Crop type mapping based on Sentinel-1 backscatter time series
| dc.contributor.author | Arias Cuenca, María | |
| dc.contributor.author | Campo-Bescós, Miguel | |
| dc.contributor.author | Álvarez-Mozos, Jesús | |
| dc.contributor.department | Ingeniería | es_ES |
| dc.contributor.department | Ingeniaritza | eu |
| dc.date.accessioned | 2021-04-20T11:23:36Z | |
| dc.date.available | 2021-04-20T11:23:36Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ground truth was available, were processed. The classification approach was based on the temporal signatures obtained for each polarization channel (VH, VV and the cross-pol ratio) for the different crops. Highest accuracies were obtained when fields were assigned to the class that minimized the RMSE, with an overall accuracy of 79% and best results for rapeseed, sunflower, alfalfa and barley. | en |
| dc.description.sponsorship | This work was partly funded by project CGL2016-75217-R (MINECO/FEDER, EU) and project PyrenEOS EFA 048/15, the latter has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France Andorra programme POCTEFA 2014-2020). | en |
| dc.format.extent | 5 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | M. Arias, M. A. Campo-Bescós and J. Álvarez-Mozos, 'Crop Type Mapping Based on Sentinel-1 Backscatter Time Series,' IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 6623-6626, doi: 10.1109/IGARSS.2018.8519005. | en |
| dc.identifier.doi | 10.1109/IGARSS.2018.8519005 | |
| dc.identifier.isbn | 978-1-5386-7151-1 | |
| dc.identifier.issn | 2153-7003 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/39564 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | 2019 IEEE International Geoscience & Remote Sensing Symposium (IGARSS): proceedings. July 22–27, 2018, Valencia, Spain | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/CGL2016-75217-R/ | |
| dc.relation.publisherversion | https://doi.org/10.1109/IGARSS.2018.8519005 | |
| dc.rights | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Crop type mapping | en |
| dc.subject | Supervised classification | en |
| dc.subject | Sentinel-1 | en |
| dc.subject | Time series | en |
| dc.title | Crop type mapping based on Sentinel-1 backscatter time series | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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