Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Sy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/2/243 |
| _version_ | 1797412353802764288 |
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| author | Amal Chakhar David Hernández-López Rocío Ballesteros Miguel A. Moreno |
| author_facet | Amal Chakhar David Hernández-López Rocío Ballesteros Miguel A. Moreno |
| author_sort | Amal Chakhar |
| collection | DOAJ |
| description | The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested. |
| first_indexed | 2024-03-09T05:01:53Z |
| format | Article |
| id | doaj.art-3b33424d7b9943dcb9a3db3748b4f519 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:01:53Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3b33424d7b9943dcb9a3db3748b4f5192023-12-03T12:59:16ZengMDPI AGRemote Sensing2072-42922021-01-0113224310.3390/rs13020243Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 DataAmal Chakhar0David Hernández-López1Rocío Ballesteros2Miguel A. Moreno3Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, SpainInstitute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, SpainInstitute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, SpainInstitute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, SpainThe availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.https://www.mdpi.com/2072-4292/13/2/243crop classificationSentinel-1Sentinel-2NDVISARoptical |
| spellingShingle | Amal Chakhar David Hernández-López Rocío Ballesteros Miguel A. Moreno Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data Remote Sensing crop classification Sentinel-1 Sentinel-2 NDVI SAR optical |
| title | Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data |
| title_full | Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data |
| title_fullStr | Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data |
| title_full_unstemmed | Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data |
| title_short | Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data |
| title_sort | improving the accuracy of multiple algorithms for crop classification by integrating sentinel 1 observations with sentinel 2 data |
| topic | crop classification Sentinel-1 Sentinel-2 NDVI SAR optical |
| url | https://www.mdpi.com/2072-4292/13/2/243 |
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