1. Introduction
Lakes are land-surrounded water bodies that generally provide freshwater for human daily needs. For instance, water from Lake Biwa, Japan, is used as a water drinking resource for people in Osaka and Kyoto and has been maintained as a conservation ecosystem with good water quality [
1]. In Indonesia, a freshwater treatment plant, namely, PDAM Kabupaten Kerinci, was built around Lake Kerinci in Jambi to take, store, filter, and distribute the water to people living nearby [
2]. Meanwhile, the worldwide demand for fish products has steadily increased due to the growing need for protein and the shift in behavior towards the consumption of healthier food [
3,
4]. The aquaculture industry often adds nutrient fertilizers, which are useful for commercial fish, to the water somewhere around the lake body. This procedure can fulfil the consumption demands; however, algal growth may be enhanced when nutrients are oversupplied. Consequently, the penetration of sunlight, which is required for respiration in fish, is limited and may lead to the extensive deterioration of water quality and the declining availability of freshwater, harming not only the fish, but also society. Therefore, the long-term monitoring of the water quality in lakes is necessary for the authorities to develop sustainable management initiatives to prevent water quality degradation and to maintain freshwater supplies in the future.
Chlorophyll-a (Chla), a pigment found in every phytoplankton species, is considered a critical water quality parameter for many environmental issues [
5,
6,
7]. The water quality and Chla concentration can be categorized into four classes based on the trophic state index: oligotrophic (less than 2.6 μg/L), mesotrophic (2.6–20 μg/L), eutrophic (20–56 μg/L), and hypertrophic (more than 56 μg/L) [
8]. The water quality condition for each class is described in
Table 1. Chla concentrations measured using field surveys are accurate and precise; however, the concentration data are only available at the sampling locations. Taking more measurements from the lake water body is hindered by the high labor and financial costs. Remote sensing technology enables researchers to empirically estimate the Chla concentration at the full spatial coverage of the lake water body by regressing the remote-sensing reflectance (
) or the features with the in situ data obtained from field survey. Dall’Olmo and Gitelson [
9] utilized the features of band ratios, combining
at wavelengths 443, 490, and 560 nm (denoted as
,
, and
) in a three-band model, in which the in situ samples used in training ranged from 4.4 μg/L to 217.3 μg/L. Al-Shehhi et al. [
10] exchanged the
at wavelengths
to
, which has been found to represent both water turbidity and algal absorption in a narrower range of in situ data (0.1–27.8 μg/L). Chen et al. [
11] performed local calibration in Chinese waters resulting in an
feature at
,
, and
. Gitelson et al. [
12] and Moses et al. [
13] simplified the three-band model to a two-band model by removing the
at
due to the similar sensitivity to absorption as
at
. Hence, Mishra and Mishra [
14] proposed a differentiate index, called the normalized differentiate Chla index (NDCI), and demonstrated that the method outperforms the three-band and two-band models in cross validation. Many researchers [
15,
16,
17,
18,
19,
20] searched for important features that are sensitive to Chla concentrations; however, the procedure is somewhat statistically exhaustive.
Another promising procedure to estimate Chla concentrations is by means of an artificial neural network (ANN). Buckton et al. [
21] proposed a fully connected neural network containing one hidden layer that revealed the capability of ANN for Chla concentration estimation. Similar work was also conducted by other researchers [
22,
23,
24]. Hafeez et al. [
25] 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
in satellite images as inputs, and the initial values for unknown parameters for model training. Pyo et al. [
28] constructed a CNN model with more than 2000 unknown parameters. This model was trained using only 238 labelled data. Meanwhile, Aptoula and Ariman [
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. Nguyen 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. Syariz 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. To evaluate the effectiveness of the proposed model learning methods, an ANN model, namely WaterNet, first proposed by Syariz et al. [
34], was adopted. The input to WaterNet was a water-body image patch of the size 7 (width) × 7 (height) × 16 (bands) and the output was an estimated Chla concentration at the center pixel of the input patch. Lastly, the proposed transfer learning method can increase the accuracy of Chla concentration retrieval in the lake water body, which can later be utilized by governments to better understand the lake water state and develop a clinical management plan to prevent water quality degradation and to maintain freshwater supplies in the future. The remainder of the paper is organized as follows.
Section 2 describes the study area, data material, acquisition, and preprocessing.
Section 3 elaborates the proposed transfer learning technique, data augmentation, and data rebalancing.
Section 4 presents the experimental results, performance, and the comparisons of the trained ANN model and related models, and
Section 5 provides the conclusions and future work.
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 Setinel-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.