Chlorophyll-a (Chla) concentrations, which serves as a phytoplankton’s substitute in inland waters, is one of leading indicators for water quality. Generally, water samples are analysed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, the limited spatial water sampling and labour-sensitive data collection make the global and long-term monitoring difficult. The developments of remote-sensing optical sensors and technologies make the long-term monitoring of Chla concentrations for an entire water body to be achievable.
The retrieval of Chla concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs they do not fully utilize the spatial and spectral information of remote sensing images in neural networks and do not consider the problem of insufficient in situ labelled data during model training which make the trained models inapplicable. In addition, a data augmentation can be used to enrich the in situ labelled data, however, an overfitting problem may arise due to higher density in a specific Chla concentration interval and lesser density in the other intervals.
In this study, a novel convolutional neural networks (CNNs) for Chla concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chla estimation into the neural network leads to an efficient and effective Chla concentration retrieval. A two-stage training method which based on transfer learning technique is also proposed to ease the problem of insufficient in situ samples. The proposed training method contains pre-training stage and transfer-learning stage. In the model pre-training stage, the WaterNet is pre-trained and initialized by using samples derived from an existing Chl-a concentration model. The pre-trained WaterNet is then retrained or refined by using the proposed transfer learning with in situ samples collected in five different campaigns during early 2019 in Laguna Lake of the Philippines. Before the transfer learning, data augmentation and rebalancing methods are conducted to enrich the variability and to near-uniformly distribute the in situ samples in Chla concentration space, respectively. Therefore, there are three different in situ labelled data: original data (no augmentation and rebalancing is applied), augmented data (only augmentation), and augmented-rebalanced data (augmentation and rebalancing is applied). For summary, the proposed methods in this study are WaterNet, two-stage training, and data rebalancing.
In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet locally. The training was conducted in a 10-fold cross validation scheme, meaning that the augmented-rebalanced in situ labelled data is divided into 10 folds where each fold is consecutively set as validation data while the others as training data. To estimate the alleviation of model overfitting, the trained WaterNet was tested by using in situ dataset in Lake Victoria of Uganda obtained in 2019, which has similar tropical state with Laguna Lake.
In evaluation, WaterNet which trained by the two-stage training using simulated and augmented-rebalanced in situ labelled data, estimated Chla concentrations in Laguna Lake waters with average root mean squared error (RMSE) of 0.372 μg/L. Furhtermore, the performance was compared to several conditions: 1) WaterNet was trained by a common one stage training, either only using simulated or augmented-rebalanced in situ labelled data, to highlight the effectiveness of two-stage training; 2) WaterNet was trained by the two-stage training using simulated and augmented in situ labelled data, to display the leverage of data rebalancing; and 3) three ANNs models (Type A: 1 hidden layer; Type B: 2 hidden layers, Type C: 3 hidden layers) was trained the two-stage training using simulated and augmented-rebalanced in situ labelled data, to show the important impact of spectral variation in neighbourhood pixels that are considered in WaterNet. The results revealed that the Chla concentrations estimation accuracy in the three conditions were 1) 2.144 μg/L (using simulated labelled data only) and 1.298 μg/L (using in situ labelled data only); 2) 0.752 μg/L (using original data) and 0.954 μg/L (using augmented data); and 1.370 μg/L (Type A), 1.429 μg/L (Type B), and 1.374 μg/L (Type C); in terms of average RMSE. This implies the effectiveness of the proposed methods compared to the other related conditions in the Chla concentrations estimation. Moreover, in testing, the trained WaterNet outperformed the other existing Chla concentrations retrieval models in empirical method, including two-band, three-band, and normalized differentiate Chla index (NDCI).

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