Water Constituent Concentrations Research Articles

Developing a novel index for detection of optically shallow waters using multispectral satellite imagery and radiative transfer modelling

ABSTRACT Optical remote sensing of water quality has been problematic due to contamination of remote sensing observations by the sea-bottom effect over complex shallow or very clear waters. This has resulted in providing misleading information on the estimation of Water Constituent Concentrations (WCCs) retrieved from satellite images. In this research, we used Radiative Transfer (RT) Modelling to develop a simple and innovative index named the Sea-Bottom Effect Index (SEBEI) to readily determine water pixels contaminated by the sea-bottom effect, (hereafter named Optically Shallow Waters, (OSWs)) from remote sensing observations. To define the SEBEI, we initially assessed the influence of the sea-bottom on simulated water-leaving reflectance (R rs ) using RT Water-Sea-Bottom (WSB) modelling. This evaluation encompassed a range of water depths, seabed albedos, and WCCs (i.e. Chlorophyll-a (Chla) and Suspended Particulate Matter (SPM)). Next, we detected the most sensitive wavelengths (i.e. 750 nm, 810 nm, and 900 nm) to the seabed contribution on the simulated R rs spectra at the water surface level and developed the SEBEI. We validated the accuracy of the proposed SEBEI over a time series of MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) images captured during low and high tidal phases over shallow waters of the Dutch Wadden Sea, the Netherlands. The results indicate that the SEBEI effectively distinguishes between OSWs and Optically Deep Waters (ODWs) using satellite images (with an R2 value of ≥ 0.97 and a Root Mean Square Error (RMSE) of ≤ 0.65). The SEBEI can serve as an intermediate solution for detecting OSWs in multispectral and hyperspectral satellite images worldwide. It eliminates the need for ancillary datasets like bathymetry maps and significantly enhances the reliability of WCC maps produced over complex shallow coastal waters.

Holistic optical water type classification for ocean, coastal, and inland waters

AbstractWater constituents exhibit diverse optical properties across ocean, coastal, and inland waters, which alter their remote‐sensing reflectance obtained via satellites. Optical water type (OWT) classifications utilized in satellite data processing aim to mitigate optical complexity by identifying fitting ocean color algorithms tailored to each water type. This facilitates comprehension of biogeochemical cycles ranging from local to global scales. Previous OWT frameworks have focused narrowly on either oceanic or inland waters and have relied too heavily on specific data collections. We propose a novel holistic OWT framework applicable to all natural waters, based on state‐of‐the‐art bio‐geo‐optical modeling and radiative transfer simulations that encompass different phytoplankton groups. This framework employs a “knowledge‐driven” paradigm, combining domain knowledge and insights from previous studies to simulate the reflectance spectrum from water constituent concentrations and inherent optical properties. Our method extracts optical variables to represent the full spectrum of reflectance, consolidating both spectral shape and magnitude. We apply the framework utilizing diverse in situ, synthetic, and satellite data (Sentinel‐3 OLCI) and demonstrate its better classifiability than other frameworks. This framework lays the foundation for comprehensive global monitoring of natural waters.

Understanding the Mechanisms and Implications of the First Flush in Mine Pools: Insights from Field Studies in Europe’s Deepest Metal Mine and Analogue Modelling

The chemical composition of mine water discharged from flooded underground mines typically improves over time. This phenomenon is called first flush and can be described by a characteristic curve. Shortly after the mine water begins to discharge, water constituent concentrations rise and then fall in an almost exponential curve, improving water quality over time. In this study, the change in mine water quality was investigated throughout the mine water body. This mine water body commonly consists of different water bodies with individual densities, resulting in mine water stratification. Anthropogenic disturbance of the mine water body can disrupt this stratification and also the positive effect of the first flush. To better understand and predict the first flush, the first flush was simulated experimentally using an analogue model of a flooded underground mine, the Agricola Model Mine. The results were compared with field studies to help understanding and predicting the change in mine water quality. Overall, the results suggest that the first flush occurs throughout the mine water body, making it similar to a chemical reactor. This better understanding of the process can lead to more effective mine water management and design of mine water treatment facilities.Graphical

Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions

Estimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ∼8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.

