AUTHOR=Flores Júnior Rogério , Barbosa Claudio Clemente Faria , Maciel Daniel Andrade , Novo Evlyn Marcia Leão de Moraes , Martins Vitor Souza , Lobo Felipe de Lucia , Sander de Carvalho Lino Augusto , Carlos Felipe Menino 

TITLE=Hybrid Semi-Analytical Algorithm for Estimating Chlorophyll-A Concentration in Lower Amazon Floodplain Waters

JOURNAL=Frontiers in Remote Sensing

VOLUME=Volume 3 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.834576

DOI=10.3389/frsen.2022.834576

ISSN=2673-6187

ABSTRACT=The Amazon Basin is the largest on the planet, and its aquatic ecosystems affect and are affected by Earth’s processes. Specifically, Amazon aquatic ecosystems have been subjected to severe anthropogenic impacts due to deforestation, mining, dam construction, and widespread agrobusiness expansion. Therefore,  the monitoring of these impacts has become crucial for conservation plans, and environmental legislation enforcement.. However, its continental dimensions, the high variability of Amazonian water masses constituents, and cloud cover frequency impose a challenge for developing accurate satellite algorithms for water quality retrieval such as chlorophyll-a concentration (Chl-a), which is proxy for the trophic state. This study presents the first application of the Hybrid Semi-Analytical Algorithm (HSAA) for Chl-a retrieval using Sentinel-3 OLCI sensor over five Amazonian floodplain lakes. Inherent and Apparent Optical Properties (IOPs & AOPs), as well as limnological data were collected at 94 sampling stations during four field campaigns along hydrological years spanning from 2015 to 2017 and used to parametrize the hybrid SAA to retrieve Chl-a in highly turbid Amazonian waters. We implemented a reparametrizing approach, called the Generalized Stacked Constraints Model to the Amazonian waters (GSCMLAFW), and used it to decompose the total absorption αt(λ) into the absorption coefficients of detritus, CDOM, and phytoplankton (αphy(λ)). The estimated GSCMLAFW αphy(λ) achieved errors lower than 24% at the visible bands and 70% at NIR. The performance of HSAA-based Chl-a retrieval was validated with in-situ measurements of Chl-a concentration and then it was compared to literature Chl-a algorithms. The results showed a smaller Mean Absolute Percentage Error (MAPE) for HSAA Chl-a retrieval (36.93%) compared to empirical Rrs models (73.39%) using a 3-bands algorithm, which confirms the better performance of semi-analytical approach. Lastly, the calibrated HSAA model was used to estimate the Chl-a concentration in OLCI images acquired during 2017 and 2019 field campaigns, and the results demonstrated reasonable errors (MAPE = 57%) and indicated the potential of OLCI bands for Chl-a estimation. Therefore, the outcomes of this study support the advance of semi-analytical models in highly turbid waters and highlight the importance of reparameterization with GSCM and the applicability of HSAA in Sentinel-3 OLCI data.