## 1 Introduction  
Marine environments are experiencing changes due to the cumulative effects of human activities, natural dynamics, and global climate variations [1]. The coastal zones of the Mediterranean are particularly vulnerable to these impacts, which serve as the primary drivers of the ongoing environmental changes in the region. Consequently, there is an increasing emphasis on developing a comprehensive understanding of the hydrodynamics of the Mediterranean Sea [2]. This interest stems from the association of numerous water parameters with extreme weather events, such as torrential rainfall and prolonged troughs, as well as their direct environmental consequences, including coastal flooding, beach erosion, degradation of coastal vegetation and agricultural lands, and saltwater intrusion into coastal aquifers, lagoons, and other freshwater resources [3]. Moreover, climatic variability has been shown to negatively impact fisheries in the region by driving changes in phytoplankton and zooplankton populations, which form the foundation of the marine food web [4]. The frequency of these phenomena has increased in recent decades, emphasising the need to examine the hydrodynamics of the region in order to gain a deeper understanding of their effects. Numerous regions within the Mediterranean Sea exhibit notable variations in their spatial extent, complex structural patterns, and temporal dynamics. Protecting these ecosystems requires accurate monitoring of their physical characteristics and governing processes at fine spatial and temporal scales [5]. Given the anticipated global climate change, gaining a comprehensive understanding of the evolutionary patterns exhibited by marine ecosystems is imperative. Thus, monitoring plays a vital role in oceanic protection and management [6]. Consequently, to effectively evaluate physical aquatic dynamics, it is essential to acquire precise and up-to-date information regarding several parameters, including sea surface salinity and sea surface temperature [7]. Therefore, to gain a more profound understanding of these productive ecosystems, employing remote sensing techniques can serve as a valuable tool for comprehending the distinctive characteristics and mechanisms operating within marine regions [8]. Hence, remote sensing can assume a crucial role in overseeing and surveying oceanographic mechanisms, contributing significantly to the advancements in the realms of societal, cultural, and economic domains by virtue of the acquisition of newly acquired up-to-date data. Satellite remote sensing proves to be a valuable instrument to observe aquatic ecosystems. The regular overpass of satellites facilitates the scheduled and economically efficient acquisition of diverse observations across extensive and inaccessible regions within brief time frames [9]. Due to the diverse number of sensors, methodologies, and platforms utilised, satellite observations exhibit variations in their spatial, spectral and temporal attributes, with distinct satellites being tailored for specific applications [10]. Additionally, integrating in situ data with satellite data has created more opportunities to investigate the ever-evolving processes within hydrographic regions, encompassing extensive spatial coverage and extended temporal durations. Nevertheless, dealing with intricate datasets, along with the task of recognising the underlying patterns and trends, proves to be challenging. As a result, advanced techniques, such as Machine Learning, have been explored, which facilitate the extraction of data in a more efficient, precise, and automated manner [11]. Machine Learning offers numerous benefits when compared to traditional approaches, as it can construct models that encompass highly dimensional and nonlinear data characterised by building complex relationships. This capability proves valuable across a wide array of applications within various components of Earth systems [12].