Water quality parameters and constituent concentrations measured in the Peel and Arctic Red Rivers, 2007–2010

Outflow from north-flowing circumpolar rivers has a strong influence on the Arctic Ocean. The Peel and Arctic Red Rivers are tributaries of the Mackenzie Delta, a large, lake-rich floodplain that forms the interface between the Mackenzie River and the Beaufort Sea basin of the Arctic Ocean. Here, we present water quality data that were collected from the Peel and Arctic Red Rivers between 2007 and 2010 as part of an International Polar Year project that investigated the seasonal hydrology and biogeochemistry of the Mackenzie River and its delta. The Peel River was sampled 57 times between May 2007 and September 2010 upstream of the community of Fort McPherson, Northwest Territories (NT), while the Arctic Red River was sampled 32 times between May 2007 and August 2008 (with one additional sample in June 2010) approximately 0.5 km upstream of its confluence with the Mackenzie River near the community of Tsiigehtchic, NT. Each water sample was analyzed for up to 22 water quality parameters, including water temperature, specific conductivity, pH, chlorophyll a, total suspended sediments, particulate nutrients (carbon, nitrogen, and phosphorus), soluble reactive silica, major ions (calcium, magnesium, potassium, sodium, chloride, and sulfate), dissolved carbon (inorganic and organic fractions), and dissolved nutrients (three nitrogen and two phosphorus fractions). This data set, which is available for download and reuse, provides important baseline information about water quality in the Peel and Arctic Red Rivers, complements other data that have been collected in these watersheds, and will be of interest to researchers, resource managers, Indigenous organizations, and governments that are active in the region.

Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs

Remote sensing product uncertainties for phytoplankton chlorophyll-a (chla) concentration in oligotrophic and mesotrophic lakes and reservoirs were characterised across 13 existing algorithms using an in situ dataset of water constituent concentrations, inherent optical properties (IOPs) and remote-sensing reflectance spectra Rrsλ collected from 53 lakes and reservoirs (346 observations; chla concentration < 10 mg m-3, dataset median 2.5 mg m-3). Substantial shortcomings in retrieval accuracy were evident with median absolute percentage differences (MAPD) > 37% and mean absolute differences (MAD) > 1.82 mg m-3. Using the Hyperspectral Imager for the Coastal Ocean (HICO) band configuration improved the accuracies by 10–20% compared to the Ocean and Land Colour Instrument (OLCI) configuration. Retrieval uncertainties were attributed to optical and biogeochemical properties using machine learning models through SHapley Additive exPlanations (SHAP). The chla retrieval uncertainty of most semi-analytical algorithms was primarily determined by phytoplankton absorption and composition. Machine learning chla algorithms showed relatively high sensitivity to light absorption by coloured dissolved organic matter (CDOM) and non-algal pigment particulates (NAP). In contrast, the uncertainties of red/near-infrared algorithms, which aim for lower uncertainty in the presence of CDOM and NAP, were primarily explained through the total absorption by phytoplankton at 673 nm (aϕ(673)) and variables related to backscatter. Based on these uncertainty characterisations we discuss the suitability of the evaluated algorithm formulations, and we make recommendations for chla estimation improvements in oligo- and mesotrophic lakes and reservoirs.

Inherent optical properties of suspended particulate matter in the southern Baltic Sea in relation to the concentration, composition and characteristics of the particle size distribution; new forms of multicomponent parameterizations of optical properties

