Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD)

Marine bio-optical properties applied to biogeochemical modelling

The seminal idea of optical oceanography is that by inspecting the colour of the ocean we can get a grasp on the biogeochemical composition of the water body. The field is used in many applications, ranging from ecology and biogeochemistry, to understanding the possible hazards in our oceans and the emerging trends of climate change. The term \u2018ocean colour\u2019 stems from the fact that the visible part of the spectrum is used by the ocean ecosystem for photosynthesis, which accounts for almost half of the global photosynthesis on Earth. The final goal of the thesis project is to improve the quality of Copernicus Marine Environment Monitoring Service (CMEMS) biogeochemical products for the Mediterranean Sea through the development of a new optical module for the MedBFM forecasting model system. CMEMS products quality assessment requires the comparison of model outputs with observations and the use of specific metrics. A quality-controlled bio-optical in-situ data set from the Biogeochemical-Argo Mediterranean floats network (BGC-Argo, with 4 radiometric, 2 physical and 1 biogeochemical variable) and remote sensing products from the Copernicus Marine Data Stream (inter-annually variable weekly data of diffuse attenuation coefficients of downward planar irradiance, Kd(var), at 490 nm) were used for such purpose. In both cases, the optical data (PAR profiles and Kd(490) maps respectively) served as model input for the MedBFM system (in 1- and 3-dimensional settings), whilst the biogeochemical data from BGC-Argo floats (fluorescence derived chlorophyll concentration profiles) and HPLC-obtained chlorophyll data from an openly accessible database were used for validation purposes. The work included two different MedBFM model configurations: firstly, in the form of a non-assimilative 1-dimensional model with various bio-optical and mixing parametrizations, where the former might serve both as a first step towards more complex optical representations and could on the other hand have a diagnostic utility by inspecting the product quality through the use of BGC-Argo floats. The combined use of a biogeochemical model of medium complexity) with a rich data set enabled also an in-depth study on the optics-related biogeochemical properties of the examined basin. The second configuration focused on the impact of using weekly variable Kd(var) versus climatological Kd(clim) values as a full 3-dimensional model optical forcing, thus estimating the effect of an updated data set in terms of spatio-temporal variability of the chlorophyll field and output quality. Such an integrated approach is useful as a first step towards the improvement of the new optical component of the 3-dimensional biogeochemical Mediterranean Sea model, striving towards the implementation of a hyperspectral radiative transfer model, which would present a fundamental upgrade to obtain a more accurate description of the underwater light field, impacting both biogeochemistry and hydrodynamics

Satellite indices of fluvial influence in coastal waters

In this dissertation a suite of satellite, discharge and in-situ measurements is used to explore the spatio-temporal distribution of terrestrial constituents in coastal environments. The covariance between riverine delivery of optically active constituents and the corresponding optical variability in neighboring coastal waters for the Mississippi and Orinoco Rivers is documented. From this work, satellite-based indices of fluvial influence (IFI), are developed which contain information on the spatial extent of riverine constituents in coastal waters. The IFI is defined as the correlation between time series of riverine discharge and an ocean color satellite-derived property in the region proximal to a river\u27s discharge point. These indices are employed to map the seasonal spatio-temporal variability of the Mississippi\u27s sediment plume in the presence of discharge and wind stress fields. Here it is shown that: (1) the IFI techniques are useful for tracking terrestrial constituents delivered by the Mississippi\u27s discharge; (2) coastal provinces dominated by fluvial influence have a different spatio-temporal distribution than those dominated by wind-driven resuspension, and thus these provinces can be discriminated, and (3) wind and discharge play different roles in the seasonal dynamics of the Mississippi\u27s plume. IFI techniques are used to isolate several individual river plumes in the northern Gulf of Mexico in which the relationship between in situ measurements of salinity and light absorption at 443nm were documented. Salinity vs. absorption relationships within the plumes were compared to modeled dissolved organic carbon fluxes from corresponding drainage basins. The results imply that variability in the flux of dissolved organic carbon across drainage basins imparts distinct variability in the optical characteristics of individual river plumes. Further, it is probable that ocean color sensors can help resolve this variability, and in fact have vast potential in aiding the understanding of the origin, persistence, trajectory and fate of riverine constituents in coastal waters

