## 2 Materials and Methods
### Prediction Models  
In this study, the RF Machine Learning algorithm was utilised to predict the SST and SSS variation around the Maltese Islands. The RF is frequently used due to its high performance and is derived from the Decision Tree algorithm. Several decision trees are assembled by sampling the dataset and randomly choosing features from each subset to create approximations of decision trees. Subsequently, the predicted outcomes of each decision tree are amalgamated to improve the accuracy of the predictive model. The RF algorithm considers nonlinear interactions among variables and can handle both categorical and numerical data types. Unlike conventional empirical or semi-empirical models such as Linear Regression, Multilayer Perceptron and Support Vector Machines, the RF constructs more accurate and adaptable models using available data and is proficient at delivering rapid outcomes, resilient to overfitting, and capable of managing multicollinearity. The RF algorithm was employed as it represents a supervised Machine Learning approach, requiring labelled data for training. The learning process in the RF models is divided into two stages: training and testing. During the training phase, the learning algorithm utilises samples from the training data to acquire insights into the features, thereby constructing the learning model. Conversely, the test set evaluates the performance of the model on unseen data, gauging its capacity to generalise to new instances. In this study, the training dataset included coordinates, spectral information, in situ data and other relevant data obtained through the PCA. This dataset was divided into an 80/20 ratio, meaning that the training subset encompassed 80% of the original dataset, while the testing subset comprised the remaining 20%. Regarding the RF algorithm, the 'ntree' parameter, indicating the number of decision trees constructed by the model, was configured to 100. While constructing more trees could potentially enhance the model's performance, it could also introduce heightened risks of overfitting, amplification of model complexity, and escalation of computational burden. Upon construction, training, and testing of the model, the Pearson Correlation Coefficient (PCC) and the Root Mean Square Error (RMSE) were used as error metrics to quantify the linear correlation between the in situ parameter values and the predicted values. The PCC value can range from \(-1\) to \(1\), with the sign indicating whether the variables are positively correlated or inversely correlated. Furthermore, a higher coefficient denotes a greater degree of co-variation between the two variables. The PCC was chosen over alternative error metrics, such as the Spearman Correlation Coefficient, due to its ability to evaluate the linear relationship between the dependent and independent variables at a constant rate. Contrastingly, other metrics assess monotonic relationships, where variables may increase or decrease at differing rates. The PCC specifically measures the strength of a consistent, proportional relationship. Apart from utilising the PCC, an RMSE value was also provided to quantify the difference between predicted values and in situ values. A lower RMSE value indicates that the predictions are closer to the actual values, suggesting a better-performing model. Lastly, the outcomes of the model were plotted to visualise and provide a graphical representation of its performance. Once the RF algorithm was trained and tested for each parameter, the satellite data from October 2022 and 2023 and June 2024, encompassing the complete datasets of the Maltese archipelago, were employed to predict these parameters for the entire region of interest. The predicted parameter outcomes were subsequently validated by comparing the predicted outcomes to model data derived from the Marine Copernicus Data Hub (Figure 6). Finally, the October 2022, October 2023, and June 2024 results were compared to understand the spatial and temporal variations of the two parameters occurring within the designated region.