This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.

We quantified the role of climate change and internal variability on marine heatwaves (MHWs) in the North Sea over more than four decades (1982–2024). A key finding is the 2013 climate shift, which was associated with increased warming and MHWs. Long-term warming accounted for 80 % of the observed trend in MHW frequency. The most intense MHW event in May 2024 was attributed to an anomalous anticyclonic atmospheric circulation. We also explored the impact of MHWs on chlorophyll concentrations.

Marine heatwaves and cold spells are periods of extreme sea temperatures. This study focuses on Chilean Northern Patagonia, a fjord region vulnerable due to its aquaculture. It aims to understand these events' distribution and identify the most affected basins. Results show higher intensity in enclosed areas like Reloncaví Sound and Puyuhuapi Fjord. Marine heatwaves are becoming more frequent over time, while cold spells are decreasing.

Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.

Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.