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Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types

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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.
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... However, in situ data series are rare and available only in a few lakes, limiting further understanding of changes in lake color over regions (Stanley et al., 2019). Satellite images have been utilized to investigate spatial distributions of lake color (Giardino et al., 2019;Lehmann et al., 2018;Topp et al., 2021;Yang et al., 2022). However, temporal shifts and trends in lake color and relevant drivers are rarely documented (Kuhn & Butman, 2021;Lehmann et al., 2019;Oleksy et al., 2022). ...
... The red, green, and blue bands, that is, R s (R), R s (G), R s (B), were first converted to an xyz chromatic color space and then were used to calculate the hue angle (α) using Equations 1-4 (Wang et al., 2015). Unlike calculations using all reflectance within 380-700 nm range dependent on spectral settings of satellite sensors (van der Woerd & Wernand, 2018), the equations used here were adapted based on the character of chromaticity space and suitable for all Landsat sensors Hou et al., 2022;Topp et al., 2021) (Text S3 in Supporting Information S1). was then determined using a look-up-tables of α and Lehmann et al., 2018). ...
... Water color is directly linked to variations in proportions and concentrations 7 of 10 of optical active constituents (OACs) in lakes (Gordon, 1983). In general, declines of could be related to lower chlorophyll-a and suspended particulate matter (SPM) in lakes (Giardino et al., 2019;Lehmann et al., 2018). However, the effects of CDOM on might not be sufficiently evaluated since CDOM has more influence on light saturation rather than hue. ...
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Plain Language Summary Lakes are affected by climate and human activity, and water quality can be reflected in lake color. Several studies have mapped the variations in lake colors, but the changing patterns in lake color and relevant reasons over the past decades remain unclear. We used satellite images to track annual trends of lake color since the 1980s in China. We found that the lake color for 68% of the lakes is trending toward blue color. The blue lakes in western China became bluer, and the green‐yellow lakes in eastern China shifted to greener colors. For lakes in western China (e.g., Tibetan Plateau), higher temperature and rainfall correlated with bluer lakes. For the shallow lake in eastern China (e.g., Yangtze River plain), more forest and grassland around the lake and weaker wind reduced the substances in lakes and are related to the shift in color. This research highlights the function of satellite images in tracking historical variations in lake color. Our results can help in understanding of the changes in lake color and its responses to climate change and human activity.
... The Wang method was initially applied to Lake Taihu (a large lake in China), while the vdWW method was initially applied to the North Sea and Dutch water bodies (coastal and inland) [11,13]. Both procedures have been used in recent studies to examine trends in water color in lakes over time and across regions (e.g., [15][16][17]), but a comparison of the accuracy of the two methods using different sensors has not been reported. ...
... The implementation of Wang and vdWW calculations has allowed researchers to classify and map the colors of water bodies across large regions. Lehmann et al. [15] calculated the λd values for λd for 1486 lakes in New Zealand, while and Giardino et al. [16] calculated the λd values for 170 Italian lakes using the vdWW method. Large datasets have also been used with the Wang method to calculate λd values for lakes [17] and rivers [19] over time in the United States. ...
... The implementation of Wang and vdWW calculations has allowed researchers to classify and map the colors of water bodies across large regions. Lehmann et al. [15] calculated the λ d values for λ d for 1486 lakes in New Zealand, while and Giardino et al. [16] calculated the λ d values for 170 Italian lakes using the vdWW method. Large datasets have also been used with the Wang method to calculate λ d values λ d for lakes [17] and rivers [19] over time in the United States. ...
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The dominant wavelength and hue angle can be used to quantify the color of lake water. Understanding the water color is important because the color relates to the water quality and its related public perceptions. In this paper, we compared the accuracy levels of two methods in calculating dominant wavelength and hue angle values using simulated satellite data calculated from in situ reflectance hyperspectra for 325 lakes and rivers in Minnesota and Wisconsin. The methods developed by van der Woerd and Wernand in 2015 and Wang et al. in 2015 were applied to simulated sensor data from the Sentinel-2, Sentinel-3, and Landsat 8 satellites. Both methods performed comparably when a correction algorithm could be applied, but the correction method did not work well for the Wang method at hue angles < 75°, equivalent to levels of colored dissolved organic matter (CDOM, a440) > ~2 m−1 or chlorophyll > ~10 mg m−3. The Sentinel-3 spectral bands produced the most accurate results for the van der Woerd and Wernand method, while the Landsat 8 sensor produced the most accurate values for the Wang method. The distinct differences in the shapes of the reflectance hyperspectra were related to the dominant optical water quality constituents in the water bodies, and relationships were found between the dominant wavelength and four water quality parameters, namely the Secchi depth, CDOM, chlorophyll, and Forel–Ule color index.
... To monitor water optical properties at large temporal and spatial scales, remote sensing is widely used to assess water column turbidity, coloured dissolved organic (CDOM) and chlorophyll-a (chl-a) concentrations by building empirical or semi-empirical regression models between remotely sensed colour bands and in situ data [7,8]. Recent work has also used water colour evaluated by dominant wavelength as an intuitive way of monitoring water quality (e.g., Lehmann et al. [9]), since it can reflect the water colour as perceived by the human eye in a numerical way. Woerd and Wernand [10] proposed empirical algorithms to estimate the hue (the dominant wavelength) and intensity of light using a weighted linear sum of remote sensing reflectance blue, green and red bands, which made it possible to transform satellite signals into colour science data [11]. ...
