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Plot of the available spectral bands in the visual range (400–710 nm) as function of spatial resolution for seven types of sensors. The multispectral imager (MSI) provides three different resolution modes, Landsat 8 has one band more than Landsat 7 at 30-m resolution. The Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Colour Instrument (OLCI) provide products at two resolutions.
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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 a...
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Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the othe...
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... Haffert et al. [47] developed VIS-X, a high-resolution spectrograph for the Magellan Clay 6.5 m telescope, enhancing sensitivity to protoplanetary systems and providing insights into planet formation processes in the visible spectrum. Van der Woerd et al. [48] conducted a study on the use of hue-angle products in optical satellite sensors for water quality monitoring, revealing accurate colour properties from ocean colour satellite instruments since 1997. ...
... The research begins by acquiring a dataset containing hue versus dominant wavelength values. In Van der Woerd and Wernand's [48] study, the generation of the dataset, hue values, and corresponding wavelengths involves a multi-step process. Initially, 500 simulated satellite remote sensing reflectance (Rrs) spectra are utilised, and corrected for Appl. ...
This research introduces an approach to visible spectroscopy leveraging image processing techniques and machine learning (ML) algorithms. The methodology involves calculating the hue value of an image and deriving the corresponding dominant wavelength. Initially, a six-degree polynomial regression supervised machine learning model is trained to establish a relationship between the hue values and dominant wavelengths. Subsequently, the ML model is employed to analyse the visible wavelengths emitted by various sources, including sodium vapour, neon lamps, mercury vapour, copper vapour lasers, and helium vapour. The performance of the proposed method is evaluated through error analysis, revealing remarkably low error percentages of 0.04%, 0.01%, 3.7%, 1%, and 0.07% for sodium vapour, neon lamp, copper vapour laser, and helium vapour, respectively. This approach offers a promising avenue for accurate and efficient visible spectroscopy, with potential applications in diverse fields such as material science, environmental monitoring, and biomedical research. This research presents a visible spectroscopy method harnessing image processing and machine learning algorithms. By calculating hue values and identifying dominant wavelengths, the approach demonstrates consistently low error rates across diverse light sources.
... The spectral properties of the FU classes were described in the works of Wernand, van der Woerd and Novoa [7,8], who reproduced the FU classes with homogenous methodologies and converted them into chromaticity coordinates. This technique allows the water colour to be objectively characterized from every spectral measurement of the water, taken with field or satellite radiometers [9,10]. In this case, the limitations of the FUI-based classification system are overcome by a more detailed characterization of water colour hues, carried out by expressing water colour through chromaticity variables such as the hue angle, the dominant wavelength or the purity. ...
... The van der Woerd and Wernand procedure ( [9,10]) was used to transform Rrs into Tristimulus X Y Z values, and then into chromaticity coordinates. The entire process was performed in RStudio, using a specific script adapted from the one by Yang [18]. ...
The water resource is severely compromised by climate change, and its availability and quality can no longer be taken for granted, even in places considered pristine, such as mountains. In this study, we evaluated the water colour variability of three artificial mountain lakes located in a relatively small basin (Western Italian Alps) at high elevations, and related this variability to the local climate conditions of the hydrological basin to which they belong. We estimated the dominant wavelength (DW) of lake water from Sentinel-2 acquisitions for the period 2017–2022, performing a chromaticity analysis. We correlated DW with climatic parameters recorded by two automated weather stations. Average DW varies from 497 nm of Serrù Lake and Agnel Lake to 512 nm of Lake Ceresole, where DW varies seasonally (variation of 75–100 nm in one year). During April–July, the DW of Lake Ceresole is significative correlated with air temperatures and snow cover (−0.8 and +0.8, respectively). During August–October, the relationship with temperature decreases to −0.5, and a correlation of 0.5 with the amount of rainfall appears. This work shows that mountain lake waters can exhibit variable quality (expressed here by water colour) in response to meteorological and hydrological conditions and events.
... Here, we generalized a water color index, the hue angle (α), as an important parameter to evaluate CDOM sources in lakes. The hue angle (α) classifies natural water bodies into 21 colors ranging from dark blue to yellow-brown according to the traditional Forel-Ule index (FUI), covering a wide range of optical properties of water bodies (Van der Woerd and Wernand, 2018;Wang et al., 2018). In recent years, the hue angle (α) has been shown to be an effective water color parameter, indicating the change in water composition, and this parameter can be obtained from high-precision multispectral satellite data (Chen et al., 2020;Chen et al., 2021;Van der Woerd and Wernand, 2018;Wang et al., 2020). ...
