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Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

Ahmadian, Nima and Ullmann, Tobias and Verrelst, Jochem and Borg, Erik and Zölitz, Reinhard and Conrad, Christopher (2019) Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 87 (4), pp. 159-175. Springer. DOI: 10.1007/s41064-019-00076-x ISBN 2512-2789 ISSN 2512-2789

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Official URL: https://link.springer.com/content/pdf/10.1007%2Fs41064-019-00076-x.pdf

Abstract

The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.

Item URL in elib:https://elib.dlr.de/130836/
Document Type:Article
Title:Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ahmadian, Nimanima.ahmadian (at) uni-wuerzburg.deUNSPECIFIED
Ullmann, Tobiastobias.ullmann (at) uni-wuerzburg.deUNSPECIFIED
Verrelst, Jochemjochem.verrelst (at) uv.esUNSPECIFIED
Borg, ErikErik.Borg (at) dlr.dehttps://orcid.org/0000-0001-8288-8426
Zölitz, Reinhardzoelitz (at) uni-greifswald.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) geo.uni-halle.deUNSPECIFIED
Date:October 2019
Journal or Publication Title:PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:87
DOI :10.1007/s41064-019-00076-x
Page Range:pp. 159-175
Editors:
EditorsEmail
Kresse, WolfgangHSNB
Hinz, StefanKIT
Franz, RottensteinerLeibniz Universität Hannover
Christopher, ConradUniversität Würzburg
Jan-Henrik, HaunertUniversität Bonn
Publisher:Springer
Series Name:Springer PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
ISSN:2512-2789
ISBN:2512-2789
Status:Published
Keywords:TerraSAR-X · Agricultural crop · Biomass · Stepwise regression · Water cloud model (WCM) · Random Forest · DEMMIN
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 - Remote sensing and geoscience, R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
Deposited By: Borg, Dr.rer.nat. Erik
Deposited On:26 Nov 2019 12:30
Last Modified:26 Nov 2019 12:30

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