This paper reports on detailed studies of the relationship between the inherent optical properties (IOPs) of suspended particulate matter populations occurring in southern Baltic Sea waters, and the basic biogeochemical and morphological quantities enabling these populations to be generally characterized. Specifically, these IOPs are the spectral absorption coefficients by all particulate matter, phytoplankton pigments and detritus, the spectral scattering and backscattering coefficients by particles, and the backscattering ratio. The biogeochemical properties of suspended matter populations are characterized by measuring concentrations of suspended particulate matter (SPM), particulate organic matter (POM) and particulate inorganic matter (PIM), as well as concentrations of various phytoplankton pigments, including chlorophyll a (Chla). We also take particle size distributions (PSDs) into account. This diverse empirical material is used to describe how the typical relationships between the IOPs of suspended matter and the concentrations of the main water constituents (SPM or Chla) can be influenced by differences in the general composition and size distributions of the suspended matter. We statistically describe the possible impact on these relationships of the variability of composition ratios, such as POM/SPM and Chla/SPM, as well as the impact of the variability of certain characteristics calculated on the basis of PSDs: the average particle diameter weighted by the particle's projected area (DA), the average apparent density of particles (ρa), the average intracellular concentration of chlorophyll a (ci), and the corresponding products of these quantities. We also demonstrate the significant variability of the specific IOPs (SIOPs) by attempting to determine them in the classical way for our southern Baltic data, and we give a possible interpretation of this variability. On the basis of these analyses, we derive new, multicomponent IOP parameterizations that are functions of several variables. These new parameterizations enable the IOPs of suspended matter to be characterized and predicted with better accuracy than with the often-used standard one-component relationships.

Combinations of multivariate statistical analysis and analytical hierarchical process for indexing surface water quality under arid conditions

Novel methods for water quality indexing increase insight into the fitness of water bodies for different uses. We hypothesized that integrating multivariate statistical analysis (MSA) with the analytical hierarchical process (AHP) may provide a reliable estimation of water quality status. Hence, twenty water samples from canals and drains in the northern Nile Delta, Egypt were collected during summer, autumn, winter, and spring and analyzed. Data were subjected to MSA, including correlation analysis, principal component analysis (PCA), and hierarchal cluster analysis (HCA). The AHP was applied to derive weights of parameters implied in developing water quality indices for irrigation (IWQI) and fish farming (FFWQI). Human health risks due to exposure to potentially toxic elements (PTEs) via dermal contact were also considered. The average concentrations of water constituents were acceptable for irrigation, except sodium adsorption ratio (SAR) and Cl−. The dissolved oxygen, total dissolved solids, Cl−, NO2-N, NO3-N, NH3, and PTEs (except Zn) did not meet standard limits for fish production. The MSA revealed that water contamination resulted from human activities (agriculture, industry, and domestic wastes) and hydrochemical processes. The PCA indicated that SAR, Cu, and pH could adequately represent water quality for irrigation, while temperature, NO2-N, Cr, and Zn could reflect fish farming requirements. The AHP provided consistent weights for the original and shortlisted parameters. The water quality varied from good to poor for irrigation and from excellent to low for fish farming. The minimum IWQI could adequately represent the IWQI (R2 = 0.83) and thus reduce the time, effort, and cost for monitoring water quality. However, the minimum FFWQI showed moderate consistency (R2 = 0.51) with FFWQI, implying that increasing the sampling size is essential for better performance. The hazard quotient of all PTEs was below 1.0 for both adults and children, indicating a safe limit. The potential cancer risk was acceptable (1.36E-06) for adults and safe (8.03E-07) for children. Results of this work would be a start point for efficient quality control programs in arid regions.

Improved hyperspectral inversion of aquatic reflectance under non-uniform vertical mixing.

Estimating the concentration of water constituents by optical remote sensing assumes absorption and scattering processes to be uniform over the observation depth. Using hyperspectral reflectance, we present a method to direct the retrieval of the backscattering coefficient (bb(λ)) from reflectance (> 600 nm) towards wavebands where absorption by water dominates the reflectance curve. Two experiments demonstrate the impact of hyperspectral inversion in the selected band set. First, optical simulations show that the resulting distribution of bb(λ) is sensitive to particle mixing conditions, although a robust indicator of non-uniformity was not found for all scenarios of stratification. Second, in the absence of spectral backscattering profiles from in situ data sets, it is shown how substituting the median of bb(λ) into a near infra-red / red band ratio algorithm improved chlorophyll-a estimates (root mean square error 75.45 mg m-3 became 44.13 mg m-3). This approach also allows propagation of the uncertainty in bb estimates to water constituent concentrations.

Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan

Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality.

Biogeochemical causes of sixty-year trends and seasonal variations of river water properties in a large European basin

We evaluated long-term trends and seasonal variations in the major physical–chemical properties of the circum-neutral Slapy reservoir (Vltava, Czech Republic) from 1960 to 2019. Mean annual water temperature increased by 2.1 °C, flow maxima shifted by ~ 13 days from the early April to mid-March, and the onset of thermal stratification of water column and spring algal peaks advanced by 19 and 21 days, respectively, due to climate warming. Concentrations of major ions, phosphorus (P), and chlorophyll increased from the 1960s to the 1990–2000s, then decreased due to changing agricultural practices and legislation, intensified wastewater treatment, and decreasing atmospheric pollution. Concentrations of dissolved organic carbon (DOC) decreased from 1960 to the 1990s due to improved wastewater treatment, then began to increase in response to climate change and reduced acidic deposition. Concentrations of water constituents exhibited varying individual long-term and seasonal patterns due to the differing effects of following major processes on their production/removal in the catchment-river system: (1) applications of synthetic fertilizers, liming and farmland draining (NO3−, SO42−, Cl−, Ca2+, Mg2+, K+, and HCO3−), (2) wastewater production and treatment (DOC, P, N forms), (3) road de-icing (Cl− and Na+), (4) atmospheric pollution (SO42−), (5) climate change (DOC), and (6) the aging of reservoirs (NH4+). The water pH increased until the early 1990s, then decreased and exhibited pronounced seasonal variations, integrating the effects of changing external acidity sources and in-lake H+ sources and sinks (i.e., microbial CO2 production/consumption and availability and transformations of inorganic N), and changes in water buffering capacity. Anthropogenic and climatic effects, reservoir aging, and changes in water eutrophication thus may significantly affect water pH also in circum-neutral systems.

Water quality dynamics of floodplain lakes in relation to river flooding and cattle grazing

Floodplain water quality is naturally regulated by river floods, but is increasingly affected by anthropogenic activities. Accordingly, we evaluated the combined influence of river flooding (pre- and post-flood periods) and rotational grazing (cattle presence or absence) on water quality of floodplain lakes. We hypothesised that river floods decrease the concentrations of most water constituents and dilute grazing effects on water quality, and that differences in cattle pressure (i.e. grazing duration) promote differences in water quality among lakes. Nutrient concentration decreased after the flood through washing-out and dilution, but other water constituents showed different responses. Contrary to expectations, water-quality changes in relation to cattle presence or absence occurred only after the flood, with higher turbidity and phosphorus during periods with than without cattle. Finally, water-quality variability among lakes was attributed to their different sizes (and therefore dilution capacities), but not to differences in cattle pressure. Our results suggest that river–floodplain connectivity and grazing location in areas with large lakes are important for preserving water quality. Pre-flood changes in water quality in relation to cattle presence or absence could have been obscured by wind-driven sediment resuspension, which has important effects during shallow-water stages, and by high phosphorus concentration, which can prevent removal during periods without cattle.

Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas

Recently, there have been significant efforts in the integration of in-situ and satellite observations for effective monitoring of coastal areas (e.g., the Copernicus program of the European Space Agency). In this study, a 15-year diurnal variation of Water Constituent Concentrations (WCCs) was retrieved from multi-sensor satellite images and in-situ hyperspectral measurements using Radiative Transfer (RT) modeling in the Dutch Wadden Sea. The existing RT model 2SeaColor was inverted against time series of in-situ hyperspectral measurements of water leaving reflectances (Rrs [sr−1]) for the simultaneous retrieval of WCCs (i.e., Chlorophyll-a (Chla), Suspended Particulate Matter (SPM), Dissolved Organic Matter (CDOM)) on a daily basis between 2003 and 2018 at the NIOZ jetty station (the NJS) located in the Dutch part of the Wadden Sea. At the same time, the existing coupled atmosphere-hydro-optical RT model MOD2SEA was used for the simultaneous retrieval of WCCs from time series of multi-sensor satellite images of the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, Multispectral Instrument (MSI) onboard Sentinel-2 and Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 between 2003 and 2018 over the Dutch Wadden Sea. At the NJS, a direct comparison (Taylor diagram and statistical analysis) showed strong agreement between in-situ and satellite-derived WCC values (Chla: R2 ≥ 0.70, RMSE ≤7.5 [mg m−3]; SPM: R2 ≥ 0.72, RMSE ≤5.5 [g m−3]; CDOM absorption at 440 nm: R2 ≥ 0.67, RMSE ≤1.7 [m−1]). Next, the plausibility of the spatial variation of retrieved WCCs over the study area was evaluated by generating maps of Chla [mg m−3], SPM [g m−3], and CDOM absorption at 440 nm [m−1] from MERIS and OLCI images using the MOD2SEA model. The integration of the spatio-temporal WCC data obtained from in-situ measurements and satellite images in this study finds applications for the detection of anomaly events and serves as a warning for management actions in the complex coastal waters of the Wadden Sea.

The impact of sea bottom effects on the retrieval of water constituent concentrations from MERIS and OLCI images in shallow tidal waters supported by radiative transfer modeling

Many coastal waters include large areas of Optically Shallow Waters (OSWs) where the sea-bottom affects above-water observations of remote sensing reflectance (Rrs [sr−1]). If not treated, the effect of bottom reflectance will interfere with the correct retrieval of Water Constituent Concentrations (WCCs) from hyperspectral and multispectral remote sensing observations. To study this phenomenon in more detail, the existing semi-infinite 2SeaColour Radiative Transfer (RT) model was modified into a finite water layer model, bounded by a diffusely reflecting surface at the sea-bottom. From simulations with the new model, called Water - Sea Bottom (WSB) model, it was observed that a ratio of spectral bands in the Near-Infrared, bands 750 nm and 900 nm, is nearly insensitive to the WCCs and increases with the shallowness of the water, and therefore can be used as a robust index to detect OSWs. The newly established Near-Infrared Bottom Effect Index (NIBEI) was applied to a series of satellite observations over the Wadden Sea during high and low tidal phases. Images from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI) were processed to retrieve WCCs of the study area. The results indicate that the sea-bottom effect in OSWs affects the accuracy of atmospheric correction and retrievals. On the other hand, applying the NIBEI to flag OSWs improves the reliability and consistency of WCCs maps. The application of proposed NIBEI on satellite images requires only Top Of Atmosphere (TOA) radiances at 750 nm and 900 nm and does not depend on atmospheric correction and ancillary local input data (e.g., bathymetry map, bottom type, empirical coefficients, in-situ measurements). As a result, the proposed NIBEI can readily be applied to detect OSWs on various ocean colour remote sensors in various shallow coastal regions.

Impact of sampling techniques on the concentration of ammonia and sulfide in pore water of marine sediments

AbstractThree ex situ pore water sampling procedures (I – rhizon samplers, II – centrifugation of sediment subsamples collected from different sediment depths without core sectioning, III – core sectioning and centrifugation of sediment sections) were compared to indicate factors that may affect concentrations of pore water constituents (ammonia and sulfides). The methods were selected and modified in such a way as to determine how the concentrations are affected by different factors related to sampling procedures, e.g. contact with atmospheric air, filtration and sediment core disturbance. They were tested on nine sediment cores collected at one site in the southern Baltic Sea. The concentration of ammonia in pore water from centrifuged sediment sections was significantly higher compared to pore water extracted by rhizons – probably due to the impact of changing pH. The factor with the greatest impact on the H2S/HS–concentration in the analyzed pore water was the contact with atmospheric air and/or the extrusion of sediments from a core liner. Rhizons proved to be the best option for sampling pore waters analyzed for H2S/HS–and NH4+/NH3. In the case of H2S/HS–we noticed the smallest loss of the analyzed constituents. For ammonia, the centrifugation of the whole sediment sections was likely to cause interferences in the indophenol blue method.

Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery

The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During the algal bloom period, the optical properties of water are extremely heterogeneous and vary on scales of tens of meters. Additionally, they vary in time under the influence of currents and wind forcing. In this case, the usage of the traditional station-based sampling to describe the state of the reservoir may be uninformative and not rational. Therefore, we proposed an original approach based on simultaneous in situ measurements of the remote sensing reflectance by a single radiometer and the concentration of water constituents by an ultraviolet fluorescence LiDAR from a high-speed gliding motorboat. This approach provided fast data collection including 4087 synchronized LiDAR and radiometric measurements with high spatial resolutions of 8 m for two hours. A part of the dataset was coincided with Sentinel-2 overpass and used for the development of regional algorithms for the retrieval of Chl a and TSM concentrations. For inland waters of the Russian Federation, such research was performed for the first time. The proposed algorithms can be used for regular environmental monitoring of the Gorky Reservoir using ship measurements or Sentinel-2 images. Additionally, they can be adapted for neighboring reservoirs, for example, for other seven reservoirs on the Volga River. Moreover, the proposed ship measurement approach can be useful in the practice of limnological monitoring of inland freshwater ecosystems with high spatiotemporal variability of the optical properties.

Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data.

Drinking water monitoring is essential for identifying health-related risks, as well as for building foundations for management of safe drinking water supplies. However, statistical analyses of drinking water quality monitoring data are challenging because of non-normal (skewed distributions) and missing values. Therefore, a new method combining a water quality index (WQI) with spatial analysis is introduced in this paper to fill the gap between data collection and data analysis. Water constituent concentrations in different seasons and from different water sources were compared based on WQIs. To generate a WQI map covering all of the study areas, predicted WQI values were created for locations in the study area based on spatial interpolation from nearby observed values. The accuracy value of predicted and measured values of our method was 0.99, indicating good predication performance. Overall, the results of this study indicate that this method will help fill the gap between the collection of large amounts of drinking water data and data analysis for drinking water monitoring and process control.

Reconstruction of Water Hyperspectral Remote Sensing Reflectance Based on Sparse Representation and Its Application

Multispectral satellite sensors have several limitations with respect to capturing the target's spectral information due to their band setting and number of bands. The hyperspectral reconstruction technique is an effective method to obtain hyperspectral information from multispectral data. In this study, we propose a hyperspectral reconstruction algorithm based on the sparse representation of water remote sensing reflectance. The proposed algorithm was validated for five ocean color sensors (Sentinel-2A MSI, MERIS, MODIS Aqua, GOCI, and ⅦRS) using in situ measured above-water remote sensing reflectance. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of the reconstructed and measured spectra for five ocean color sensors were less than 10% and 0.005 sr-1, respectively. Compared with the spectra reconstruction algorithm based on multi-variable linear regression, the proposed algorithm can obtain the features of complex water remote sensing reflectance without using in situ-measured reflectance for algorithm tuning. In addition, the accuracy of the proposed algorithm is better than the spectra reconstruction algorithm based on multi-variable linear regression. Two spectra reconstruction algorithms were applied to five ocean color sensors to test the applicability of the remotely estimated water constituent concentration. The statistical results for the reconstructed spectral factors and in situ water constituent concentration suggest that the reconstructed reflectance derived by the proposed algorithm has a performance similar to that of in situ-measured hyperspectral reflectance. The reconstructed reflectance derived by the proposed algorithm performs better than the spectra reconstruction algorithm based on multi-variable linear regression. Finally, the proposed algorithm was applied to GOCI data to remotely estimate the chlorophyll-a and total suspended matter concentrations. The accuracy of the water constituent concentration estimated from reconstructed images is better than that using original multispectral images. For the estimation of the chlorophyll-a concentration, the MAPE improved from 80.6% to 51.5% and the RMSE improved from 12.175 μg·L-1 to 7.125 μg·L-1. For the estimation of total suspended matter, the MAPE improved from 19.1% to 18.8% and the RMSE improved from 29.048 mg·L-1 to 28.596 mg·L-1.

