## 3 Results and Discussion
### RF Predictive Accuracy for SSS and SST  
Figure 10 depicts the predictive accuracy of the RF algorithm for SSS and SST data in 2022, 2023, and 2024. Each graph contrasts the predicted SSS or SST values with unseen model data sourced from the Marine Copernicus Data Hub, providing a visual evaluation of the accuracy and generalisation capabilities of the algorithm. The scatter plot representing SSS data for 2022 exhibits points that are moderately scattered around the line of best fit, indicating some variability in the predictions. Nonetheless, it is important to comprehend that the predicted SSS values for 2022 closely resemble the model data, implying that the RF algorithm generated accurate predictions, albeit with slight variations. In contrast, the scatter plots for SSS in 2023 and 2024 exhibit a stronger and more positive clustering of points around the line of best fit, indicating that the RF model demonstrated superior predictive performance compared to 2022. Regarding SST, both 2022 and 2023 exhibit a comparable pattern, demonstrating a robust correlation between predicted and model values. The data points are closely grouped around the line of best fit, indicating a high level of predictive precision. Conversely, the predictive accuracy for SST in 2024 demonstrates a weaker positive correlation, indicating reduced accuracy, which may once again be attributed to the size of the 2024 dataset. Nonetheless, the predicted SST values for 2024 remain highly comparable to the model SST values despite showing some minor variations. Hence, the results reaffirm that the RF model serves as a dependable tool for SST prediction, given its consistently high accuracy across the two years. While the scatter plots in Figure 10 demonstrate positive correlations between the predicted and model data, employing in situ data could have yielded more reliable and precise analysis compared to using model-generated data. This is primarily because in situ measurements represent the actual conditions for ground trut  
Figure 10: Scatter plots demonstrating the predictive accuracy of the RF algorithm for 2022, 2023, and 2024 in comparison to model data. reference to evaluate the accuracy of predictive models. Moreover, in situ measurements can offer real-time data at a finer resolution, capturing temporal variations more accurately compared to model data, which may rely on periodic sampling or time-averaging. However, due to the absence or insufficient acquisition of in situ data for SSS and SST in 2022, 2023, and 2024, the utilisation of model data was necessary, serving as a satisfactory substitute for in situ data. In this study, an integrated approach was utilised, and it proved to be successful and insightful. However, several limitations were encountered, which may have impacted the overall direction of the study or the accuracy of the results. These limitations stem from challenges inherent in the use of satellite and in situ data, the complexity of accurately modelling marine environments, and the constraints associated with the Machine Learning technique employed. The following summarises the key limitations observed during the study. The spatial resolution could only be a maximum of 10 m as that is the highest resolution of the Sentinel-2 datasets. Therefore, certain fine-scale variations within the marine environment might have been overlooked. Additionally, in situ data was not obtained for SSS and SST validation due to time and cost constraints. Therefore, model data, which is less accurate than ground truth data, was required to validate the results. Lastly, the in situ dataset for SSS and SST in 2022 and 2023 had in situ measurements from a sea glider survey collected in 2021, whilst the in situ dataset for 2024 might have been too small, which could have led to errors within the RF algorithm during training and testing. As a result, this data might not have been sufficient for the algorithm to understand the underlying patterns within the dataset.