Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals

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Title:Main Title: Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals
Description:Abstract: Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.
Identifier:10.5194/isprsarchives-XLI-B8-959-2016 (DOI)
Responsible Party
Creators:Ulrike Lussem (Author), Christoph Hütt (Author), Guido Waldhoff (Author)
Publisher:ISPRS
Publication Year:2018
Topic
TR32 Topic:Land Use
Related Subproject:Z1
Subjects:Keywords: Agriculture, Classification, Land Use, Land Cover
Geogr. Information Topic:Environment
File Details
Filename:isprs_archives_XLI_B8_959_2016.pdf
Data Type:Text - Article
File Size:1.2 MB
Date:Available: 22.06.2016
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:[TR32DB] Data policy agreement
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Specific Information - Publication
Publication Status:Published
Review Status:Not peer reviewed
Publication Type:Article
Source:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Source Website:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/
Volume:XLI-B8
Number of Pages:5 (959 - 963)
Metadata Details
Metadata Creator:Ulrike Lussem
Metadata Created:22.08.2018
Metadata Last Updated:22.08.2018
Subproject:Z1
Funding Phase:3
Metadata Language:English
Metadata Version:V50
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