[1816] - 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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Citation
Lussem, U., Hütt, C., Waldhoff, G., 2016. Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 959 - 963. DOI: 10.5194/isprsarchives-XLI-B8-959-2016.
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Identification
Title(s):Main Title: Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals
Description(s):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(s):DOI: 10.5194/isprsarchives-XLI-B8-959-2016
Responsible Party
Creator(s):Author: Ulrike Lussem
Author: Christoph Hütt
Author: Guido Waldhoff
Publisher:ISPRS
Topic
TR32 Topic:Land Use
Related Sub-project(s):Z1
Subject(s):CRC/TR32 Keywords: Agriculture, Classification, Land Use, Land Cover
Topic Category:Environment
File Details
File Name:isprs_archives_XLI_B8_959_2016.pdf
Data Type:Text
File Size:1246 kB (1.217 MB)
Date(s):Available: 2016-06-22
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
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Publication
Status:Published
Review:NoPeerReview
Year:2016
Type:Article
Source:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume:XLI-B8
Page Range:959 - 963
Metadata Details
Metadata Creator:Ulrike Lussem
Metadata Created:2018-08-22
Metadata Last Updated:2018-08-22
Subproject:Z1
Funding Phase:3
Metadata Language:English
Metadata Version:V43
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