Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

dc.contributor.authorHoltgrave, Ann-Kathrin
dc.contributor.authorRöder, Norbert
dc.contributor.authorAckermann, Andrea
dc.contributor.authorErasmi, Stefan
dc.contributor.authorKleinschmit, Birgit
dc.date.accessioned2020-11-05T11:38:29Z
dc.date.available2020-11-05T11:38:29Z
dc.date.issued2020-09-09
dc.date.updated2020-10-08T00:15:52Z
dc.description.abstractAgricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.en
dc.identifier.eissn2072-4292
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11838
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10728
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherSAR remote sensingen
dc.subject.otheroptical remote sensingen
dc.subject.otherLower Saxonyen
dc.subject.otherphenological developmenten
dc.subject.othervegetation indicesen
dc.subject.otheragricultureen
dc.titleComparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoringen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber2919en
dcterms.bibliographicCitation.doi10.3390/rs12182919en
dcterms.bibliographicCitation.issue18en
dcterms.bibliographicCitation.journaltitleRemote Sensingen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume12en
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Landschaftsarchitektur und Umweltplanung::FG Geoinformation in der Umweltplanungde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG Geoinformation in der Umweltplanungde
tub.affiliation.instituteInst. Landschaftsarchitektur und Umweltplanungde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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