Cyanobacteria mapping in Promissão reservoir by hybrid algorithms and images from OLCI/Sentinel-3 sensor

• Grant number: 21/02714-7
• Support type: Scholarships in Brazil - Master
• Effective date (Start): August 01, 2021
• Effective date (End): February 28, 2023
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal researcher: Cláudio Clemente Faria Barbosa
• Grantee: Thainara Munhoz Alexandre de Lima
• Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil

Abstract

The cyanobacteria bloom in continental waters is a worldwide problem that needs to be mapped and monitored for the potential risk it poses to human health. In recent decades, with the increase in population growth and climate change, the biogeochemical processes of these aquatic environments have been negatively affected with regard to water quality. In this context, remote sensing emerges as an important tool in monitoring these ecosystems, where through bio-optical algorithms, information on water quality parameters and biogeochemical processes can be derived. Despite widespread use, the high spatio-temporal variations in the optical properties of Brazilian aquatic systems pose challenges to the use of these algorithms, which prove to be accurate only for specific ranges of parameter concentrations. Another important aspect concerns the estimation of chlorophyll-a as a criterion for issuing cyanobacterial flowering alerts. Chlorophyll is a pigment present in several species of phytoplankton, being an adequate indicator of phytoplankton biomass, but not cyanobacteria. Different studies have shown that phycocyanin exhibits a strong correlation with cyanobacterial flowering, being a unique and important pigment for monitoring trophic environments. Therefore, in this research, the main objective is to develop a hybrid algorithm for mapping cyanobacteria in the Promissao/SP reservoir using the OLCI/Sentinel-3 sensor. The aim of this research is to obtain a hybrid algorithm that has temporal coverage for the annual hydrological cycle, allowing continuous monitoring of cyanobacteria in the reservoir. In addition, it is expected that the algorithm will be able to circumvent the limitations of bio-optical algorithms in monitoring water quality in Brazilian aquatic systems, mainly with regard to monitoring and issuing cyanobacterial bloom alerts.