[1817] - Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Hütt, C., Waldhoff, G., 2018. Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata. European Journal of Remote Sensing, 51 (1), 62 - 74. DOI: 10.1080/22797254.2017.1401909.
Identification
Title(s):Main Title: Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata
Description(s):Abstract: Crop distribution information is essential for tackling some challenges associated with providing food for a growing global population. This information has been successfully compiled using the Multi-Data Approach (MDA). However, the current implementation of the approach is based on optical remote sensing, which fails to deliver the relevant information under cloudy conditions. We therefore extend the MDA by using Land Use/Land Cover classifications derived from six multitemporal and dual-polarimetric TerraSAR-X stripmap images, which do not require cloud-free conditions. These classifications were then combined with auxiliary, official geodata (ATKIS and Physical Blocks (PB)) data to lower misclassification and provide an enhanced LULC map that includes further information about the annual crop classification. These final classifications showed an overall accuracy (OA) of 75% for seven crop-classes (maize, sugar beet, barley, wheat, rye, rapeseed, and potato). For potatoes, however, classification does not appear to be as consistently accurate, as could be shown from repeated comparisons with variations of training and validation fields. When the rye, wheat, and barley classes were merged into a winter cereals class, the resultant five crop-class classifications had a high OA of about 90%.
Identifier(s):DOI: 10.1080/22797254.2017.1401909
Citation Advice:Christoph Hütt & Guido Waldhoff (2018) Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata, European Journal of Remote Sensing, 51:1, 62-74, DOI: 10.1080/22797254.2017.1401909
Responsible Party
Creator(s):Author: Christoph Hütt
Author: Guido Waldhoff
Funding Reference(s):Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation
Publisher:Taylor & Francis
Topic
TR32 Topic:Land Use
Related Sub-project(s):Z1
Subject(s):CRC/TR32 Keywords: Backscatter, Classification, Data Fusion, Geodata, Land Cover Mapping, Land Use, Land Cover, Multi-Temporal, Radar/X-Band, Remote Sensing, Remote Sensing Methods, TerraSAR-X
File Details
File Name:huettwaldhoff2018mdaforcropswithterrasarxandofficialgeodata.pdf
Data Type:Text
File Size:3356 kB (3.277 MB)
Date(s):Available: 2017-12-01 (online)
Date Accepted: 2017-11-02
Date Submitted: 2017-02-10
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
Download Information:https://www.tandfonline.com/doi/full/10.1080/22797254.2017.1401909
General Access and Use Conditions:© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Access Limitations:none
Licence:Creative Commons Attribution 4.0 International (CC BY 4.0)
Geographic
North:50.93859
East:6.59139
South:50.77561
West:6.37715
Measurement Region:Ellebach
Measurement Location:--Ellebach--
Specific Informations - Publication
Status:Accepted
Review:PeerReview
Year:2018
Type:Article
Article Type:Journal
Source:European Journal of Remote Sensing
Issue:1
Volume:51
Number Of Pages:12
Page Range:62 - 74
Metadata Details
Metadata Creator:Christoph Hütt
Metadata Created:2018-08-27
Metadata Last Updated:2018-08-27
Subproject:Z1
Funding Phase:3
Metadata Language:English
Metadata Version:V43
Metadata Export
Metadata Export:
Export XML
Dataset Metrics
Page Visits:74
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity
Features