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.

Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis.

High-resolution, physical-biological models of coastal and shelf regions typically use a single functional phytoplankton group, which limits their ability to represent ecological gradients (e.g. highly productive shelf systems adjacent to oligotrophic regions), as these are dominated by different functional phytoplankton groups. We implemented a size-structured ecosystem model in a high-resolution, regional circulation model of the northeast North American shelf and adjacent deep ocean in order to assess whether the added functional complexity of two functional phytoplankton groups improves the model's ability to represent surface chlorophyll concentrations along an ecological gradient encompassing five distinct regions. We used satellite-derived SST and sea-surface chlorophyll for our model assessment, as these allow investigation of spatial variability and temporal variations from monthly to interannual, and analyzed three complimentary statistical measures of model-data agreement: model bias, root mean square error and model efficiency (or skill). All three measures were integrated for the whole domain, for distinct subregions and were calculated in a spatially explicit manner. Comparison with a previously published simulation that used a model with a single phytoplankton functional group indicates that the inclusion of an additional phytoplankton group representing picoplankton markedly improves the model's skill.

Lakes are inestimable renewable natural resources that are under significant pressure by human activities. Monitoring lakes regularly is necessary to understand their dynamics and the drivers of these dynamics to support effective management. Remote sensing by satellite sensors offers a significant opportunity to increase the spatiotemporal coverage of environmental monitoring programs for inland waters. Lake color is a water quality attribute that can be remotely sensed and is independent of the sensor specifications and water type. In this study we used the Multispectral Imager (MSI) on two Sentinel-2 satellites to determine the color of water of 170 Italian lakes during two periods in 2017. Overall, most of the lakes appeared blue in spring and green-yellow in late summer, and in particular, we confirm a blue-water status of the largest lakes in the subalpine ecoregion. The color and its seasonality are consistent with characteristics determined by geomorphology and primary drivers of water quality. This suggests that information about the color of the lakes can contribute to synoptic assessments of the trophic status of lakes. Further ongoing research efforts are focused to extend the mapping over multiple years.