METHODS article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1570827
This article is part of the Research TopicSatellite Remote Sensing for Hydrological and Water Resource Management in Coastal ZonesView all 5 articles
Combination Of Neural Network Models For Estimating Chlorophyll-a Over Turbid And Clear Waters (CONNECT)
Provisionally accepted- 1Univ. Littoral Côte d’Opale, CNRS, Univ. Lille, IRD, UMR 8187 - LOG - Laboratoire d’Océanologie et de Géosciences, F-62930 Wimereux, France, Wimereux, France
- 2Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, SP, Brazil, São Paulo, Brazil
- 3Faculdade de Oceanografia, Rio de Janeiro State University, Rua São Francisco Xavier, 524, Maracanã, Rio de Janeiro 20550-013, RJ, Brazil, Rio de Janeiro, Brazil
- 4Institute of Biology, Rio de Janeiro Federal University, Avenue Rodolpho Rocco 211, sl. A1-071, Rio de Janeiro 20551-030, RJ, Brazil, Rio de Janeiro, Brazil
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Estimation of Chlorophyll-a concentration (Chl-a) across diverse aquatic systems using Moderate Resolution Imaging Spectroradiometer-Aqua (MODIS-A) data has posed challenges, particularly the inability of existing algorithms to maintain consistent accuracy across varying optical water conditions, from oligotrophic clear waters to highly turbid productive systems. Traditional Blue/Green ratio approaches often show limitations over optically complex waters where colored dissolved organic matter and suspended sediments interfere with phytoplankton signal detection. In contrast, Red/NIR (Near-Infrared) models perform relatively well in productive coastal domains but are less effective in open ocean waters where phytoplankton absorption is too weak to produce detectable signals in these longer wavelengths. To address these challenges, we developed a Combination Of Neural Network models for Estimating Chlorophyll-a over Turbid and clear waters (CONNECT model) based on the principle that different Optical Water Types (OWTs) require specialized bio-optical algorithms. The methodology involves the development of two Multi-Layer Perceptron (MLP) models (NN-Clear & NN-Turbid) that are trained and evaluated on a comprehensive in-situ dataset with simultaneous measurements of Remote Sensing Reflectance (Rrs) and Chl-a gathered in various environments from clear to ultra-turbid waters (N=5358) with Chl-a ranging between 0.017 and 838.24 µg.L -1 . These specialized models are then combined through a weighted blending approach to produce unified Chl-a estimates that adapts to the optical conditions of various water types. In particular, the algorithm merging process involves the use of probability values corresponding to 2 groups of Optical Water Types as the blending coefficients. Accuracy evaluations performed on both in-situ and matchup datasets indicate a remarkable advancement of the CONNECT model compared to the traditional Blue/Green approaches over different trophic conditions with an improvement of 49.65% on the matchup validation considering the Symmetric Signed Percentage Bias (SSPB) metric.
Keywords: chlorophyll-a, machine learning, Optical water types, coastal eutrophication, MODIS-Aqua, Ocean color remote sensing
Received: 04 Feb 2025; Accepted: 08 Aug 2025.
Copyright: © 2025 TRAN, Vantrepotte, El Hourany, Jorge, Kampel, Cardoso Dos Santos, Oliveira, Paranhos and Jamet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Vincent Vantrepotte, Univ. Littoral Côte d’Opale, CNRS, Univ. Lille, IRD, UMR 8187 - LOG - Laboratoire d’Océanologie et de Géosciences, F-62930 Wimereux, France, Wimereux, France
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