## 4 Conclusions
Similarly, the quality of in situ data is pivotal, as the RF algorithm relies on this data for its predictions. Employing precise instruments, such as drifters, sea gliders, and floats, to accurately measure sea surface parameters is essential for extending this methodology to broader study areas and reducing errors within the algorithm. By addressing the limitations and leveraging new technologies and methodologies, future research can enhance the accuracy, resolution, and applicability of these predictive models. The following recommendations outline key areas for future work, offering a pathway to build upon the current findings and push the boundaries of what can be achieved in marine environmental prediction and monitoring. The untapped potential of utilising drone data to predict marine parameters can be a new opportunity that offers the researcher results at a higher resolution, some of which can reach a centimetre scale. This would provide more detail and give a better understanding of the small changes occurring within our marine regions compared to the utilisation of satellite data. Secondly, gathering a large SSS and SST in situ dataset can help further validate the results of this study as this would allow the MLAs to learn on a large number of data points, better understanding the underlying patterns within these coastal parameters and therefore, should provide a more accurate and reliable model to predict SSS and SST. Subsequently, acquiring additional ground-truth data can mitigate the issue of relying on less accurate model data, thereby providing a better understanding of the accuracy levels of the algorithm. Lastly, the methodology utilised in this study can also be applied to other dynamic marine parameters such as chlorophyll to understand whether this approach can be applied to them and, if so, what level of accuracy the predictions can reach. **Author Contributions:** Conceptualization, G.C.D., A.G. and M.G.B. ; methodology, G.C.D. and A.G.; software, G.C.D. ; validation, G.C.D. ; formal analysis, G.C.D. ; investigation, G.C.D. ; resources, A.G. and M.G.B. ; data curation, G.C.D., A.G. and M.G.B. ; writing--original draft preparation, G.C.D. ; writing--review and editing, A.G., M.G.B. and A.D.; visualisation, G.C.D. ; supervision, A.G. and M.G.B. ; project administration, A.G. and M.G.B. ; funding acquisition, M.G.B. All authors have read and agreed to the published version of the manuscript. This work was supported by the BathMalta project, which was funded by the Malta Council for Science and Technology (MCST) through the Space Research Fund Call 2022, SRF-2022-1S1. Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. The glider data was obtained through a partnership between the Oceanography Malta Research Group (OMRG) at the University of Malta and the ODYSSEA project as well as ALSEAMAR. Additionally, the float instrument deployment was facilitated by a collaborative effort between the OMRG and the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OCS). The authors declare no conflicts of interest.