A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA)
Daniel Andrade Maciel, Cláudio Clemente Faria Barbosa, Evlyn Márcia Leão de Moraes Novo, Raianny Leite do Nascimento Wanderley, Pedro Bacellar, Lohan Barbosa Baía, Henrique Bernini, Rafael G. Chasles, Áurea Maria Ciotti, Alice César Fassoni‐Andrade, Rogério Flores Júnior, Thainara M.A. Lima, Fernando Bezerra Lopes, Rogério Ribeiro Marinho, Anne H.S. Martinelli, Vitor S. Martins, Marcela Miranda, Gabriel Moiano Cesar, David da Motta Marques, Maurício Almeida Noernberg, Fernanda Helena Oliveira da Silva, Felipe Pacheco, Rejane S. Paulino, Waterloo Pereira Filho, Júlio César Pimenta dos Santos, Lino Augusto Sander de Carvalho, Marcio Sousa da Silva, Matheus Henrique Tavares, Marta Eichemberger Ummus, Aline M. Valério, Pedro Tadao Hamamoto Filho, Fernanda Watanabe, Rafaela C. Zem
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.