Chlorophyll-a (Chl-a) concentration is a key climate variable, as its variability is associated with meteorological and oceanographic processes. This study analyzed 25 years (1998–2022) of Chl-a data from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI) multisensor archive for the South Brazil Bight, Southwestern Atlantic. Temporal variability and trends were assessed using the Census X11 method, Mann-Kendall, and Sens’ slope tests. The ESA OC-CCI data highlight reliable regional performance, although Chl-a concentrations above 10 mg.m⁻³ were underestimated. Temporal analyses showed the lowest Chl-a variability (29%) in open ocean waters, with 81% of the variability attributed to seasonal dynamics influenced by the South Atlantic Subtropical Gyre (SASG). A negative Chl-a trend of −11.0% was observed over the 25-year period, attributed to the expansion of the oligotrophic area of the SASG. In the shelf areas southwest of São Sebastião Island, Chl-a variability was moderate (34%–39%), with no discernible long-term trend, but significant interannual variability (44%). The Cape Frio upwelling region shows an increasing Chl-a trend (14.5% in the last 25 years), driven by atmospheric circulation affecting local winds. The highest Chl-a variability (74%) occurred along the southern continental shelf, associated with seasonal nutrient inputs from the Subtropical Shelf Front, with a Chla-a trend increase of 7.5% in 25 years. These results highlight the dynamic and variable Chl-a responses to environmental forcing across the South Brazil Bight.

The present study evaluates the influence of industrialization on suspended particulate matter (SPM) dynamics along the northern coast of Rio de Janeiro, focusing specifically on the Açu Port Industrial Complex (APIC). A 20-year MODIS-Aqua (1 km) dataset (2002–2022) was processed using the OC-SMART atmospheric correction. For SPM estimation, a retrieval approach for coastal turbid waters that integrates two optimized bio-optical algorithms based on Optical Water Types (OWTs) was developed. The validity of this approach was substantiated through the utilization of the GLORIA in situ dataset and satellite matchups, which demonstrated its robust performance across a range of turbidity conditions. Its main innovation lies in the OWT-based fusion of two optimized SPM models, enabling robust retrievals across diverse coastal optical conditions. Statistical analyses based on Census X11 decomposition and the Seasonal Mann–Kendall test revealed strong spatial and temporal variability, with SPM concentrations increasing by up to 60% near the APIC during the study period, coinciding with dredging, port expansion, and sediment disposal. These findings indicate a pronounced anthropogenic signal, while spatial and temporal correlation analyses demonstrated that sediment dispersion is consistently directed northward, primarily controlled by currents and wind forcing. The results indicate that industrial activities augment the supply of sediments, while natural hydrodynamic processes govern their dispersion and transport, emphasizing the impact of human pressures and physical drivers on coastal sediments.

Chlorophyll-a concentration (Chl-a) satellite retrieval is an important proxy to assess the ocean trophic state and marine ecosystem responses to climate variability. However, uncertainties in water-leaving remote sensing reflectance spectra (Rrs()) retrieved from Ocean Color (OC) satellite sensors may affect Chl-a products depending on sensor performance, processing scheme, and environmental conditions. Chl-a algorithms also have their own uncertainty, particularly related to optical water type (OWT), phytoplankton composition, and physiology. Here, we compared simultaneous in-situ Rrs() and Chl-a measurements in the South Brazil Bight (SBB) with collocated satellite products from MODIS-Aqua, VIIRS-JPSS-1/SNPP, and OLCI–S3A/B, using empirical (OCx) and semi-analytical (GIOP) algorithms. All sensors showed similar Rrs() performance, with an overall negative bias, particularly for shorter wavelengths. These differences were minimized by maximum band ratios, especially for MODIS-Aqua (RPD = 16%; R2 = 0.90). For Chl-a, MODIS-Aqua showed the highest correlation using OCx (R2 = 0.95), while OLCI-OC4ME presented the lowest mean bias (0.13 mg·m-3) and RMSE (0.6 mg·m-3). In inner shelf waters, empirical algorithms mostly overestimated Chl-a, whereas VIIRS-GIOP provided the best performance (R2 = 0.78, Bias = 0.09 mg·m-3, RMSE = 0.2 mg·m-3). Overall, the three sensors provided reliable Rrs and Chl-a data across inner shelf and open-ocean waters of the SBB. Empirical OCx algorithms using MODIS-Aqua or VIIRS are suitable for spatiotemporal analyses of Chl-a patterns, whereas VIIRS-GIOP, followed by OLCI-OCx, is preferable for quantitative analyses, particularly in optically complex inner shelf waters. These findings support the continuity of long-term OC observations beyond MODIS-Aqua and their application to monitoring bio-optical and biogeochemical variability in the SBB.

Coastal ecosystems play essential ecological, social, and economic roles at the land-ocean interface and are highly sensitive to environmental changes, whether natural or human-induced. Monitoring these regions is crucial for developing sustainable, ecosystem-based environmental policies (UNESCO-IOC). Brazil, with approximately 8,000 kilometers of tropical and subtropical coastline, hosts diverse coastal and marine ecosystems. However, these areas face significant pressures, including climate change, urban expansion, intensive agriculture, and deforestation. Despite their importance, Brazil, like many coastal regions, lacks extensive, long-term in situ data to assess how these pressures impact biogeochemical quality. Ocean color remote sensing helps fill this data gap by continuously providing key biogeochemical variables—such as phytoplankton biomass, particulate and dissolved matter, and associated organic carbon stocks—for over 25 years. This study demonstrates how optimized ocean color data can offer a comprehensive view of coastal ecosystems' responses to human pressures on both land and ocean. Specifically, MODIS ocean color time series have been analyzed to identify trends in the biogeochemical properties of Brazilian coastal and shelf waters, including estuaries, bays, and major lagoons, over the past two decades (COCOBRAZ ANR FAPESP project). This work illustrates how ocean color data can be a major source of information for supporting the preservation and management of coastal ecosystems.