An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE)
One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In additi...
| Autores principales: | , , , , , , , |
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| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2466 https://doi.org/10.3390/agronomy14030557 |
| _version_ | 1855028441703776256 |
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| author | Medina Medina, Angel James Salas López, Rolando Zabaleta Santisteban, Jhon Antony Tuesta Trauco, Katerin Meliza Turpo Cayo, Efrain Yury Huaman Haro, Nixon Oliva Cruz, Manuel Gómez Fernández, Darwin |
| author_browse | Gómez Fernández, Darwin Huaman Haro, Nixon Medina Medina, Angel James Oliva Cruz, Manuel Salas López, Rolando Tuesta Trauco, Katerin Meliza Turpo Cayo, Efrain Yury Zabaleta Santisteban, Jhon Antony |
| author_facet | Medina Medina, Angel James Salas López, Rolando Zabaleta Santisteban, Jhon Antony Tuesta Trauco, Katerin Meliza Turpo Cayo, Efrain Yury Huaman Haro, Nixon Oliva Cruz, Manuel Gómez Fernández, Darwin |
| author_sort | Medina Medina, Angel James |
| collection | Repositorio INIA |
| description | One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management. |
| format | info:eu-repo/semantics/article |
| id | INIA2466 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA24662024-04-02T17:01:37Z An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) Medina Medina, Angel James Salas López, Rolando Zabaleta Santisteban, Jhon Antony Tuesta Trauco, Katerin Meliza Turpo Cayo, Efrain Yury Huaman Haro, Nixon Oliva Cruz, Manuel Gómez Fernández, Darwin SAR Rice Monitoring Changes https://purl.org/pe-repo/ocde/ford#4.01.06 SAR (radar) Radar de abertura sintética Monitoring Vigilancia Oryza sativa Rice Arroz One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management. 2024-04-02T17:01:35Z 2024-04-02T17:01:35Z 2024-03-08 info:eu-repo/semantics/article Medina, A.; Salas, R.; Zabaleta, J.; Tuesta, K.; Turpo, E.; Huaman, N.; Oliva, M.; & Gómez, D. (2024). An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE). Agronomy, 14(3), 557. doi: 10.3390/agronomy14030557 2073-4395 https://hdl.handle.net/20.500.12955/2466 https://doi.org/10.3390/agronomy14030557 eng urn:issn:2073-4395 Agronomy info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | SAR Rice Monitoring Changes https://purl.org/pe-repo/ocde/ford#4.01.06 SAR (radar) Radar de abertura sintética Monitoring Vigilancia Oryza sativa Rice Arroz Medina Medina, Angel James Salas López, Rolando Zabaleta Santisteban, Jhon Antony Tuesta Trauco, Katerin Meliza Turpo Cayo, Efrain Yury Huaman Haro, Nixon Oliva Cruz, Manuel Gómez Fernández, Darwin An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title | An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title_full | An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title_fullStr | An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title_full_unstemmed | An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title_short | An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) |
| title_sort | analysis of the rice cultivation dynamics in the lower utcubamba river basin using sar and optical imagery in google earth engine gee |
| topic | SAR Rice Monitoring Changes https://purl.org/pe-repo/ocde/ford#4.01.06 SAR (radar) Radar de abertura sintética Monitoring Vigilancia Oryza sativa Rice Arroz |
| url | https://hdl.handle.net/20.500.12955/2466 https://doi.org/10.3390/agronomy14030557 |
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