Abstract
Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.
Highlights
Eutrophication refers to the degradation of water quality due to high increasing of phytoplankton biomass in the watershed
The estimated chl-a concentration from IRTM-neural networks (NN) and OC4Me channels which collocated with in-situ stations were extracted and compared to in-situ chl-a concentration
A convolutional neural networks (CNN) model for chl-a concentration retrieval was proposed in this study
Summary
Eutrophication refers to the degradation of water quality due to high increasing of phytoplankton biomass in the watershed. It occurs as the aquaculture pond managers intentionally adding fertilizers to reach their goals, that are to enhance primary productivity and to increase the density of important fish (Boyd and Tucker, 1998). Several aspects have been damaged recently, such as fish kills, human health, and even economic stability. Long-term monitoring and real-time measurements of water quality play a critical role and recently become worldwide growing concern regarding to the quality of water resources available for multiple resources (Barzegar et al, 2018). The water quality monitoring is able to help decision-makers in setting achievable target for water quality improvement
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