Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping
Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR)...
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| Format: | Article |
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MDPI AG
2019-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/13/1518 |
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| author | Rubén Valcarce-Diñeiro Benjamín Arias-Pérez Juan M. Lopez-Sanchez Nilda Sánchez |
| author_facet | Rubén Valcarce-Diñeiro Benjamín Arias-Pérez Juan M. Lopez-Sanchez Nilda Sánchez |
| author_sort | Rubén Valcarce-Diñeiro |
| collection | DOAJ |
| description | Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km<sup>2</sup> in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%). |
| first_indexed | 2024-12-24T03:22:32Z |
| format | Article |
| id | doaj.art-1f833f102ef8419187dcf05b7ff6616f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:22:32Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1f833f102ef8419187dcf05b7ff6616f2022-12-21T17:17:26ZengMDPI AGRemote Sensing2072-42922019-06-011113151810.3390/rs11131518rs11131518Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type MappingRubén Valcarce-Diñeiro0Benjamín Arias-Pérez1Juan M. Lopez-Sanchez2Nilda Sánchez3Department of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros 50, 05003 Avila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros 50, 05003 Avila, SpainIUII, University of Alicante, P.O. Box 99, E-03080 Alicante, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros 50, 05003 Avila, SpainLand-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km<sup>2</sup> in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).https://www.mdpi.com/2072-4292/11/13/1518agricultureclassificationC5.0 algorithmmultitemporalpolarimetric SARRADARSAT-2Sentinel-1 |
| spellingShingle | Rubén Valcarce-Diñeiro Benjamín Arias-Pérez Juan M. Lopez-Sanchez Nilda Sánchez Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping Remote Sensing agriculture classification C5.0 algorithm multitemporal polarimetric SAR RADARSAT-2 Sentinel-1 |
| title | Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping |
| title_full | Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping |
| title_fullStr | Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping |
| title_full_unstemmed | Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping |
| title_short | Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping |
| title_sort | multi temporal dual and quad polarimetric synthetic aperture radar data for crop type mapping |
| topic | agriculture classification C5.0 algorithm multitemporal polarimetric SAR RADARSAT-2 Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/11/13/1518 |
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