The Southwestern Atlantic Ocean (SAO), is considered as one of the most productive areas of the world, with high abundance of ecologically and economical important fish species. Yet, the biological responses of this complex region to climate variability are still uncertain. Here, using 24 years of satellite derived Chl-a datasets, we classified the SAO into coherent regions based on homogeneous temporal variability of Chl-a concentration, as revealed by the SOM (Self-Organizing Maps) analysis. These coherent biogeographical regions were the basis of our regional trend analysis in phytoplankton biomass, regional phenological indices, and environmental forcing variations. A generalized positive trend in phytoplankton concentration is observed, especially in the highly productive areas of the northern shelf-break, where phytoplankton biomass is increasing at an outstanding rate up to 0.42 ± 0.04 mg m-3 per decade associated with the sea surface temperature (SST) warming (0.11 ± 0.02 °C decade-1) and the mixed layer depth shoaling (-3.36 ± 0.13 m decade-1). In addition to the generalized increase in chlorophyll, the most sticking changes in phytoplankton dynamics observed in the SAO are related to the secondary bloom that occurs in most of the regions (15 ± 3 and 24 ± 6 days decade-1) which might be explained by the significant warming trend of SST, which would sustain the water stratification for a longer period, thus delaying the secondary bloom initialization. Consistent with previous studies, our results provided further evidences of the impact of climate change in these highly productive waters.

The Ocean and Land Colour Instrument Imagery (OLCI) sensor provides moderate spatial and temporal resolution of marine data, becoming a promising tool for monitoring environmental changes in coastal waters. Therefore, it is fundamental to test and validate the resulting products from diverse algorithms to ensure the quality of the data. The complex waters of southern Buenos Aires Province inner and mid-shelf, characterized by the presence of estuaries and river inputs, are highly influenced by total suspended matter (TSM) variability. In this study, we evaluate the performance of three TSM products in different waters (estuarine, coastal and mid-shelf waters) with in situ data. Two products were obtained using neural networks (NN), i.e. OLCI L2 ESA standard product (TSM_NN) and Case 2 Regional Coast Colour processing (C2RCC_STD); and one product using the combination of an alternative Baseline Residual Atmospheric Correction approach and the Nechad 2010 TSM algorithm (BLR_NCHD). In general, TSM match-up results indicate that the OLCI TSM_NN and CR2CC_STD products are acceptable (R2 of 0.79-0.74, n=17, RMSE= 21-20 mg/L). The best results were obtained for BLR_NCHD product (R2=0.86, RMSE=7 mg/L). Future efforts needed to improve TSM retrieval involves the evaluation of the conversion factor between backscattering to TSM for the NN approaches and the evaluation of the atmospheric correction using in situ water reflectance measurements.