Evaluating the Performance of the OC5 algorithm of IFREMER for the highly turbid waters of Río de la Plata
Remote sensing provides a global vision of the oceans; validation is, however, an essential previous step. IFREMER developed the empirical algorithm OC5 for highly turbid (or type 2) waters and it performed well for the northwestern European shelf. The aim of this study was to evaluate the performan...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/41988 |
| Acceso en línea: | http://hdl.handle.net/11336/41988 |
| Access Level: | acceso abierto |
| Palabra clave: | Validation Oc5 Algorithm Suspended particulate matter Chlorophyll-a Río De La Plata https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| Sumario: | Remote sensing provides a global vision of the oceans; validation is, however, an essential previous step. IFREMER developed the empirical algorithm OC5 for highly turbid (or type 2) waters and it performed well for the northwestern European shelf. The aim of this study was to evaluate the performance of this algorithm for the Río de la Plata estuary, utilizing in situ observations of chlorophyll-a and suspended matter. Our results show a low point-to-point correlation between in situ and remote observations for both variables. In addition, the root mean square log error (RMSE) exceeded 35% for both variables, indicating a poor performance of the OC5 algorithm. This might be related to the empirical nature of the algorithm, to the amount and distribution of the data used for the analysis, to the species that compose the phytoplankton of the region, to the presence of other optically active substances in the water, and to errors in the atmospheric corrections and/or to the spatial variability of the analyzed variables. In conclusion, our results confirm the need to develop regional algorithms which take into account the particular physical and biological characteristics of the area under study. |
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