importance of single spectral bands or indices for modelling biophysical reality in the several growing stages of winter wheat. Model accuracies for FPAR ranged between a coeffi-cient of determination of 0.19 and 0.83, showing, that the model accuracy is linked with the phenolog-ical phase. The results showed that for each biophysical parameter, different spectral variables become important for modelling and the number of important variables depends on the phenological time span. The prediction of biophysical parameters for short phenological groups, often depends only on one to three variables. The results also showed, that in the phenological phase of fruit development, the model accuracy is the lowest and the determination of the importance is more vague.

Item URL in elib:https://elib.dlr.de/111310/
Document Type:Article
Title:Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dahms, Thorstenthorsten.dahms (at) uni-wuerzburg.deUNSPECIFIED
Seissiger, Sylviasylvia.seissiger (at) web.deUNSPECIFIED
Borg, ErikErik.Borg (at) dlr.deUNSPECIFIED
Vajen, Hans-HermannHans-Hermann.Vajen (at) dlr.deUNSPECIFIED
Fichtelmann, BerndBernd.Fichtelmann (at) dlr.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Date:1 December 2016
Journal or Publication Title:Photogrammetrie Fernerkundung Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:5-6
DOI :10.1127/pfg/2016/0303
Page Range:pp. 285-299
Publisher:E. Schweizerbartsche Verlagsbuchhandlung
ISSN:1432-8364
Status:Published
Keywords:biophysical parameter; RapidEye; vegetation indices; winter wheat; phenology; conditional inference forest
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Neustrelitz , Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
German Remote Sensing Data Center > National Ground Segment
Deposited By: Wöhrl, Monika
Deposited On:09 Mar 2017 11:08
Last Modified:08 Mar 2018 18:48

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