... The hue angle (α) classifies natural water bodies into 21 colors ranging from dark blue to yellow-brown according to the traditional Forel-Ule index (FUI), covering a wide range of optical properties of water bodies (Van der Woerd and Wernand, 2018;Wang et al., 2018). In recent years, the hue angle (α) has been shown to be an effective water color parameter, indicating the change in water composition, and this parameter can be obtained from high-precision multispectral satellite data (Chen et al., 2020;Chen et al., 2021;Van der Woerd and Wernand, 2018;Wang et al., 2020). Moreover, the Google Earth Engine (GEE) platform stores satellite images over long time series, providing an opportunity to assess CDOM sources on a large scale. ...
The source composition of chromophoric dissolved organic matter (CDOM) in lakes is closely related to regional environmental changes, human activities, and the carbon cycle. The spectral slope ratio (SR) is an important parameter of CDOM optical components, and combined with remote sensing technology, the source composition of CDOM can be tracked comprehensively and efficiently in large regions. Here, we proposed a CDOM source tracking remote sensing model (CDOM-SR) based on the hue angle (α) to assess the spatial pattern and long-term trend of the CDOM source composition in Chinese lakes (surface area ≥ 1 km2) from 1986 to 2021. Validation results show that the CDOM-SR model has a good SR estimation performance with a median absolute percentage difference, root mean square deviation, median ratio, and median deviation of 17.91%, 0.23, 1.02, and 0.03, respectively. We found that the average SR of Chinese lakes presents an obvious spatial pattern of high in the west and low in the east due to the difference in human activity intensity and the natural geographical environment. Additionally, we found that the average SR of Chinese lakes from 1986 to 2021 decreased at a rate of - 0.06/10 years, of which 64.37% of lakes decreased significantly, 15.42% of lakes had no significant change, and only 20.20% of lakes increased. The widespread decrease in the average SR indicates that the increasing human activity discharge of terrestrial organic matter has had an important impact on the source composition of the CDOM in Chinese lakes. Our results provide a new resource for remote sensing monitoring of CDOM sources and important insights into lake carbon cycling under the influence of ongoing human activities.
... Z tristimulus values according to the algorithm proposed by Van der Woerd and Wernand (2018). The steps used in this study are listed in Table S3. ...
... Recently, an algorithm based on multispectral information acquired from satellite sensors has been proposed to derive the hue angle, an indicator that can be used to determine the λ dom of a water body (i.e., the water colour) [62,63]. This indicator is called the Forel-Ule Index (FUI) and is derived from Remote Sensing Reflectances (Rrs). ...
... The FUI is not based on local retrieval algorithms; therefore, it can characterise natural waters easily and effectively [64,65]. FUI, still used today, is a benchmark standard in numerous studies [13,51,[65][66][67][68][69][70][71], and is characterised by having a relatively low uncertainty [62,63,66]. ...
... In addition, spectral signatures were examined for outliers within the water pixels. Once the ROIs were created, they were used to extract SPM concentration and Rrs values in the Visible domain, which were needed to obtain the λ dom (water colour) via the Forel-Ule index [63]. More information on the procedure to obtain the λ dom from Landsat and S2 images can be found in [63]. ...
Over the past half century, the demand for sand and gravel has led to extensive quarrying activities, creating many pit lakes (PLs) which now dot floodplains and urbanized regions globally. Despite the potential importance of these environments, systematic data on their location, morphology and water quality remain limited. In this study, we present an extensive assessment of the physical and optical properties in a large sample of PLs located in the Po River basin (Italy) from 1990 to 2021, utilizing a combined approach of remote sensing (Landsat constellation and Sentinel-2) and traditional limnological techniques. Specifically, we focused on the concentration of Suspended Particulate Matter (SPM) and the dominant wavelength (λdom, i.e., water colour). This study aims to contribute to the analysis of PLs at a basin scale as an opportunity for environmental rehabilitation and river floodplain management. ACOLITE v.2022, a neural network particularly suitable for the analysis of turbid waters and small inland water bodies, was used to atmospherically correct satellite images and to obtain SPM concentration maps and the λdom. The results show a very strong correlation between SPM concentrations obtained in situ and those obtained from satellite images, both for data derived from Landsat (R2 = 0.85) and Sentinel-2 images (R2 = 0.82). A strong correlation also emerged from the comparison of spectral signatures obtained in situ via WISP-3 and those derived from ACOLITE, especially in the visible spectrum (443–705 nm, SA = 10.8°). In general, it appeared that PLs with the highest mean SPM concentrations and the highest mean λdom are located along the main Po River, and more generally near rivers. The results also show that active PLs exhibit a poor water quality status, especially those of small sizes (<5 ha) and directly connected to a river. Seasonal comparison shows the same trend for both SPM concentration and λdom: higher values in winter gradually decreasing until spring–summer, then increasing again. Finally, it emerged that the end of quarrying activity led to a reduction in SPM concentration from a minimum of 43% to a maximum of 72%. In this context, the combined use of Landsat and Sentinel-2 imagery allowed for the evaluation of the temporal evolution of the physical and optical properties of the PLs in a vast area such as the Po River basin (74,000 km2). In particular, the Sentinel-2 images consistently proved to be a reliable resource for capturing episodic and recurring quarrying events and portraying the ever-changing dynamics of these ecosystems.
