Identification of musaceous crops in the cantons of Matina and Siquirres (Costa Rica) using rescaling of synthetic aperture radar (SAR) Sentinel 1A imagery
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| 作者: | |
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| 格式: | artículo original |
| 状态: | Versión publicada |
| Fecha de Publicación: | 2025 |
| 实物特征: | Vegetation indices derived from optical images are commonly used tools for crop mapping, but their effectiveness can be limited in regions with adverse weather conditions, such as areas with high cloudiness or frequent precipitation. Facing these limitations, measurements made with Synthetic Aperture Radar (SAR) emerge as a viable solution. SAR also has a long history of use for forest remote sensing where models of relationships between forest structural parameters and measured backscatter signatures are essential (Sinha et al., 2015). A notable example of this application is observed in the cantons of Siquirres and Matina cantons, in Costa Rica, where dual polarization SAR has been used to accurately estimate the extent of musaceous monocultures. Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The images were acquired in IW (Interferometric Wide-swath) mode on descending orbits, with a calibration/workflow process and a subsequent rescaling that allowed to draw a clear distinction between forested areas and monocrops more effectively than radar vegetation index based on cross-polarization. In fact, vertical/vertical polarization proved to be efficient for musaceous monocrops identification in the cantons of Matina and Siquirres (Costa Rica). |
| País: | Portal de Revistas UNA |
| 机构: | Universidad Nacional de Costa Rica |
| Repositorio: | Portal de Revistas UNA |
| 语言: | Español Inglés Portugués |
| OAI Identifier: | oai:ojs.www.una.ac.cr:article/21705 |
| 在线阅读: | https://www.revistas.una.ac.cr/index.php/geografica/article/view/21705 |
| Palabra clave: | Remote sensing Radar SAR forests crops Teledetección radar bosques cultivos Sensoriamento remoto florestas plantações |