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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. Starting from the vibrational modes of water molecules, the authors derive five major spectral niches in aquatic ecosystems using a radiative transfer model. They then predict the global distributions of these spectral niches using remote sensing and validate the predictions with metagenomic data on the distribution patterns of cyanobacterial pigments.

The 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 presented.

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.

In order to increase the monitoring capabilities of inland and coastal waters, there is a need for new, affordable, sensitive and mobile instruments that could be operated semi-automatically in the field. This paper presents a prototype device to measure chlorophyll a fluorescence: the SmartFluo. The device is a combination of a smartphone offering an intuitive operation interface and an adapter implying a cuvette holder, as well as a suitable illumination source. SmartFluo is based on stimulated fluorescence of water constituents such as chlorophyll a. The red band of the digital smartphone camera is sensitive enough to detect quantitatively the characteristic red fluorescence emission. The adapter contains a light source, a strong light emitting diode and additional filters to enhance the signal-to-noise ratio and to suppress the impact of scattering. A novel algorithm utilizing the red band of the camera is provided. Laboratory experiments of the SmartFluo show a linear correlation (R 2 = 0.98) to the chlorophyll a concentrations measured by reference instruments, such as a high-performance benchtop laboratory fluorometer (LS 55, PerkinElmer).

The dramatic loss of sea ice due to global warming is changing light conditions for marine primary production, but exactly how is not well understood. Previous studies revealed that small peaks in the absorption spectrum of liquid water, due to molecular vibrations of H 2 O, delineate a series of spectral niches for aquatic photosynthesis. Ice, however, has a smoother absorption spectrum and scatters light much more strongly than liquid water. Here, we show with a radiative transfer model that the loss of sea ice causes a pronounced blue shift, narrowing light spectra in the euphotic zone to shorter wavelengths. Furthermore, ice cover yields a smooth continuum of light spectra, whereas open water creates distinct spectral niches selecting for phytoplankton species with different photosynthetic pigments. These results indicate that the loss of sea ice will cause major changes in both the pigment and species composition of primary producers in polar ecosystems. This study deduces from first principles how sea ice and seawater shape light spectra in fundamentally different ways. The loss of sea ice may thus trigger changes in both the pigment and species composition of primary producers in polar ecosystems.

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.

Biomass estimation of multiple phytoplankton groups from remote sensing reflectance spectra requires inversion models that go beyond the traditional band-ratio techniques. To achieve this objective retrieval models are needed that are rooted in radiative transfer (RT) theory and exploit the full spectral information for the inversion. HydroLight numerical solutions of the radiative transfer equation are well suited to support this inversion. We present a fast and flexible Python framework for forward and inverse modelling of multi- and hyperspectral observations, by further extending the formerly developed HydroLight Optimization (HYDROPT) algorithm. Computation time of the inversion is greatly reduced using polynomial interpolation of the radiative transfer solutions, while at the same time maintaining high accuracy. Additional features of HYDROPT are specification of sensor viewing geometries, solar zenith angle and multiple optical components with distinct inherent optical properties (IOP). Uncertainty estimates and goodness-of-fit metrics are simultaneously derived for the inversion routines. The pursuit to retrieve multiple phytoplankton groups from remotely sensed observations illustrates the need for such flexible retrieval algorithms that allow for the configuration of IOP models characteristic for the region of interest. The updated HYDROPT framework allows for more than three components to be fitted, such as multiple phytoplankton types with distinct absorption and backscatter characteristics. We showcase our model by evaluating the performance of retrievals from simulated Rrs spectra to obtain estimates of 3 phytoplankton size classes in addition to CDOM and detrital matter. Moreover, we demonstrate HYDROPTs capability for the inter-comparison of retrievals using different sensor band settings including coupling to full spectral coverage, as would be needed for NASA’s PACE mission. The HYDROPT framework is now made available as an open-source Python package.

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.