Remote Sensing of Coral Reefs: Uncertainty in the Detection of Benthic Cover, Depth, and Water Constituents Imposed by Sensor Noise

Coral reefs are biologically diverse and economically important ecosystems that are on the decline worldwide in response to direct human impacts and climate change. Ocean color remote sensing has proven to be an important tool in coral reef research and monitoring. Remote sensing data quality is driven by factors related to sensor design and environmental variability. This work explored the impact of sensor noise, defined as the signal to noise ratio (SNR), on the detection uncertainty of key coral reef ecological properties (bottom depth, benthic cover, and water quality) in the absence of environmental uncertainties. A radiative transfer model for a shallow reef environment was developed and Monte Carlo methods were employed to identify the range in environmental conditions that are spectrally indistinguishable from true conditions as a function of SNR. The spectrally averaged difference between remotely sensed radiance relative to sensor noise, ε, was used to quantify uncertainty in bottom depth, the fraction of benthic cover by coral, algae, and uncolonized sand, and the concentration of water constituents defined as chlorophyll, dissolved organic matter, and suspended calcite particles. Parameter uncertainty was found to increase with sensor noise (decreasing SNR) but the impact was non-linear. The rate of change in uncertainty per incremental change in SNR was greatest for SNR &lt; 500 and increasing SNR further to 1000 resulted in only modest improvements. Parameter uncertainty was complicated by the bottom depth and benthic cover. Benthic cover uncertainty increased with bottom depth, but water constituent uncertainty changed inversely with bottom depth. Furthermore, water constituent uncertainty was impacted by the type of constituent material in relation to the type of benthic cover. Uncertainty associated with chlorophyll concentration and dissolved organic matter increased when the benthic cover was coral and/or benthic algae while uncertainty in the concentration of suspended calcite increased when the benthic cover was uncolonized sand. While the definition of an optimal SNR is subject to user needs, we propose that SNR of approximately 500 (relative to 5% Earth surface reflectance and a clear maritime atmosphere) is a reasonable engineering goal for a future satellite sensor to support research and management activities directed at coral reef ecology and, more generally, shallow aquatic ecosystems.

Remote sensing of water constituent concentrations using time series of in-situ hyperspectral measurements in the Wadden Sea

This study aimed to investigate the capability of the two-stream radiative transfer model 2SeaColor for the simultaneous retrieval of Chlorophyll-a (Chla), Suspended Particulate Matter (SPM) and Colored Dissolved Organic Matter (CDOM) concentrations from remote sensing measurements under various conditions (i.e., solar zenith angle values (SZAs) and water turbidity levels). For this evaluation, a time series of diurnal in-situ hyperspectral measurements of remote sensing reflectance (Rrs) concurrent with in-situ measured Chla and SPM concentrations between 2008 and 2010 by the NIOZ jetty station (the NJS), located in the Dutch part of the Wadden Sea, was used. Validation of the model retrievals against in-situ measurements showed an acceptable accuracy (Chla: R2 = 0.80 and RMSE = 2.98 [mg m−3]; SPM: R2 = 0.89 and RMSE = 2.53 [g m−3]) with good agreement between the temporal trends of measured and retrieved concentration values over multiple years. However, the model inversion results yielded less good estimates at SZA > 60° during winter. Furthermore, the effect of the tide on the variation of daily time series of Chla and SPM concentrations was analyzed. At the particular NJS location, the tidal effects on the concentrations of SPM and Chla were found to be small. The capability of the 2SeaColor model to retrieve reliable estimates, and the favorable location of the NJS, which is little influenced by tidal phase variations, contribute to a better understanding of the long-term variability of Chla and SPM concentrations. The results of this study may support the ongoing efforts on Sentinel-3 Ocean and Land Color Instrument (OLCI) calibration and validation at the Dutch Wadden Sea.