A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA)

Understanding water quality (WQ) is essential for grasping biogeochemical cycles and assessing human impacts such as deforestation, climate change, dam construction, illegal mining, and urbanization. However, monitoring WQ across Brazil's vast aquatic systems requires significant resources. Remote sensing improves this process by offering high-resolution and large-scale observations. Nevertheless, to produce reliable remote-sensing WQ products based on remote sensing demands comprehensive datasets with concurrent aquatic reflectance and in situ measurements (e.g., Chlorophyll-a, Secchi Disk Depth, Suspended Sediments). Such data enables advanced semi-analytical and machine learning models to capture Brazil's bio-optical water complexity. Collaborative efforts and open-data sharing are essential for building these datasets. Here, we introduce a new curated, high-quality bio-optical dataset across different aquatic systems in Brazil, called BRAZA (Bio-optical aquatic database for remote sensing of water quality in BRAZil coAstal and inland waters). By leveraging data from 17 institutions, covering 2,895 stations across + 128 lakes, rivers, reservoirs, and coastal areas of Brazil's five administrative regions our dataset presents an important contribution to support remote sensing WQ-based analysis in Brazil.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-07-21
DOI
https://doi.org/10.1038/s41597-025-05609-1
Citations
5
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
8.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA)

Henrique Bernini, Cláudio Clemente Faria Barbosa, Maurício Almeida Noernberg, Rogério Flores Júnior et al.
5 citations
Scientific Data
Marine and coastal ecosystems
8.97
article

A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA)

Henrique Bernini, Cláudio Clemente Faria Barbosa, Maurício Almeida Noernberg, Rogério Flores Júnior, Marcela Miranda, Daniel Andrade Maciel, Fernanda Watanabe, Marta Eichemberger Ummus, David da Motta Marques, Júlio César Pimenta dos Santos, Rogério Ribeiro Marinho, Matheus Henrique Tavares, Aline M. Valério, Evlyn Márcia Leão de Moraes Novo, Gabriel Moiano Cesar, Raianny Leite do Nascimento Wanderley, Marcio Sousa da Silva, Vitor Souza Martins, Felipe Pacheco, Rejane S. Paulino, Fernanda Helena Oliveira da Silva, Waterloo Pereira Filho, Thainara M.A. Lima, Áurea Maria Ciotti, Alice César Fassoni‐Andrade, Lino Augusto Sander de Carvalho, Pedro Tadao Hamamoto Filho, Rafael G. Chasles, Pedro Bacellar, Rafaela C. Zem, Fernando Bezerra Lopes, Anne H.S. Martinelli, Lohan Barbosa Baia
article en
5 citations

Abstract

Understanding water quality (WQ) is essential for grasping biogeochemical cycles and assessing human impacts such as deforestation, climate change, dam construction, illegal mining, and urbanization. However, monitoring WQ across Brazil's vast aquatic systems requires significant resources. Remote sensing improves this process by offering high-resolution and large-scale observations. Nevertheless, to produce reliable remote-sensing WQ products based on remote sensing demands comprehensive datasets with concurrent aquatic reflectance and in situ measurements (e.g., Chlorophyll-a, Secchi Disk Depth, Suspended Sediments). Such data enables advanced semi-analytical and machine learning models to capture Brazil's bio-optical water complexity. Collaborative efforts and open-data sharing are essential for building these datasets. Here, we introduce a new curated, high-quality bio-optical dataset across different aquatic systems in Brazil, called BRAZA (Bio-optical aquatic database for remote sensing of water quality in BRAZil coAstal and inland waters). By leveraging data from 17 institutions, covering 2,895 stations across + 128 lakes, rivers, reservoirs, and coastal areas of Brazil's five administrative regions our dataset presents an important contribution to support remote sensing WQ-based analysis in Brazil.

Scientific DataVol. 12(1)
Universidade Federal do Rio de Janeiro (BR), Universidade Tecnológica Federal do Paraná (BR), Universidade Federal do Rio Grande do Sul (BR), Universidade de Brasília (BR), Vale (Brazil) (BR), Cornell University (US), Universidade Federal do Ceará (BR), Universidade Federal de Santa Maria (BR), Indiana University Bloomington (US), Instituto de Pesca (BR), New York State University College of Human Ecology (US), Hospital de São Sebastião (PT), Serviço Geológico do Brasil - CPRM (BR), Universidade Federal do Paraná (BR), Universidade Federal do Pará (BR), Universidade Federal do Amazonas (BR), Instituto Nacional de Pesquisas Espaciais (BR), Universidade Estadual Paulista (Unesp) (BR), Mississippi State University (US)
National Science Foundation, Biodiversa+, Universidade Estadual da Paraíba, Agence Nationale de la Recherche, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Bundesministerium für Bildung und Forschung, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação de Amparo à Pesquisa do Estado do Amazonas, Norges Forskningsråd, Fundação de Apoio à Pesquisa do Distrito Federal, Fundação de Apoio à Pesquisa do Estado da Paraíba, Agência Espacial Brasileira
Life below water, Clean water and sanitation
Openalex Percentile: Top 2%
Marine and coastal ecosystems
8.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.