Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

With more than 80% of Brazilians living in cities, urbanization has had an important impact on climatic variations. São José dos Campos is located in a region experiencing rapid urbanization, which has produced a remarkable Urban Heat Island (UHI) effect. This effect influences the climate, environment and socio-economic development on a regional scale. In this study, the brightness temperatures and land cover types from Landsat TM images of São José dos Campos from 1986, 2001 and 2010 were analyzed for the spatial distribution of changes in temperature and land cover. A quantitative approach was used to explore the relationships among temperature, land cover areas and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI). The results showed that urban and bare areas correlated positively with high temperatures. Conversely, areas covered in vegetation and water correlated positively with low temperatures. The indices showed that correlations between the NDVI and NDWI and temperature were low (<0.5); however, a moderate correlation was found between the NDBI and temperature.

Cyanobacterial blooms have substantial direct and indirect negative impacts on freshwater ecosystems including releasing toxins, blocking light needed by other organisms, and depleting oxygen. There is growing concern over the potential for climate change to promote cyanobacterial blooms, as the positive effects of increasing lake surface temperature on cyanobacterial growth are well documented in the literature; however, there is increasing evidence that cyanobacterial blooms are also being initiated and persisting in relatively cold‐water temperatures (< 15°C), including ice‐covered conditions. In this work, we provide evidence of freshwater cold‐water cyanobacterial blooms, review abiotic drivers and physiological adaptations leading to these blooms, offer a typology of these lesser‐studied cold‐water cyanobacterial blooms, and discuss their occurrence under changing climate conditions.

We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m³. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m³). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.