## 4 Conclusions  
Remote sensing and Machine Learning present many opportunities to improve our understanding of marine ecosystems. Therefore, this study was performed to dive into the potential of using such data and techniques to understand better the physical process occurring around the Maltese Islands. Initially, a set of aims and objectives were defined, and this research sought to reach these. The main aim was to integrate in situ data, satellite data, and MLAs to generate accurate SSS and SST predictions for the Maltese archipelago. Thus, an empirical workflow was implemented by utilising the Sentinel-2 satellite platforms, the RF Machine Learning technique, and in situ data obtained through sea gliders and ARGO floats. Subsequently, numerical data produced by the RF Machine Learning method was transformed into visual data in the form of correlation graphs and SSS and SST maps of the Maltese Islands to give a better understanding of the spatial and temporal changes occurring within this region. The main findings from this study show that integrating satellite-derived data, in situ data, and MLAs proved to be great at obtaining accurate predicted SSS and SST data, illustrating the potential of utilising all three techniques in tandem. Additionally, the RF performance for both these parameters shows that the algorithm is capable of handling dynamic parameters, producing highly accurate predicted parametric maps of SSS and SST for three separate years, 2022, 2023, and 2024. This gave a good understanding not only of the spatial but also the temporal changes occurring within these parameterssurrounding the Maltese Islands. Lastly, the SSS and SST results proved that the RF algorithm requires a substantial amount of in situ data to effectively learn the underlying patterns and relationships within the dataset, as evidenced by the reduced PCCs of SSS and SST results in 2024 in comparison to the 2022 and 2023 PCCs. Following comprehensive data collection and rigorous analysis, the study provided valuable insights into the use of satellite data and MLAs to predict salinity and temperature. These are crucial parameters for marine monitoring and are essential for numerous applications, including marine conservation, resource management, climate change preparedness, water quality management, erosion, sediment management, and much more. This study proved that satellite data can be utilised to predict highly dynamic marine parameters. However, as our reliance on satellite-based monitoring increases, this research has also illustrated that through the use of in situ instruments, traditional monitoring techniques should not be neglected. Most Machine Learning applications that utilise satellite imagery depend on ground-based data for training and validation. Consequently, ongoing in situ surveys, particularly those at regularly monitored locations, are crucial to assess the growing range of remote sensing platforms, techniques and data as a means to improve the overall scientific understanding of these methodologies. Additionally, the methodology employed in this study, despite being applied within a relatively small area surrounding the Maltese Islands, demonstrates significant potential for implementation in larger and more extensive regions. This adaptability stems from the reliance of the utilised approach on the integration of in situ data and satellite data rather than being inherently tied to the specific study location. However, a key limitation of this methodology lies in its dependence on the quality of the satellite data. Climatic and oceanographic conditions, such as cloud coverage, sun glint, and turbid waters, can compromise the accuracy of the RF model by introducing outliers into the satellite dataset. These outliers arise from spectral discrepancies in comparison to typical aquatic regions. To mitigate this, it is crucial to use satellite images that are free from such conditions and instead provide clear and uninterrupted visuals.