| Texto completo | |
| Autor(es): |
Oliveira, Andrea De Lima
;
Rudorff, Natalia
;
Sathyendranath, Shubha
;
Cortivo, Fabio Dall
;
Rodrigues, Silvana Vianna
;
Sudatti, Daniela
;
Fernandes, Luciano Felicio
;
Brandini, Frederico
;
Kampel, Milton
Número total de Autores: 9
|
| Tipo de documento: | Artigo Científico |
| Fonte: | International Journal of Remote Sensing; v. 46, n. 22, p. 36-pg., 2025-10-27. |
| Resumo | |
Phytoplankton underpin marine food webs and carbon cycling, converting dissolved carbon dioxide into organic matter and exporting it to deeper layers. However, these organisms are sensitive to environmental changes that affect their growth and community structure differently, which may be represented by their taxonomic structure or cell size categories. Consequently, there is increasing interest in developing and improving satellite-based models for estimating the abundance of phytoplankton size classes (PSCs) and different taxonomic groups. Satellites can reliably estimate two key properties related to phytoplankton biomass and ocean dynamics: chlorophyll-a concentration (Chla), the primary pigment of phytoplankton, and sea surface temperature (SST), which is associated with water masses and often related to nutrient availability. In this study, we tested different approaches and developed regional models to retrieve PSCs from satellite data. The regional models were fitted to the South Brazil Bight (SBB) in the Southwestern Atlantic Ocean. The in situ training and validation datasets were obtained from oceanographic cruises conducted in the SBB during 2019-2022. We applied different model parameterisation schemes to compare SST-independent and SST-dependent models with both global and regional fits. The models were applied to both in situ data and satellite observations from Ocean and Land Colour Instrument (OLCI) sensors on board Sentinel 3A and 3B satellites, alongside the Multi-scale Ultra-high Resolution (MUR) SST product. The regional SST-dependent approach consistently outperformed alternatives across all size classes, achieving correlation coefficients (rho) greater than 0.7, bias less than 0.14, and mean absolute error (MAE) of less than 0.36. By comparison, the regional SST-independent approach (rho > 0.54, bias < 0.17, MAE < 0.38) and the global SST-dependent approach (rho > 0.59, bias < 0.11, and MAE < 0.40) showed weaker performance. These results highlight the importance of regional SST-dependent models for improving PSC estimation accuracy in the SBB and similar regions where SST variability affects nutrient availability, phytoplankton biomass, and community structure. (AU) | |
| Processo FAPESP: | 21/04128-8 - Caracterização da evolução da qualidade biogeoquímica das águas costeiras brasileiras nas últimas duas décadas a partir da observação de satélites: impacto de forçantes naturais e antrópicas |
| Beneficiário: | Milton Kampel |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |