## 5 Conclusions and Future Work  
A transfer learning method containing the stages of model pretraining, main training, and fine tuning, was proposed to train ANN models for Chla concentration estimation using Sentinel-3 images. In addition, data augmentation and rebalancing were performed not only to increase the variability of the training dataset, but also to balance the samples in terms of Chla concentrations. To evaluate the ease of overfitting and to compare with related models, the models were trained using the Chla dataset from Laguna Lake and then tested using the Chla dataset from Lake Victoria, which has the same trophic state with Laguna Lake. The quantitative assessments on the Sentinel-3 WFR images demonstrate that the proposed transfer learning method is better than that of WaterNet, and the trained CNN outperforms the related models in terms of Chla estimation accuracy. Considering that the data rebalancing can provide massive effects to the performance of the model, in the near future, WaterNet will be redesigned such that the neural network can be applied to other optical satellite imagery with better spatial resolution, including Sentinel-2 and Landsat 8 images, in order to improve the extraction of important spatial features in lake water bodies. In addition, other water quality parameters, such as turbidity and total suspended matter, will be included in the modelling. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\begin{tabular}{c} **Station** \\ **Name** \\ \end{tabular} } & \multicolumn{4}{c}{**Estimation Error (in ug/L)**} \\ \cline{2-6}  & \begin{tabular}{c} **Three-Band** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Two-Band** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **NDCI** \\ \end{tabular} & \
\begin{tabular}{c} **WaterNet** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline \(\text{LV}_{1}\) & 0.746 & 0.653 & 0.304 & 0.645 & 0.302 \\ \hline \(\text{LV}_{2}\) & 0.367 & 0.303 & \(-0.164\) & 0.277 & 0.117 \\ \hline \hline
**RMSE** & 0.588 & 0.509 & 0.244 & 0.496 & 0.229 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparisons of the ANN model trained by the proposed transfer learning with the related models using Chla samples acquired from Lake Victoria, Uganda. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Model Name**} & \multicolumn{1}{c}{**Formula**} & \multicolumn{1}{c}{**Calibration Model**} \\ \hline Three-band model & \(\left\{\left[R_{rs}^{-1}(665)-R_{rs}^{-1}(709)\right]\times R_{rs}(754)\right\}\) & Linear regression \\ \hline Two-band model & \(\left\{\left[R_{rs}(709)\div R_{rs}(665)\right]\right\}\) & Linear regression \\ \hline NDCI & \(\left\{\begin{array}{c}\left[R_{rs}(665)-R_{rs}(709)\right]\\ \div\left[R_{rs}(665)+R_{rs}(709)\right]\end{array}\right\}\) & \\ \hline WaterNet & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information of the compared Chla estimation models. **Author Contributions:** Conceptualization, M.A.S., C.-H.L. and L.M.J. ; data curation, M.A.S. ; formal analysis, M.A.S., C.-H.L. and L.M.J. ; funding acquisition, C.-H.L. ; investigation, M.A.S., D.H., U.L. and B.M.S. ; methodology, M.A.S. ; project administration, C.-H.L. ; software, M.A.S. and D.H.; supervision, C.-H.L., U.L., B.M.S. and L.M.J. ; validation, M.A.S. and D.H.; visualization, M.A.S. ; writing--original draft, M.A.S., C.-H.L. and L.M.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by the Ministry of Science and Technology, Taiwan (grant numbers MOST 106-2923-M-006-003-MY3 and 109-2923-M-006-001-MY3); and the Indonesian Ministry of Research and Technology/National Agency for Research and Innovation (grant number 1377/PKS/ITS/2020). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable.