Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018–2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.

Chlorophyll-a (Chl-a) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl-a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl-a. Our study indicates that a global algorithm, on average, underestimates in-situ Chl-a by ~59%, although underestimation was only observed for waters with Chl-a > 0.5 mg m⁻³. In high Chl-a waters (>1 mg m⁻³), Chl-a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl-a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl-a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce.

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.