Development of a modular system for continuous monitoring of inland water quality by satellite - MAPAQUALI

• Grant number: 20/14613-8
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2021
• End date: March 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Cláudio Clemente Faria Barbosa
• Grantee: Cláudio Clemente Faria Barbosa
• Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovação (Brasil). São José dos Campos , SP, Brazil
• Associated researchers: Aurea Maria Ciotti ; Carolline Tressmann Cairo ; Cristina Souza Freire Nordi ; Evlyn Márcia Leão de Moraes Novo ; Felipe de Lucia Lobo ; Fernanda Sayuri Yoshino Watanabe ; Lino Augusto Sander De Carvalho ; Maria do Carmo Carvalho ; Marta Condé Lamparelli ; Vitor Souza Martins
• Associated research grant(s): 24/05686-2 - 37th International Congress on Limnology, AR.BR

Abstract

Approximately 12% of the fresh water available for use on the Earth's surface circulates over the Brazilian territory. On one hand, water availability puts Brazil in a privileged position. On the other hand, a great challenge arises; how to monitor and sustainably use these natural resources. Studies have reported intensification of algal blooms events and contamination by cyanobacterial toxins in many Brazilian hydroelectric reservoirs, due to nutrients input from agriculture surrounding reservoirs as well as input of untreated water of nearby cities. Bimonthly or quarterly monitoring in some sampling stations, despite having a significant contribution to the understanding of water quality, lacks space-time coverage to better support public policies for the sustainable use of these aquatic systems. Given the Brazil continental size and the number and dimensions of its aquatic ecosystems, studying and monitoring them is only feasible, over a national scale, with the support of orbital remote sensing and with the training of human resources to apply this technology in this large number of environments. Therefore, this proposal aims to: i) Obtain resources to operationalize a prototype of a modular system for continuous monitoring of water quality and cyanobacterial bloom alerts, customized for the Billings and Promissão reservoirs. The implementation part (coding) of the system is in the testing phase, however, the operationalization depends on calibration, validation and customization for the environment to be monitored, which demands data in situ; ii) develop bio-optical algorithms to estimate phycocyanin concentration; and iii) investigate the development of bio-optical algorithms for mapping phytoplankton diversity from hyperspectral sensors. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PAULINO, REJANE S.; MARTINS, VITOR S.; NOVO, EVLYN M. L. M.; BARBOSA, CLAUDIO C. F.; DE CARVALHO, LINO A. S.; BEGLIOMINI, FELIPE N.. Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images. REMOTE SENSING, v. 14, n. 8, p. 30-pg., 2022-04-01. (20/14613-8, 18/12083-1, 14/23903-9)

MACIEL, DANIEL ANDRADE; PAHLEVAN, NIMA; BARBOSA, CLAUDIO CLEMENTE FARIA; DE NOVO, EVLYN MARCIA LEAO DE MORAES; PAULINO, REJANE SOUZA; MARTINS, VITOR SOUZA; VERMOTE, ERIC; CRAWFORD, CHRISTOPHER J.. Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis. LIMNOLOGY AND OCEANOGRAPHY LETTERS, v. N/A, p. 9-pg., 2023-08-06. (18/12083-1, 03/06999-8, 20/14613-8, 11/19523-8, 13/09045-7, 12/19821-1, 11/23594-8, 08/56252-0, 14/23903-9)

DE LIMA, THAINARA MUNHOZ ALEXANDRE; GIARDINO, CLAUDIA; BRESCIANI, MARIANO; BARBOSA, CLAUDIO CLEMENTE FARIA; FABBRETTO, ALICE; PELLEGRINO, ANDREA; BEGLIOMINI, FELIPE NINCAO. Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms. REMOTE SENSING, v. 15, n. 5, p. 21-pg., 2023-03-01. (22/01107-2, 20/14613-8)