Modelling chlorophyll-a concentrations in a continental aquatic ecosystem of the Brazilian semi-arid region based on remote sensing

Autor: Fernando Bezerra Lopes, Cláudio Clemente Faria Barbosa, Evlyn Marcia Leão de Moraes Novo, Lino Augusto Sander de Carvalho, Eunice Maia de Andrade, Adunias dos Santos Teixeira
Jazyk: English<br />Spanish; Castilian<br />Portuguese
Rok vydání: 2021
Předmět:
Zdroj: Revista Ciência Agronômica, Vol 52, Iss 2 (2021)
Druh dokumentu: article
ISSN: 1806-6690
DOI: 10.5935/1806-6690.20210028
Popis: ABSTRACT Remote-sensing data are essential to evaluate dynamic processes such as eutrophication and increases in the concentration of suspended sediments in continental aquatic systems. The aim of this study, therefore, was to develop models to estimate chlorophyll-a concentrations from remote-sensing data in continental waters of the Brazilian semi-arid region. The study area corresponds to the Orós reservoir, located in the state of Ceará. The models were developed based on measurements taken at 20 sampling points. Water samples were collected from the reservoir to i) analyse the chlorophyll-a, electrical conductivity, pH and turbidity; ii) take optical measurements in situ of water transparency and spectral radiance. Radiance measurements were carried out using an ASD FieldSpec®3 Hi-Res spectroradiometer. The spectral data were later transformed into reflectance values and used to test the performance of several models found in the literature for estimating chlorophyll-a. The results showed that for the three-band model, the maximum value for the coefficient of determination (R2), of 0.88, was obtained using the λ1 = 660 nm, λ2 = 690 nm and λ3 = 717 nm spectral bands. The model employing two spectral bands presented the best performance (R2 = 0.87) in the λ1 = 660 nm and λ2 = 690 nm bands. An absolute mean error of 5.35 and 5.00 µg L-1 was found for the three- and two-band models respectively. The developed models are reliable, showing that chlorophyll-a concentrations can be quantified from remote-sensing field data with a high degree of accuracy.
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