## 3 Results and Discussion  
### Random Forest Performance  
Datasets from October 2022, October 2023, and June 2024 were utilised to train and test the RF algorithm for both SSS and SST. The initial two datasets included in situ SSS and SST data gathered between August and December 2021, while the June 2024 dataset included in situ data collected in June 2024 using a float. Moreover, these datasets incorporated four spectral bands extracted from satellite data for the years 2022, 2023, and 2024, respectively. These were utilised to assess the predictive accuracy of the RF algorithm for more dynamic parameters compared to bathymetry. The PCC and RMSE were employed to evaluate the relationship between the in situ SSS and SST data and the corresponding predictions made by the RF algorithm. The results are summarised in Table 4. Furthermore, correlation plots were generated between the in situ data and the predicted sea surface parameters to visualise these relationships, as shown in Figure 8.  
The RF algorithm attained very high correlation coefficients and low RMSE values for SSS in 2022 and 2023, demonstrating a strong linear relationship between the predicted SSS values and the in situ measurements. This is further validated by the SSS scatter plots, which show a tight clustering of points around the diagonal line, indicating a close relationship between the predicted and actual values. The minimal differences between the two years demonstrate that the performance of the model remains highly consistent over time. Conversely, in 2024, there is a noticeable decline in the correlation strength compared to previous years. This reduction can likely be attributed to the substantially smaller training dataset available for this year, comprising only 40 data points, compared to the 400 data points in the datasets for 2022 and 2023. Despite this, the predicted SSS values remain closely aligned with the observed in situ SSS values. This stability suggests that the RF algorithm reliably captures the underlying patterns and variability in SSS despite potential interannual changes in the data. Thus, the RF algorithm effectively models the factors influencing salinity, including freshwater inputs, evaporation, and ocean currents. Similarly, the SST correlation coefficients for 2022 and 2023 are high and positive, indicating a strong and consistent correlation, whilst the coefficient for 2024 is significantly lower due to the same factors affecting the 2024 SSS dataset. This consistency further suggests that the performance of the model is stable and capable of generalising well across different temporal datasets. Despite the high correlation, the lower coefficients and higher RMSE values for SST compared to SSS suggest that predicting SST is inherently more difficult. This difficulty likely arises from the complex interactions of factors affecting temperature, including atmospheric conditions, ocean-atmosphere heat exchange, and regional climatic variability. Additionally, the greater dispersion of data points seen in the SST scatter plots in Figure 8 further indicates that SST data exhibits higher variability and more noise compared to SSS. The higher correlation coefficients and closely clustered scatter plots for SSS relative to SST indicate that the RF algorithm more accurately predicts salinity than temperature. This difference in performance may be due to the characteristics of the data and the underlying physical processes. SSS tends to display more stable and predictable patterns, while SST is more susceptible to short-term and small-scale fluctuations. This rationale can similarly be applied when considering parameters such as bathymetry in relation to the two sea surface parameters. Bathymetry tends to exhibit more stable variations, as it is subjected to less change compared to the more variable salinity and temperature parameters. Therefore, the stability in variation of a parameter is associated with the effectiveness of the RF algorithm in learning the underlying patterns within the datasets.