## 1 Introduction
[PERSON] et al. [25] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Trophic Class** &
\begin{tabular}{c} **Chla Concentration** \\ **Range (in ug/L)** \\ \end{tabular} & **Water Condition** \\ \hline \hline \multirow{2}{*}{Oligotrophic} & \multirow{2}{*}{0–2.6} & A lake with very clear waters and high drinking water quality due to low nutrient content and algal production. \\  & & \\ \hline \multirow{2}{*}{Mesotrophic} & \multirow{2}{*}{2.6–20} & Commonly clear water lakes with beds of submerged aquatic plants and medium levels of nutrients. \\  & & \\ \hline \multirow{2}{*}{Eutrophic} & \multirow{2}{*}{20–56} & The water body will be dominated either by aquatic plants or algae. \\  & & Highly nutrient-rich lakes characterized by frequent and severe nuisance algal blooms and low transparency. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of trophic state index [8]. designed several fully connected neural networks and searched for the optimal hyperparameters, including the number of hidden layers and the number of neurons in a layer. The study also revealed that the optimal ANN model outclassed the other machine learning methods, including random forest, cubist regression, and support vector regression, in terms of Chla concentration estimation. Furthermore, several researchers utilized convolutional neural networks (CNNs), which consider neighborhood spectral information in Chla concentration modelling using convolutional layers with 3D kernels [26; 27; 28; 29]. An ANN model requires a high number of labelled data--that is, it uses in situ Chla concentrations as outputs and their corresponding \(R_{rs}\) in satellite images as inputs, and the initial values for unknown parameters for model training. [PERSON] et al. [28] constructed a CNN model with more than 2000 unknown parameters. This model was trained using only 238 labelled data. Meanwhile, [PERSON] and [PERSON] [26] utilized 320 labelled data to train a CNN model containing 2432 unknown parameters. However, overfitting problems may arise because insufficient labelled data are used to search for the optimal values of thousands of unknown parameters during model training. [PERSON] et al. [30] applied data augmentation to enrich the labelled data; however, they did not consider the data imbalance problem that may affect the estimation accuracy. Furthermore, some researchers utilized simulated datasets instead of in situ Chla concentration data to deal with the labelled data insufficiency [31; 32; 33]. A simulated dataset means that the Chla concentration information is obtained from an existing known model. With this procedure, the labelled data insufficiency can be solved; however, training a neural network model with a simulated dataset may not reach the global optimum of the defined loss function. [PERSON] et al. [34] proposed a two-stage training method, in which the model is firstly pretrained using a simulated dataset, and the pretrained model is then retrained using an in situ dataset. The advantage of this method is that the pretraining process is able to provide good initial values for the unknown parameters before the main training process using the in situ dataset. The training process can train an ANN model rather well for Chla concentration estimation. However, the overfitting problem is not fully alleviated because of the lack of training sample variability and the problem of training sample imbalance. In this study, the main objectives were (1) to propose a transfer learning technique using the two-stage transfer training approach for better Chla concentration estimation accuracy; (2) to enrich and balance the Chla-labelled data by performing data augmentation and rebalancing techniques; and (3) to test the ANN model trained using the improved proposed two-stage training transfer learning approach with an in situ dataset from Laguna Lake, using the in situ dataset acquired from Lake Victoria, Uganda.