... We found averaged dominant wavelength values of shallow and deep water in the green part of the spectrum (528 nm and 545 nm, respectively) (Figure 15b), confirming the importance of phytoplankton pigments for optical water quality in Tauranga Harbour. Moreover, the dominant wavelength in the yellow to red part of the spectrum in the upper fringes of the inundated regions (Figures 9 and 11) was consistent with CDOM and SPM loading from rivers and subestuaries as well as bottom sediment resuspension driven by waves [9,34,35]. Note that because of the requirement in our method of a set of proximal pixels with uniform reflectance, the bottom correction using the spatial method could not be applied at the edge of subtidal and intertidal regions where high variation of chl-a and CDOM concentrations occur [26]. ...
... This method cannot be applied to images that have been histogram stretched or contrast enhanced because this kind of image manipulation would skew the optical relationship between targets and measurements used to develop Equations (7)- (9). In addition, the spatial method correction requires remote sensing images with a high original spatial resolution. ...
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Sentinel-2 imagery is potentially ideal for providing a rapid assessment of the ecological condition of estuarine water due to its high temporal and spatial resolution and coverage. However, for optically shallow waters, the problem of isolating the effect of seabed reflectance from the influence of water properties makes it difficult to use the observed surface reflectance to monitor water quality. In this study, we adopt a methodology based on Lyzenga’s model to estimate water quality properties such as the dominant wavelength and diffuse attenuation coefficient (Kd) of shallow estuarine waters. Lyzenga models the observed reflectance (R) using four parameters: total water depth (z), sea-bed reflectance (Rb), water reflectance (Rw) and Kd. If Rb is known a priori and multiple observations of R are available from different total water depths, we show that Lyzenga’s model can be used to estimate the values of the remaining two parameters, Kd and Rw. Observations of R from different water depths can either be taken from the same image at different proximal locations in the estuary (“spatial method”) or from the same pixel observed at different tidal stages (“temporal method”), both assuming homogeneous seabed and water reflectance properties. Tests in our case study estuary show that Kd and Rw can be estimated at water depths less than 6.4 m. We also show that the proximity restriction for the reflectance correction with the temporal method limits outcomes to monthly or seasonal resolution, and the correction with the spatial method performs best at a spatial resolution of 60 m. The Kd extracted from the blue band correlates well with the observed Kd for photosynthetically active radiation (PAR) (r2 = 0.66) (although the relationship is likely to be estuary-specific). The methodology provides a foundation for future work assessing rates of primary production in shallow estuaries on large scales.
... The 7,572 GLORIA R rs spectra originate from 31 countries over an almost global geographical range from 67°N to 54°S and from 122°W to 178°E (Fig. 2) with the majority of samples from lakes (60%), followed by coastal waters (32%), estuaries (4%), and the remainder from rivers and other water body types. The wide range of radiometric and water quality measurements in GLORIA (Fig. 2) is consistent with the global diversity of R rs spectral shapes with respect to optical water types 85,86 and visual color ranges 87,88 (Fig. 3). The range of water quality attributes is comprehensive and their frequency distributions are shown in (Fig. 2). ...
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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.
... A notable field of study for proglacial lakes is also regional-scale modeling, for which this methodology could be applied. Regional-scale studies about proglacial lakes do exist, such as Lehmann et al. (2018), which investigated the different colors of more than 1000 proglacial lakes in New Zealand from satellite imagery, to understand the time variations in glacier flour inflow. This and other models could be confirmed or enhanced by ground-based surveys such as the one presented in this paper. ...
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Proglacial lakes are distinctive features of deglaciated landscapes and often act as sediment sinks, collecting solid material from subglacial erosion or washout of deglaciated areas. The solid transport flow, strongly linked to the glaciers and periglacial landforms, may rise due to the rapid changes driven by climate warming, causing deep transformations in the basin hydrology, and even the appearance or disappearance of lakes at a decadal timescale. The goal of this study was to present a geophysical–geotechnical approach that integrates several techniques, to quantify the sediment distribution in a proglacial lake. A geophysical survey is performed with ground‐penetrating radar (GPR) installed on a boat, whereas a time‐domain reflectometer (TDR) measures the electrical conductivity and permittivity of the lakebed sediments. Unperturbed samples are collected and analyzed to measure the main geotechnical properties of the sediment: grain‐size distribution, plastic limit, and liquid limit. Such properties support the interpretation of the GPR data and the detection of spatial variations of the sediment facies. To validate the proposed methodology, field tests were carried out at Lake Seracchi, the largest lake of the Rutor glacier, Italian Alps. It formed around 1880 because of the recent glacier shrinkage, as chronicled by valuable historical documents. Its greyish waters carry a significant amount of suspended sediment recognized as glacial flour, which gradually accumulates on the bottom of the lake. The obtained bathymetry and sediment thickness maps of Lake Seracchi show the strength of the approach: from only a few manual samples, it is possible to extrapolate the geotechnical properties of interest, such as friction angle or hydraulic conductivity, to wider areas, surveyed by the geophysical techniques. This is achieved by investigating the spatial distribution of key geophysical properties linked to the geotechnical properties of interest.
... Landsat 8 was the most recent Landsat satellite and Landsat 8 OLI captures images with narrower spectral bands compared to Landsat 7 ETM+, but with an improved radiometric precision over a 12-bit dynamic range and higher signal-to-noise ratio. The OLI collects data in eight bands at 30 m resolution and one panchromatic band at 15 m resolution [9]. Thus, we used Landsat 8 OLI data in this study analyze the urban vegetation. ...
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