Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data

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Title:Main Title: Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data
Description:Abstract: Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes.
Identifier:https://doi.org/10.3390/ijgi9020120 (DOI)
Related Resources:Describes Dataset https://www.tr32db.uni-koeln.de/data.php?dataID=1845 (URL)
Is Derived From Dataset https://www.tr32db.uni-koeln.de/data.php?dataID=1848 (URL)
Responsible Party
Creators:Christoph Hütt (Author), Guido Waldhoff (Author), Georg Bareth (Author)
Funding Reference:Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation
Publisher:Multidisciplinary Digital Publishing Institute
Publication Year:2020
Topic
TR32 Topic:Land Use
Related Subproject:Z1
Subjects:Keywords: Land Cover Mapping, SAR, Satellite Data, Remote Sensing Methods
Geogr. Information Topic:Geoscientific Information
File Details
Filename:ijgi-09-00120-v3.pdf
Data Type:Text - Article
File Size:23.7 MB
Date:Available: 21.02.2020
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
Download Information:This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
General Access and Use Conditions:This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Access Limitations:This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Licence:[Creative Commons] Attribution 4.0 International (CC BY 4.0)
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Specific Information - Publication
Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:ISPRS International Journal of Geoinformation
Source Website:https://www.mdpi.com/2220-9964/9/2/120/htm
Issue:2
Volume:9
Number of Pages:15 (1 - 15)
Metadata Details
Metadata Creator:Christoph Hütt
Metadata Created:12.06.2020
Metadata Last Updated:12.06.2020
Subproject:Z1
Funding Phase:3
Metadata Language:English
Metadata Version:V50
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Dataset Downloads:5
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