... The x and y two-dimensional information has been used to monitor water, vegetation, and other ground features [22], [23], [24]. In comparison to the (x, y) color representation, it has been recently deduced that the hue angle is easily utilized for water color monitoring [25], [26], [27], [28]. However, the application of vegetation remote sensing based on reflectance-based hue angle is rarely reported. ...
... According to (4)-(7), the hue-angle D65 calculated based on a continuous reflectance spectrum using the D65 illuminant data is considered the true value of the hue angle. Furthermore, several water color remote sensing studies [25], [26], [27] have shown that the hue angle was calculated based on ground measurements and satellite data using wavelength-independent light source data, which was referred to as hue-angle noD65 in this study. As a result, S λ in (4)- (7), which was used to calculate the hue-angle noD65 , was set to 1. ...
... Previous studies on the estimation of the plant chlorophyll status using color characteristics relied primarily on visible cameras or color meters in the laboratory and in the field [47], [48], [49]. Reflectance-based hue angle has been used in water color remote sensing [25], [26], [27]. However, the usage of standard illuminant data is not taken into consideration in this reflectance-based hue angle calculation, which contradicts the original design of the CIE XYZ color space. ...
Remote sensing of leaf chlorophyll content (LCC) is critical in precision agriculture, forest monitoring, and pest management. Chlorophyll absorbs light in the visible and red-edge spectrum areas. However, many multispectral sensors lack red-edge bands. Subsequently, the development of effective visible chlorophyll-related indicators is important. The hue angle defined in the CIE xy chromaticity diagram, which reflects the human eye’s color discrimination ability relying on visible light (400–700 nm), is introduced in this study for LCC estimation. This study proposes a new multispectral hue angle calculation method employing D65 standard illuminant data, investigates the effect of illuminant factors and band settings on hue angle calculation, and presents tristimulus weight coefficients for calculating MERIS, MSI, and ETM+ hue angles. Furthermore, the sensitivity of the link between LCC and different hue angles, as well as vegetation indices (VIs), is assessed in dense and sparse canopies. The results show that using wavelength-independent illuminant data or discrete bands overcalculates the reflectance-based hue angle. Moreover, our proposed hue angle calculated using D65 standard illuminant data is more sensitive to LCC than the existing hue angle using wavelength-independent illuminant data. In dense canopies, the multispectral hue angle has a stronger relationship with LCC than in sparse canopies. The multispectral hue angle generally appears weaker or similar to the red-edge VIs (MTCI and CI red-edge ), but stronger than the nonred-edge VIs (CIgreen, NDVI, NGRDI, and TGI). These findings imply that the multispectral hue angle calculated from visible bands provides the potential to monitor the LCC of large-scale plants.
... Similarly to the previous approach, an analysis of other parameters of camera settings is conducted. As evaluation involves a depth camera, a similar conditional analysis is applied to certain modeling aspects of the depth camera, such as the linear coefficient of the depth camera chromaticity values [28] and the minimum and maximum range of the depth camera, which typically represents a hardware specification for various commercially available depth cameras. The comparison of the model performance for gesture-related and camera-related parameters under the three range conditions is presented in Table 1. ...
Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by virtual 3D models. Our framework utilizes Unreal Engine to synthesize realistic hand gestures, offering customization options and reducing the risk of overfitting. Multiple variants, including gesture speed, performance, and hand shape, are generated to improve generalizability. In addition, we simulate different camera locations and types, such as RGB, infrared, and depth cameras, without incurring additional time and cost to obtain these cameras. Experimental results demonstrate that our proposed framework, SynthoGestures\footnote{\url{https://github.com/amrgomaaelhady/SynthoGestures}}, improves gesture recognition accuracy and can replace or augment real-hand datasets. By saving time and effort in the creation of the data set, our tool accelerates the development of gesture recognition systems for automotive applications.
... The FUI was calculated based on the CIE-XYZ standard color system proposed by the International Commission on Illumination (CIE), in which the X, Y, Z tristimulus values were first proposed. Sentinel-2 has five visible light bands, and the formula for calculating the X, Y, Z tristimulus values is Equation (3) [27]: ...
Guangzhou and Shenzhen are two core cities in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It is increasingly important to regulate water quality in urban development. The Forel–Ule Index (FUI) can be obtained by optical data and is an important indicator. Therefore, we used Sentinel-2 to calculate the FUI of 41 lakes and reservoirs in Guangzhou and Shenzhen from January to December in 2016–2021, and analyzed their spatio-temporal variations, including spatial distributions, seasonal variations, and inter-annual variations. We also performed a correlation analysis of driving factors. In Guangzhou, the FUI was low in the north and west, and high in the south and east. In Shenzhen, the FUI was high in the west and low in the east. Moreover, 68% of the lakes and reservoirs in Guangzhou exhibited seasonal variations, with a low FUI in summer and autumn, and high levels in spring and winter. Shenzhen had the lowest FUI in autumn. Furthermore, 36% of the lakes and reservoirs in Guangzhou exhibited increasing inter-annual variations, whereas Shenzhen exhibited stable and decreasing inter-annual variations. Among the 41 lakes and reservoirs analyzed herein, the FUI of 10 water areas were positively correlated with precipitation, while the FUI of 31 water areas were negatively correlated with precipitation. Increased precipitation leads to an increase in external pollutants and sediment, as well as the resuspension of substances in the water, resulting in more turbid water. Therefore, an increase in precipitation is positively correlated with the FUI, whereas a decrease in precipitation is negatively correlated with the FUI. These findings can be used to design suitable management policies to maintain and control the local water quality.
... The Forel-Ule (FU) scale consists of 21 colours and is used to visually identify optical water types [35][36][37]. The Forel-Ule Index (FUI) was derived from the FU scale using the X, Y, and Z tristimulus values in the chromaticity diagram, which correspond to red (X), green (Y), and blue (Z) in the visible light spectrum, based on camera or satellite images [36,38,39]. The FUI derived from Sentinel-3 has been used to map the extent of flood plumes, with FU values of 10 or above being representative of riverine-influenced areas (primary, secondary, and tertiary plumes) [40]. ...
Assessments of the water quality in coastal zones often rely on indirect indicators from contributing river inputs and the neighbouring ocean. Using a novel combination of distance accumulation cost methods and an ocean-colour product derived from SENTINEL-3 data, we developed a probabilistic method for the assessment of dissolved inorganic nitrogen (DIN) in Liverpool Bay (UK) for the period from 2017 to 2020. Using our approach, we showed the annual and monthly likelihood of DIN exposure from its 12 major contributory rivers. Furthermore, we generated monthly risk maps showing the probability of DIN exposure from all rivers, which revealed a seasonal variation of extent and location around the bay. The highest likelihood of high DIN exposure throughout the year was in the estuarine regions of the Dee, Mersey, and Ribble, along with near-shore areas along the north
Wales coast and around the mouth of the rivers Mersey and Ribble. There were seasonal changes in the risk of DIN exposure, and this risk remained high all year for the Mersey and Dee estuary regions. In contrast, for the mouth and near the coastal areas of the Ribble, the DIN exposure decreased in spring, remained low during the summer and early autumn, before displaying an increase during winter. Our approach offers the ability to assess the water quality within coastal zones without the need of complex hydrodynamic models, whilst still having the potential to apportion nutrient exposure to specific riverine inputs. This information can help to prioritise how direct mitigation strategies can be applied to specific river catchments, focusing the limited resources for coastal zone and river basin management.
... Van der Woerd et al. found that the hue angle α calculated by the multi-spectral channel exhibited a deviation ∆α of −5 • to 20 • , which was not entirely random and could be roughly described using a specific fifth-order equation for different satellite sensors [19]. For Sentinel-2 MSI, Equation (11) could be used for the approximate calculation of the hue angle deviation ∆α: ∆α = −61.805a 5 + 257.86a 4 − 300.67a 3 + 40.595a 2 + 65.296a − 9.3398 (12) where a is the hue angle α adjusted to be ranged from 0 • to 360 • and then divided by 100. ...
Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms.
