Colour classification of 1486 lakes across a wide range of optical water types

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

The Color of Water from Space: A Case Study for Italian Lakes from Sentinel-2

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

Spectra of a shallow sea-unmixing for class identification and monitoring of coastal waters

Ocean colour-based monitoring of water masses is a promising alternative to monitoring concentrations in heterogeneous coastal seas. Fuzzy methods, such as spectral unmixing, are especially well suited for recognition of water masses from their remote sensing reflectances. However, such models have not yet been applied for water classification and monitoring. In this study, a fully constrained endmember model with simulated endmembers was developed for water class identification in the shallow Wadden Sea and adjacent German Bight. Its performance was examined on in situ measured reflectances and on MERIS satellite data. Water classification by means of unmixing reflectance spectra proved to be successful. When the endmember model was applied to MERIS data, it was able to visualise well-known spatial, tidal, seasonal, and wind-related variations in optical properties in the heterogeneous Wadden Sea. Analyses show that the method is insensitive to small changes in endmembers. Therefore, it can be applied in similar coastal areas. For use in open ocean situations or coastal or inland waters with other specific inherent optical properties, re-simulation of the endmember spectra with local optical properties is required. However, such an adaptation requires only a limited number of local in situ measurements

Hue-angle product for low to medium spatial resolution optical satellite sensors

In the European Citclops project, with a prime aim of developing new tools to involve citizens in the water quality monitoring of natural waters, colour was identified as a simple property that can be measured via a smartphone app and by dedicated low-cost instruments. In a recent paper, we demonstrated that colour, as expressed mainly by the hue angle (α), can also be derived accurately and consistently from the ocean colour satellite instruments that have observed the Earth since 1997. These instruments provide superior temporal coverage of natural waters, albeit at a reduced spatial resolution of 300 m at best. In this paper, the list of algorithms is extended to the very first ocean colour instrument, and the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution product. In addition, we explore the potential of the hue angle derivation from multispectral imaging instruments with a higher spatial resolution but reduced spectral resolution: the European Space Agency (ESA) multispectral imager (MSI) on Sentinel-2 A,B, the Operational Land Imager (OLI) on the National Aeronautics and Space Administration (NASA) Landsat-8, and its precursor, the Enhanced Thematic Mapper Plus (ETM+) on Landsat-7. These medium-resolution imagers might play a role in an upscaling from point measurements to the typical 1-km pixel size from ocean colour instruments. As the parameter α (the colour hue angle) is fairly new to the community of water remote sensing scientists, we present examples of how colour can help in the image analysis in terms of water-quality products

Microstructure observations during the spring 2011 STRATIPHYT-II cruise in the northeast Atlantic

Small-scale temperature and conductivity varia-tions have been measured in the upper 100 m of the northeastAtlantic during the STRATIPHYT-II cruise (Las Palmas–Reykjavik, 6 April–3 May 2011). The measurements weredone at midday and comprised 2 to 15 vertical profiles at eachstation. The derived turbulent quantities show a transitionbetween weakly-stratified (mixed layer depth, MLD,100), which was centered atabout 48◦N. The temperature eddy diffusivities,KT, rangefrom 10−5to 100m2s−1in the weakly-stratified stations, andrange from 3×10−4to 2×100m2s−1in the well-mixed sta-tions. The turbulent kinetic energy dissipation rates,ε, rangefrom 3×10−8to 2×10−6m2s−3south of the transition zone,and from 10−7to 10−5m2s−3north of the transition zone.The station-averagedKTvalues throughout the mixed layerincrease exponentially with the wind speed. The station-averagedεvalues throughout the mixed layer scale with thewind stress similarity variable with a scaling factor of about1.8 in the wind-dominated stations (ε≈1.8u3∗/(−κz)). Thevalues ofKTandεare on average 10 times higher comparedto the values measured at the same stations in July 2009.The results presented here constitute a unique data set giv-ing large spatial coverage of upper ocean spring turbulencequantities

Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans

Stretching and bending vibrations of water molecules absorb photons of specific wavelengths, a phenomenon that constrains light energy available for aquatic photosynthesis. Previous work suggested that these absorption properties of water create a series of spectral niches but the theory was still too simplified to enable prediction of the spectral niches in real aquatic ecosystems. Here, we show with a state-of-the-art radiative transfer model that the vibrational modes of the water molecule delineate five spectral niches, in the violet, blue, green, orange and red parts of the spectrum. These five niches are effectively captured by chlorophylls and phycobilin pigments of cyanobacteria and their eukaryotic descendants. Global distributions of the spectral niches are predicted by satellite remote sensing and validated with observed large-scale distribution patterns of cyanobacterial pigment types. Our findings provide an elegant explanation for the biogeographical distributions of photosynthetic pigments across the lakes and oceans of our planet

Citclops:A next-generation sensor system for the monitoring of natural waters and a citizens’ observatory for the assessment of ecosystems’ status

41 pages, 28 figures, 1 table, supporting information https://doi.org/10.1371/journal.pone.0230084.s001.-- Through the interface at [http://www.citclops.eu/search/welcome.php] users are able to easily download all data. All data are also available from the dataset with DOI: 10.5281/zenodo.3497440The European-Commission—funded project ‘Citclops’ (Citizens’ observatory for coast and ocean optical monitoring) developed methods, tools and sensors, which can be used by citizens to monitor natural waters, with a strong focus on long-term data series related to environmental sciences. The new sensors, based on optical technologies, respond to a number of scientific, technical and societal objectives, ranging from more precise monitoring of key environmental descriptors of the aquatic environment (water colour, transparency and fluorescence) to an improved management of data collected with citizen participation. The sensors were tested, calibrated, integrated on several platforms, scientifically validated and demonstrated in the field. The new methods and tools were tested in a citizen-science context. The general conclusion is that citizens are valuable contributors in quality and quantity to the objective of collecting, integrating and analysing fragmented and diverse environmental data. An integration of these data into data-analysis tools has a large potential to support authoritative monitoring and decision-making. In this paper, the project’s objectives, results, technical achievements and lessons learned are presentedMore specifically, all authors (LC, JP, MRW, OZ, JAB, HVDW, RB, AF, SN, PT, FV, MB, KD) received funding from the European Union’s FP7 research and innovation programme under grant agreement No 308469 'Citclops'. LC received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 824711 'MICS' and No 824580 'EU-Citizen.Science'With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI