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Crop type classification using a combination of optical and radar remote sensing data: a review

Orynbaikyzy, Aiym and Gessner, Ursula and Conrad, Christopher (2019) Crop type classification using a combination of optical and radar remote sensing data: a review. International Journal of Remote Sensing, 40 (17), pp. 6553-6595. Taylor & Francis. DOI: 10.1080/01431161.2019.1569791 ISSN 0143-1161

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Official URL: https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1569791

Abstract

Reliable and accurate crop classification maps are an important data source for agricultural monitoring and food security assessment studies. For many years, crop type classification and monitoring were focused on single-source optical satellite data classification. With advancements in sensor technologies and processing capabilities, the potential of multi-source satellite imagery has gained increasing attention. The combination of optical and radar data is particularly promising in the context of crop type classification as it allows explaining the advantages of both sensor types with respect to e.g. vegetation structure and biochemical properties. This review article gives a comprehensive overview of studies on crop type classification using optical and radar data fusion approaches. A structured review of fusion approaches, classification strategies and potential for mapping specific crop types is provided. Finally, the partially untapped potential of radaroptical fusion approaches, research gaps and challenges for upcoming future studies are highlighted and discussed.

Item URL in elib:https://elib.dlr.de/129210/
Document Type:Article
Title:Crop type classification using a combination of optical and radar remote sensing data: a review
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Orynbaikyzy, AiymAiym.Orynbaikyzy (at) dlr.deUNSPECIFIED
Gessner, Ursulaursula.gessner (at) dlr.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) geo.uni-halle.deUNSPECIFIED
Date:January 2019
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:40
DOI :10.1080/01431161.2019.1569791
Page Range:pp. 6553-6595
Publisher:Taylor & Francis
ISSN:0143-1161
Status:Published
Keywords:data fusion, optical data, SAR data, remote sensing, crop type classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Orynbaikyzy, Aiym
Deposited On:18 Sep 2019 10:04
Last Modified:18 Sep 2019 10:04

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