The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

In this study we verified data of water temperatures collected by CORSAN-RS from 1996 to 2004 in several cities of the state of Rio Grande do Sul, southern Brazil, and analyzed the possibility of raising the most cultivated fish species in Brazil. The water temperature from 1996 to 2004 was 16 to 28ºC in summer, 17 to 23ºC in fall, 14 to 17ºC (down to 9ºC in the coldest months) in winter and 14 to 21ºC in spring. Native species of this state, such as silver catfish (Rhamdia quelen), traíra (Hoplias malabaricus), dorado (Salminus brasiliensis), pintado (Pimelodus maculatus), as well as carps (family Cyprinidae), are resistant to the low winter temperatures. These species have a lower growth rate in coldest months (winter/spring) but a good development in warmer months (summer/fall), reaching a satisfactory performance throughout the year. In the periods of more intense cold, mortality of some introduced species, such as surubim from Amazon Basin (Pseudoplatystoma sp.), pirapitinga (Piaractus brachypomus), pirarucu (Arapaimas gigas), pacu (Piaractus mesopotamicus), tambaqui (Colossoma macropomum) and Nile tilapia (Oreochromis niloticus) may occur. In addition, as most tropical species have a thermal range for growth and reproduction between 20 to 28ºC, some species may have poor development even in fall. Therefore, water temperature in this state should be considered in the choice of fish species to be cultivated.

A técnica de análise derivativa de dados espectrais foi usada para estudar a variação dos constituintes opticamente ativos (COAs) na água, por meio de dados de campo e de imagens do sensor orbital Hyperion/EO-1. A imagem Hyperion usada neste estudo foi adquirida no dia 23 de junho de 2005, no final do período de cheia. Uma campanha de campo foi realizada entre 23 e 29 de junho de 2005, para coletar dados espectrais e limnológicos in situ. A imagem foi pré-processada visando eliminar faixas de pixels anômalos e convertida de valores de radiância para reflectância de superfície, portanto, corrigidos dos efeitos de absorção e espalhamento atmosféricos. Uma análise da correlação foi realizada para examinar a associação da reflectância e de sua primeira derivada espectral com as concentrações dos COAs. Melhores resultados foram obtidos após a diferenciação dos espectros, o que ajudou a reduzir a influência de efeitos indesejáveis, provindos de diferentes fontes de radiância, sobre as medidas de reflectância da superfície da água realizadas em ambos os níveis de aquisição de dados. Por meio de ajustes de regressões empíricas, considerando o conjunto de dados Hyperion, a primeira derivada espectral em 711 nm explicou 86% da variação da concentração de sedimentos inorgânicos em suspensão (µg.l-1) e a primeira derivada espectral em 691 nm explicou 73% da variação na concentração da clorofila-alfa (µg.l-1). As relações de regressão foram não-lineares, pois, em geral, as águas que se misturam na planície de inundação Amazônica se tornam opticamente complexas. A técnica de análise derivativa hiperespectral demonstrou potenciais para mapear a composição dessas águas.

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.