The vertical diffuse attenuation coefficient $K_d$ ($\mathrm{PAR}$) is used for calculating the euphotic zone, the first optical depth that is important for primary productivity models. Currently, $K_d \left(\mathrm{PAR}\right)$ can be estimated using an irradiometer or a Secchi disk ($\mathrm{SD}$). The main objective of this work is to define a model that can be applied to a wide range of optical marine conditions to estimate $K_d \left(\mathrm{PAR}\right)$ by $\mathrm{SD}$. We used irradiance profiles and $\mathrm{SD}$ depth ($Z_{SD}$) from 679 stations in various marine regions. Three parametric models were developed, and their statistical performance was evaluated in view of previous approaches reported and remote sensing data. The best results were obtained with an adaptive model representing three cases: clear-water, turbid-water, and a transition zone (R² = 0.965, MAE = 0.083, RMSD = 0.239, BIAS = 0.01, and MPI = 0.854). Previous models considering a single optical depth figure at which the $SD$ disappears did not capture the marine optical complexity. Our classification of 113 stations with spectral absorption data into Jerlov water types indicated that no unique correspondence existed between estimated $K_d$ (PAR) and water type, making it ambiguous to associate compatible inherent optical properties and chlorophyll with $Z_{SD}$. Although obtaining $K_d$ ($\mathrm{PAR}$) from $Z_{SD}$ is simple/low-cost, care should be taken in the methodology used to measure $Z_{SD}$ to ensure consistent results across different optical marine conditions.

This study aimed to describe the interannual climate variability in the West Antarctic Peninsula (WAP) under austral summer conditions. Time series of January sea-surface temperature (SST) at 1 km spatial resolution from satellite-based multi-sensor data from Moderate Resolution Imaging Spectrometer (MODIS) Terra, MODIS Aqua, and Visible Infrared Imager Radiometer Suite (VIIRS) were compiled between 2001 and 2020 at localities near the Gerlache Strait and the Carlini, Palmer, and Rothera research stations. The results revealed a well-marked spatial-temporal variability in SST at the WAP, with a one-year warm episode followed by a five-year cold episode. Warm waters (SST > 0 °C) reach the coast during warm episodes but remain far from the shore during cold episodes. This behavior of warm waters may be related to the regional variability of the Antarctic Circumpolar Current, particularly when the South Polar Front (carrying warm waters) reaches the WAP coast. The WAP can be divided into two zones representing two distinct ecoregions: the northern zone (including the Carlini and Gerlache stations) corresponds to the South Shetland Islands ecoregion, and the southern zone (including the Palmer and Rothera stations) corresponds to the Antarctic Peninsula ecoregion. The Gerlache Strait is likely situated on the border between the two ecoregions but under a greater influence of the northern zone. Our data showed that the Southern Annular Mode (SAM) is the primary driver of SST variability, while the El Niño Southern Oscillation (ENSO) plays a secondary role. However, further studies are needed to better understand regional climate variability in the WAP and its relation with SAM and ENSO; such studies should use an index that adequately describes the ENSO in these latitudes and addresses the limitations of the databases used for this purpose. Multi-sensor data are useful in describing the complex climate variability resulting from the combination of local and regional processes that elicit different responses across the WAP. It is also essential to continue improving SST approximations at high latitudes.

Ocean color is the result of absorption and scattering, as light interacts with the water and the optically active constituents. The measurement of ocean color changes enables monitoring of these constituents (dissolved or particulate materials). The main objective of this research is to use digital images to estimate the light attenuation coefficient ($K_d$), the Secchi disk depth ($Z_{SD}$), and the chlorophyll a ($Ch{l}_a)$ concentration and to optically classify plots of seawater using the criteria proposed by Jerlov and Forel using digital images captured at the ocean surface. The database used in this study was obtained from seven oceanographic cruises performed in oceanic and coastal areas. Three approaches were developed for each parameter: a general approach that can be applied under any optical condition, one for oceanic conditions, and another for coastal conditions. The results of the coastal approach showed higher correlations between the modeled and validation data, with $r_p$ values of 0.80 for $K_d$, 0.90 for $Z_{SD}$, 0.85 for Chl_a, 0.73 for Jerlov, and 0.95 for Forel–Ule. The oceanic approach failed to detect significant changes in a digital photograph. The most precise results were obtained when images were captured at 45° (n = 22; $F_{r cal}=11.02>F_{r crit}=5.99$). Therefore, to ensure precise results, the angle of photography is key. This methodology can be used in citizen science programs to estimate Z_SD, K_d, and the Jerlov scale.

The vertical attenuation coefficient of photosynthetically active radiation ($K_d\left(PAR\right)$) is essential for characterizing the underwater light field and for operational marine monitoring. Although there have been efforts to use the standard satellite light attenuation product at 490 nm ($K_d\left(490\right)$) to estimate ($K_d\left(PAR\right)$) over a decade, earlier approaches were constrained by limited data. This study used a globally representative robust database of in-situ and satellite observations spanning diverse marine optical conditions and applied rigorous quality control. Three empirical models (linear, power, and a higher-order polynomial) were developed using four $K_d\left(490\right)$ satellite variants validated against an independent dataset and benchmarked against six published algorithms (36 total approximations). Performance was assessed using a Model Performance Index (MPI), where values closer to 1 indicate a better model. The best model was a power regression driven by the standard satellite $K_d\left(490\right)$, which yielded an MPI of 0.8704, indicating a robust performance under a wide variability of marine optical conditions. These results highlight the value of multisensor products, which with a rigorous quality control protocol, could be used to estimate the $K_d\left(PAR\right)$ from the standard satellite $K_d\left(490\right)$. The objective of the proposed algorithm is to generate long-term $K_d\left(PAR\right)$ time series. This algorithm will be operational for implementation in marine ecosystem monitoring systems and can contribute to strengthening decision-making.