Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata
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| Title: | Main Title: Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata |
| Description: | 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: | 10.1080/22797254.2017.1401909 (DOI) |
| 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
| Creators: | Christoph Hütt (Author), Guido Waldhoff (Author) |
| Funding Reference: | Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation |
| Publisher: | Taylor & Francis |
| Publication Year: | 2018 |
Topic
| TR32 Topic: | Land Use |
| Related Subproject: | Z1 |
| Subjects: | 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
| Filename: | huettwaldhoff2018mdaforcropswithterrasarxandofficialgeodata.pdf |
| Data Type: | Text - Article |
| File Size: | 3.3 MB |
| Dates: | Available: 01.12.2017 (online) Accepted: 02.11.2017 Submitted: 10.02.2017 |
| Mime Type: | application/pdf |
| Data Format: | |
| 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
Specific Information - Publication
| Publication Status: | Accepted |
| Review Status: | Peer reviewed |
| Publication Type: | Article |
| Article Type: | Journal |
| Source: | European Journal of Remote Sensing |
| Source Website: | https://tandfonline.com/loi/tejr20 |
| Issue: | 1 |
| Volume: | 51 |
| Number of Pages: | 13 (62 - 74) |
Metadata Details
| Metadata Creator: | Christoph Hütt |
| Metadata Created: | 27.08.2018 |
| Metadata Last Updated: | 27.08.2018 |
| Subproject: | Z1 |
| Funding Phase: | 3 |
| Metadata Language: | English |
| Metadata Version: | V50 |
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Dataset Statistics
| Page Visits: | 631 |
| Metadata Downloads: | 0 |
| Dataset Downloads: | 13 |
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