The Ocean Colour Thematic Assembly Centre (OCTAC) of the Copernicus Marine Service delivers state-of-the-art Ocean Colour core products for both global oceans and European seas, derived from multiple satellite missions. Since 2015, the OCTAC has provided global and regional high-level merged products that offer value-added information not directly available from space agencies. This is achieved by integrating observations from various missions, resulting in homogenized, inter-calibrated datasets with broader spatial coverage than single-sensor data streams. OCTAC enhanced continuously the basin-level accuracy of essential ocean variables (EOVs) across the global ocean and European regional seas, including the Atlantic, Arctic, Baltic, Mediterranean, and Black seas. From 2019 onwards, new EOVs have been introduced, focusing on phytoplankton functional groups, community structure, and primary production. This paper provides an overview of the evolution of the OCTAC catalogue from 2015 to date, evaluates the accuracy of global and regional products, and outlines plans for future product development.

In this work we present a super-resolution approach for deriving high-spatial-resolution and high-temporal-resolution ocean colour satellite datasets. The technique is based on DINEOF (data-interpolating empirical orthogonal functions), a data-driven method that uses the spatio-temporal coherence of analysed datasets to infer missing information. DINEOF is used here to effectively increase the spatial resolution of satellite data and is applied to a combination of Sentinel-2 and Sentinel-3 datasets. The results show that DINEOF is able to infer the spatial variability observed in the Sentinel-2 data to the Sentinel-3 data while reconstructing missing information due to clouds and reducing the amount of noise in the initial dataset. In order to achieve this, the Sentinel-2 and Sentinel-3 datasets have undergone the same pre-processing, including a comprehensive, region-independent, and pixel-based automatic switching scheme for choosing the most appropriate atmospheric correction and ocean colour algorithm to derive in-water products. The super-resolution DINEOF has been applied to two different variables (turbidity and chlorophyll) and two different domains (Belgian coastal zone and the whole of the North Sea), and the sub-mesoscale variability of the turbidity along the Belgian coastal zone has been studied.

Aquaculture has the potential to sustainably meet the growing demand for animal protein. The availability of water is essential for aquaculture development, but there is no knowledge about the potential inland water resources of the Rwenzori region of Uganda. Though remote sensing is popularly utilized during studies involving various aspects of surface water, it has never been employed in mapping inland water bodies of Uganda. In this study, we assessed the efficiency of seven remote-sensing derived water index methods to map the available surface water resources in the Rwenzori region using moderate resolution Sentinel 2A/B imagery. From the four targeted sites, the Automated Water Extraction Index for urban areas (AWEInsh) and shadow removal (AWEIsh) were the best at identifying inland water bodies in the region. Both AWEIsh and AWEInsh consistently had the highest overall accuracy (OA) and kappa (OA > 90%, kappa > 0.8 in sites 1 and 2; OA > 84.9%, kappa > 0.61 in sites 3 and 4), as well as the lowest omission errors in all sites. AWEI was able to suppress classification noise from shadows and other non-water dark surfaces. However, none of the seven water indices used during this study was able to efficiently extract narrow water bodies such as streams. This was due to a combination of factors like the presence of terrain shadows, a dense vegetation cover, and the image resolution. Nonetheless, AWEI can efficiently identify other surface water resources such as crater lakes and rivers/streams that are potentially suitable for aquaculture from moderate resolution Sentinel 2A/B imagery.

The northeast Atlantic and adjacent regions, such as the North Sea, are among the fastest-warming areas in the world. However, the role of climate change and internal variability in marine heatwaves (MHWs) in this region remains poorly understood. This study aims to quantify the relevant changes in sea surface temperature (SST) and MHWs in the North Sea, as well as to identify the leading patterns of interannual MHW variability over more than 4 decades (1982–2024). Our results indicated a new regime shift in the annual mean SST in the North Sea since 2013. Therefore, we examined the relationships between MHW trends and long-term sea surface warming trends to quantify the role of climate change in the intensification of MHWs. We found that the increase in MHWs is related to the significant decadal change in SST over the North Sea and we have revealed that large-scale climate modes, such as the Atlantic Multidecadal Oscillation and the East Atlantic Pattern, play a crucial role in this decadal change in SST. In particular, the SST trend has doubled in the post-2013 period (0.8 °C per decade), compared with the pre-2013 period (0.4 °C per decade), leading to longer and more frequent MHWs. The SST, MHW frequency, and MHW days increased significantly by 0.38 °C per decade, 1.04 events per decade, and 17.27 d per decade, respectively, over the entire study period. After removing the long-term sea surface warming trend before MHW detection, all MHW features exhibited insignificant trends, indicating that the long-term SST trend is the primary driver of the observed long-term MHW trend in the North Sea region, thereby confirming the crucial role of mean SST changes in MHWs in this region. Furthermore, we found that 80 % of the observed trend in MHW frequency is attributed to long-term warming, while the rest is attributed to internal variability. The SST record in May 2024, manifest by the longest (27 d) and most intense (2.2 °C) MHW event, is attributed to an anomalous anticyclonic atmospheric circulation over the Baltic Sea and southern Norway, which enhanced solar radiation over the North Sea. Finally, we also investigated how the chlorophyll a concentration responded to the MHW, revealing a decrease in the deep and cold-water regions of the northern North Sea and an increase in the shallow and warm-water areas of the southern North Sea.

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.

In this work we present a super-resolution approach to derive high spatial and temporal resolution ocean colour satellite datasets. The technique is based on DINEOF (Data Interpolating Empirircal Orthogonal Functions), a data-driven method that uses the spatio-temporal coherence of the analysed datasets to infer missing information. DINEOF is now used to effectively increase the spatial resolution of satellite data, and is applied to a combination of Sentinel-2 and Sentinel-3 datasets. The results show that DINEOF is able to infer the spatial variability observed in the Sentinel-2 data into the Sentinel-3 data, while reconstructing missing information due to clouds and reducing the amount of noise in the initial dataset. In order to achieve this, both Sentinel-2 and Sentinel-3 datasets have undergo the same preprocessing, including a comprehensive, region-independent, and pixel-based automatic switching scheme for choosing the most appropriate atmospheric correction and ocean colour algorithm to derive the in-water products. The super-resolution DINEOF has been applied to two different variables (turbidity and chlorophyll) and two different domains (Belgian coastal zone and the whole North Sea), and the submesoscale variability of the turbidity along the Belgian coastal zone has been studied.