Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices
The Valencian agricultural landscape is made up of a multitude of small-sized plots. Most remote sensing research has been focused on the discrimination of the large-scale land covers or uses, however, in Spain, crop recognition at plot level is crucial to develop management policies at different sc...
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| Format: | conferenceObject |
| Language: | Inglés |
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Wageningen Academic Publishers
2023
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| Online Access: | https://hdl.handle.net/20.500.11939/8682 https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-947-3_121 |
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| author | Moltó, Enrique Izquierdo-Sanz, Héctor |
| author_browse | Izquierdo-Sanz, Héctor Moltó, Enrique |
| author_facet | Moltó, Enrique Izquierdo-Sanz, Héctor |
| author_sort | Moltó, Enrique |
| collection | ReDivia |
| description | The Valencian agricultural landscape is made up of a multitude of small-sized plots. Most remote sensing research has been focused on the discrimination of the large-scale land covers or uses, however, in Spain, crop recognition at plot level is crucial to develop management policies at different scales. This work describes preliminary work for constructing and validating a classification model for automatic identification of crops in an agricultural zone where different species of fruit trees are cultivated. The model is based on the analysis of time series of two spectral indices obtained from Sentinel-2 images and using a Fourier decomposition approach. A two-step classification procedure is proposed: a random forest classifier at pixel level, followed by a plot classification. The overall accuracy achieved was 84.18% and the Kappa index was 0.71. |
| format | conferenceObject |
| id | ReDivia8682 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Wageningen Academic Publishers |
| publisherStr | Wageningen Academic Publishers |
| record_format | dspace |
| spelling | ReDivia86822025-04-25T14:50:51Z Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices Moltó, Enrique Izquierdo-Sanz, Héctor Sentinel-2 Spectral index Time series Fourier decomposition Supervised classification E90 Agrarian structure N01 Agricultural engineering U10 Mathematical and statistical methods The Valencian agricultural landscape is made up of a multitude of small-sized plots. Most remote sensing research has been focused on the discrimination of the large-scale land covers or uses, however, in Spain, crop recognition at plot level is crucial to develop management policies at different scales. This work describes preliminary work for constructing and validating a classification model for automatic identification of crops in an agricultural zone where different species of fruit trees are cultivated. The model is based on the analysis of time series of two spectral indices obtained from Sentinel-2 images and using a Fourier decomposition approach. A two-step classification procedure is proposed: a random forest classifier at pixel level, followed by a plot classification. The overall accuracy achieved was 84.18% and the Kappa index was 0.71. 2023-07-25T13:44:34Z 2023-07-25T13:44:34Z 2023 conferenceObject Moltó, E., & Izquierdo-Sanz, H. (2023). Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices. Precision agriculture’23, pp. 963-969. https://hdl.handle.net/20.500.11939/8682 10.3920/978-90-8686-947-3_121 https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-947-3_121 en 2023-07 Precision Agriculture '23 Bologna info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CV Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Wageningen Academic Publishers electronico |
| spellingShingle | Sentinel-2 Spectral index Time series Fourier decomposition Supervised classification E90 Agrarian structure N01 Agricultural engineering U10 Mathematical and statistical methods Moltó, Enrique Izquierdo-Sanz, Héctor Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title | Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title_full | Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title_fullStr | Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title_full_unstemmed | Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title_short | Crop recognition at orchard level in Mediterranean conditions using time series of Sentinel-2 spectral indices |
| title_sort | crop recognition at orchard level in mediterranean conditions using time series of sentinel 2 spectral indices |
| topic | Sentinel-2 Spectral index Time series Fourier decomposition Supervised classification E90 Agrarian structure N01 Agricultural engineering U10 Mathematical and statistical methods |
| url | https://hdl.handle.net/20.500.11939/8682 https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-947-3_121 |
| work_keys_str_mv | AT moltoenrique croprecognitionatorchardlevelinmediterraneanconditionsusingtimeseriesofsentinel2spectralindices AT izquierdosanzhector croprecognitionatorchardlevelinmediterraneanconditionsusingtimeseriesofsentinel2spectralindices |