## 1 Introduction
One area where remote sensing and Machine Learning can be utilised is in the measurement of sea surface temperature (SST) and sea surface salinity (SSS), which serve as fundamental components in marine research. Both these parameters are pivotal metrics to gauge climate variability, oceanic conditions, and ecological dynamics. By evaluating surface temperature and salinity, researchers acquire invaluable insights into climate trends, localised weather occurrences, fluctuations in marine biodiversity, ocean circulation patterns and water quality [13,14]. Methods for monitoring SST and SSS encompass in situ measurements, satellite observations, and model-generated data. In situ approaches that acquire SST and SSS measurements involve directly sampling water temperature and salinity at specific locations. Common in situ techniques include ship-based measurements, observations from buoys, drifters, gliders and floats and instrumented moorings that produce long-term data [15,16]. The SST and SSS measurements obtained through these in situ techniques are subsequently utilised to authenticate and evaluate the precision of the satellite-derived data [17]. Furthermore, by merging the data generated by satellites with the extensive records of in situ measurements, it becomes feasible to create more intricate reconstructions of the observable marine patterns over a broader expanse of the Earth's sur face [18]. This integration enables stakeholders to effectively evaluate and monitor changes in these physical parameters with both temporal precision and broad spatial coverage [19]. Although the integration of satellite data, in situ data, and Machine Learning holds the potential to revolutionise marine science, the full potential of remote sensing technologies remains largely untapped within the field of environmental management [20; 21; 22]. The authors of [23] delineated four primary themes contributing to the underutilisation of these technologies. These themes encompass concerns related to the costs and precision of data products, especially related to highly dynamic parameters, uncertainties regarding the continuity of satellite missions, and challenges associated with obtaining administrative approval for integrating remote sensing into the decision-making process. The latter theme proves essential as the future of applied remote sensing hinges on the maritime managers, who serve as the ultimate end-users, to acknowledge the necessity for data capable of unveiling alterations in patterns across an almost uninterrupted spatial range, highlighting disturbances and features within these oceanic regions [8]. Hence, the major challenge lies in establishing remote sensing techniques as a customary tool for evaluating changes in aquatic zones by acquainting end-users with the extensive array of existing satellite data and the available imagery that adequately meets their requirements [24]. Achieving this goal would transform this sector from a data-poor field marked by limited spatiotemporal coverage into a data-rich field where new hydrographic data is collected regularly and with high accuracy [25]. Therefore, this study aims to integrate in situ data, satellite data, and a Machine Learning algorithm, namely random forest, to generate accurate spatial and temporal sea surface salinity (SSS) and sea surface temperature (SST) maps for the Maltese archipelago through multispectral satellite products. Additionally, this paper intends to propose an efficient methodology for acquiring local marine physical data, which can subsequently be utilised within a wider European context, encompassing larger regions characterised by greater temporal and spatial variability.