An improved algorithm for estimating chlorophyll-a in coastal waters of southern Brazil from multispectral satellite images

Talita Montagna; Alexander Christian Vibrans; Robson Ventura de Souza

doi:10.26848/rbgf.v18.1.p633-645

Authors

Talita Montagna Fundação Universidade Regional de Blumenau
Vibrans Fundação Universidade Regional de Blumenau https://orcid.org/0000-0002-8789-5833
Souza Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina https://orcid.org/0000-0003-0588-0038

DOI:

https://doi.org/10.26848/rbgf.v18.1.p633-645

Keywords:

Remote sensing, Monitoring

Abstract

Remote sensing chlorophyll-A (CLA) estimates from global models have been used to support decision making in southern Brazil, the most important bivalve mollusks production region (~9 thousand tons in 2022) in the country, and a recent study indicated that these estimates poorly represent the actual levels of CLA. The aim of the study was to develop an improved algorithm for estimating CLA in these coastal waters from multispectral images. A CLA database generated in situ between 2007 and 2009 was used to calibrate and validate algorithms based on spectral data from the Medium Resolution Imaging Spectrometer (MERIS) (ENVISAT satellite) (300m spatial resolution), including algorithms based on red and near-infrared bands: two bands (2B and M2B), three bands (3B) and the Normalized Difference Chlorophyll Index (NDCI and MNDCI). Outputs from the global models OC4ME and Neural Network were also evaluated. NIR-red algorithms outputs correlated significantly with the measured CLA, except for MNDCI. The best performing models during the calibration, those based on 2B and NDCI (R² = 0.37, residual standard error = 2.57 mg.m^-3), were validated and fitted better the measured data (R² >= 0.22) and showed lower RMSE values (around 2.5 mg.m^-3) than the global models’ outputs, which did not even correlate significantly (p>0.05) with in situ CLA measurements. The developed models performed better than the global models evaluated nevertheless they have a limited prediction power when compared to regional algorithms developed elsewhere and this is probably linked to the low range of CLA measurements used to train the models.

Author Biographies

Vibrans, Fundação Universidade Regional de Blumenau

Possuo graduação em Ciências Florestais Zonas Temperadas - Georg August Universität Göttingen (1987) e graduação em Ciências Florestais Zonas Tropicais e Subtropicais - Georg August Universität Göttingen (1987), mestrado em Engenharia Ambiental pela Universidade Regional de Blumenau (1999) e doutorado em Geografia pela Universidade Federal de Santa Catarina (2003). Atualmente sou professor do quadro da Universidade Regional de Blumenau, nas áreas de Dendrologia e Manejo Florestal. Sou pesquisador da área de Inventário Florestal, Ecologia Florestal e Sensoriamento Remoto. Atuo nos Programas de Pós-Graduação em Engenharia Ambiental (Mestrado e Doutorado) e Engenharia Florestal (Mestrado), ambos da Universidade Regional de Blumenau. Coordeno o Inventário Florístico Florestal de Santa Catarina (IFFSC) desde 2006. Fui Pro-Reitor de Pesquisa e Pós-Graduação da Universidade Regional de Blumenau de 2015 a 2019. Sou editor associado da Revista Biota Neotropica, para as áreas de Inventários Florísticos/Fitossociologia/Fitogeografia desde 2018. ORCID 0000-0002-8789-5833

Souza, Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina

Graduou-se em Medina Veterinária pela Universidade Federal do Paraná (2004), tem mestrado em Ciências Veterinárias pela mesma instituição (2006) e doutorado em Aquicultura pela Universidade Federal de Santa Catarina (2017). De 2003 a 2008 fez parte do Grupo Integrado de Aquicultura e Estudos Ambientais da Universidade Federal do Paraná. Em 2006 foi professor substituto do Departamento de Zootecnia da Universidade Federal do Paraná. Desde 2009 é pesquisador do Centro de Desenvolvimento em Aquicultura e Pesca da Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina. Tem experiência na área de Recursos Pesqueiros, com ênfase em Aquicultura, atuando principalmente nos seguintes temas: modelagem matemática aplicada a aquicultura, ciência de dados, monitoramentos sanitários de áreas destinadas a aquicultura, controle higiênico-sanitário de moluscos bivalves e desenvolvimento de tecnologia para a produção de formas jovens de organismos aquáticos para fins de repovoamento.

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