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Mapping annual crops in Portugal with Sentinel-2 data

2022-10-28Documento de conferência Acesso aberto
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dc.contributor.authorBenevides, Pedro
dc.contributor.authorCosta, Hugo
dc.contributor.authorMoreira, Francisco D.
dc.contributor.authorCaetano, Mário
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2022-11-03T22:17:46Z
dc.date.available2022-11-03T22:17:46Z
dc.date.issued2022-10-28
dc.descriptionBenevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2022). Mapping annual crops in Portugal with Sentinel-2 data. In C. M. U. Neale, & A. Maltese (Eds.), Proceedings of SPIE.Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV (Vol. 12262). SPIE. Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2636125---Funding Information: The work has been supported by projects foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. A special thanks to IFAP for ceding LPIS dataset of controlled parcels.
dc.description.abstractThis paper presents an annual crop classification exercise considering the entire area of continental Portugal for the 2020 agricultural year. The territory was divided into landscape units, i.e. areas of similar landscape characteristics for independent training and classification. Data from the Portuguese Land Parcel Identification System (LPIS) was used for training. Thirty-one annual crops were identified for classification. Supervised classification was undertaken using Random Forest. A time-series of Sentinel-2 images was gathered and prepared. Automatic processes were applied to auxiliary datasets to improve the training data quality and lower class mislabeling. Automatic random extraction was employed to derive a large amount of sampling units for each annual crop class in each landscape unit. An LPIS dataset of controlled parcels was used for results validation. An overall accuracy of 85% is obtained for the map at national level indicating that the methodology is useful to identify and characterize most of annual crop types in Portugal. Class aggregation of the annual crop types by two types of growing season, autumn/winter and spring/summer, resulted in large improvements in the accuracy of almost all annual crops, and an overall accuracy improvement of 2%. This experiment shows that LPIS dataset can be used for training a supervised classifier based on machine learning with high-resolution remote sensing optical data, to produce a reliable crop map at national level.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent706391
dc.identifier.doi10.1117/12.2636125
dc.identifier.isbn9781510655270
dc.identifier.isbn9781510655287
dc.identifier.otherPURE: 47542238
dc.identifier.otherPURE UUID: 2e53ab3d-f929-4cea-9271-ff8596f61eb4
dc.identifier.otherScopus: 85142863437
dc.identifier.otherWOS: 000891771700010
dc.identifier.urihttp://hdl.handle.net/10362/145227
dc.identifier.urlhttps://www.scopus.com/pages/publications/85142863437
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000891771700010
dc.identifier.urlhttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/12262/122620M/Mapping-annual-crops-in-Portugal-with-Sentinel-2-data/10.1117/12.2636125.short
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSociety of Photo-Optical Instrumentation Engineers
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FMOS%2F0046%2F2017/PT
dc.relationhttps://doi.org/10.54499/PCIF/SSI/0102/2017
dc.subjectAgriculture
dc.subjectCrop mapping
dc.subjectPortugal
dc.subjectSentinel-2
dc.subjectSupervised Classification
dc.subjectElectronic, Optical and Magnetic Materials
dc.subjectCondensed Matter Physics
dc.subjectComputer Science Applications
dc.subjectApplied Mathematics
dc.subjectElectrical and Electronic Engineering
dc.subjectSDG 15 - Life on Land
dc.titleMapping annual crops in Portugal with Sentinel-2 dataen
dc.typeconference object
degois.publication.titleProceedings of SPIE. Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV
degois.publication.titleRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV
degois.publication.volume12262
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardNumberPCIF/SSI/0102/2017
oaire.awardNumberPCIF/MOS/0046/2017
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FSSI%2F0102%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FMOS%2F0046%2F2017/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication7f731199-68cf-46d5-b72b-1ae7819b7e3a
relation.isProjectOfPublication42429afc-e9ee-44e2-8443-b8741ec75623
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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