WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

Muhammad Aldila Syariz,Manh Van Nguyen,Lalu Muhamad Jaelani,Chao-Hung Lin,Ariel C Blanco

doi:10.3390/rs12121966

Muhammad Aldila Syariz, Manh Van Nguyen + Show 3 more

Open Access

https://doi.org/10.3390/rs12121966

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Abstract

The retrieval of chlorophyll-a (Chl-a) 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 do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a 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 Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. 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. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

Highlights

Eutrophication occurs when a water body becomes overly enriched with nutrients, e.g., phosphorus and nitrogen
A three-layer structure, consisting of input, hidden, and output layers, was adopted in these artificial neural networks (ANNs)-based studies, and the results reveal the capability of ANNs to retrieve Chl-a concentrations
To evaluate the performance of WaterNet, we adopted the k-fold cross-validation, in which k is set to 10 and all samples from the campaigns were uniformly partitioned into 10 folds

Summary

Introduction

Eutrophication occurs when a water body becomes overly enriched with nutrients, e.g., phosphorus and nitrogen. The avoidance of eutrophication and the monitoring of Chl-a concentrations are important for the improvement of human health and for the reduction of financial losses. Long-term water quality monitoring and measurement is necessary and has become a growing global concern. The conventional approach, which involves collecting and analyzing water samples in a professional laboratory, can accurately extract water quality parameters, such as chlorophyll-a (Chl-a) concentration, colored dissolved organic matter (CDOM), and total suspended sediment, at few sampling stations. This approach is labor intensive and time consuming when long-term monitoring and management are required

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Conclusion