## 4 Experimental Results and Discussion
### Evaluation of the Transfer Learning  
To evaluate the proposed transfer learning technique with the processes of data augmentation and rebalance, the ANN named WaterNet was used for Chla concentration estimation. For details about WaterNet, please refer to Section 3.2. To evaluate the performance of the three training stages in the transfer learning, the hyperparameters containing the batch size, the optimizer, and the number of epochs was the same and 10-fold cross validation was performed on the rebalanced dataset from Laguna Lake. The evaluation results are presented in Table 4. After the model pretraining, the accuracy of the estimated Chla concentrations was not satisfied. The range and average of RMSEs of the folds were 2.070-2.228 \(\mathrm{\SIUnitSymbolMicro g}\)/L and 2.144 \(\mathrm{\SIUnitSymbolMicro g}\)/L, respectively. This implies that a poor performance with high estimation errors was obtained when training the ANN model using the simulated Chla data. Although the ANN model at this stage cannot effectively retrieve the Chla concentrations, this training stage can provide suitable initial values for the unknown parameters for the coming stage. As a result, a better estimation result was obtained in the main-training stage. The range and average of the RMSEs at folds decreased to 0.4866-0.6887 \(\mathrm{\SIUnitSymbolMicro g}\)/L and 0.5819 \(\mathrm{\SIUnitSymbolMicro g}\)/L, respectively. Moreover, the trained ANN model was further fine-tuned in the next stage. The average RMSE improved from 0.5819 \(\mathrm{\SIUnitSymbolMicro g}\)/L in the second stage to 0.3724 \(\mathrm{\SIUnitSymbolMicro g}\)/L in the third stage. This was caused by setting the layers in the band extension and feature extraction phases to untrainable and only permitting the backpropagation to work on the layers in the Chla concentration phase.  
The ANN model trained by the proposed transfer learning was applied to five Sentinel-3 images, which were acquired at similar dates with the field campaigns in Laguna Lake, Philippines. The Chla concentration maps for the water body, shown in Figure 6, are visualized by colors ranging from yellow (6 \(\upmu\)g/L) to red (12 \(\upmu\)g/L). In addition, the outputs from the feature extraction phase in the ANN shown in Figure 3 are convolutional feature maps of the size 3 \(\times\) 3 \(\times\) 5. The feature maps imply the importance of spatial features for the Chla estimation. To visualize the feature maps for the whole lake body, the center pixels of the feature maps were extracted and combined to form spatial feature maps. The Chla concentrations of Laguna Lake on 6 April 2019, estimated by the trained ANN and the spatial feature maps extracted from the trained ANN, are shown in Figure 7. The spatial feature maps #1 and #3 are flashier than the others. To address this on the two spatial feature maps, the two dashed boxes are set on the maps to represent the area of interest for highlight and discussion. As shown in the brown dashed box, most of the features within this area have smaller values in feature map #1 and higher values in feature map #3. The significant differences between these two feature maps result in high Chla concentrations during the model prediction. As for those in the yellow dashed box, the opposite results are obtained, because the area is homogeneous and the pixels within this area have similar values. This observation revealed that the proposed transfer learning is able to preserve spatial features that are important in Chla concentration estimation.