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Hydrogeomorphic controls on benthic light availability in rivers

Light is vital to the dynamics of aquatic ecosystems. It drives photosynthesis and photochemical reactions, affects thermal structure, and influences the behavior of aquatic biota. While the influence of hydrology and geomorphology on other ecosystem-limiting factors have been well studied (e.g., habitat, nutrient cycling), the more fundamental limitation of light availability has received much less attention. In this thesis, I analyzed and quantified the hydrogeomorphic controls on benthic (or riverbed) light availability using a combination of meta-analyses, field studies, laboratory studies, and model simulations. I developed a benthic light availability model (BLAM) that predicts photosynthetically active radiation (PAR) at the riverbed (Ebed) by calculating the amount of above-canopy PAR that is attenuated by all five hydrogeomorphic controls: topography, riparian vegetation, channel geometry, optical water quality, and hydrologic regime. This model was used to assess and characterize broad spatial patterns of Ebed and temporal variations associated with variable flow conditions for a wide range of rivers. BLAM was also used to assess the effects of riparian deforestation and degraded optical water quality associated with agriculturalization on Ebed. BLAM is the first model to quantify Ebed using all five hydrogeomorphic controls, and thus provides a new tool that can be used to investigate the role of light in river ecosystem dynamics and establish light availability targets in water resource management. BLAM also provides a framework for future models to characterize spatiotemporal variations of ultraviolet and infrared radiation in rivers

Development of Satellite-Assisted Forecasting System for Oyster Norovirus Outbreaks

Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. The overall goal of this study was to develop a satellite-assisted forecasting system for oyster norovirus outbreaks. The forecasting system is comprised of three components: (1) satellite algorithms for retrieval of environmental variables, including salinity, temperature, and gage height, (2) an Artificial Neural Network (ANN) based model, called NORF model, for predicting relative risk levels of oyster norovirus outbreaks, and (3) a mapping method for visualizing spatial distributions of norovirus outbreak risks in oyster harvest areas along Louisiana coast. The new satellite algorithms, characterized with linear correlation coefficient ranging from 0.7898 to 0.9076, make it possible to produce spatially distributed daily data with a high resolution (1 kilometer) for salinity, temperature, and gage height in coastal waters. Findings from this study suggest that oyster norovirus outbreaks are predictable, and in Louisiana oyster harvest areas, the NORF model predicted historical outbreaks from 1994 - 2014 without any confirmed false positive or false negative predictions when the estimated relative risk level was \u3e 0.6, while no outbreak occurred when the risk level was \u3c 0.5. However, more outbreak data are needed to confirm the threshold for norovirus outbreaks. Gage height and temperature were the most important environmental predictors of oyster norovirus outbreaks while wind, rainfall, and salinity also predicted norovirus outbreaks. The ability to predict oyster norovirus outbreaks at their onset makes it possible to prevent or at least reduce the risk of norovirus outbreaks by closing potentially affected oyster beds. By combining the NORF model with the remote sensing algorithms created in this dissertation, it is possible to map oyster norovirus outbreak risks in all oyster growing waters and particularly in the areas without direct measurements of relevant environmental variables, greatly expanding the coverage and enhancing the effectiveness of oyster monitoring programs. The hot spot (risk) maps, constructed using the methods developed in this dissertation, make it possible for oyster monitoring programs to manage oyster harvest waters more efficiently by focusing on hot spot areas with limited resources

Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida

The overall objective of this dissertation research is to understand the spatiotemporal dynamics of water quality parameters in different water bodies of South Florida. Two major approaches (multivariate statistical techniques and remote sensing) were used in this study. Multivariate statistical techniques include cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), discriminant analysis (DA), absolute principal component score-multiple linear regression (APCS-MLR) and PMF receptor modeling techniques were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, a 15-year (2000–2014) data set of 12 water quality variables, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. PCA/FA identified potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules, and causes were explained. The APCS-MLR and PMF models apportioned their contributions to each water quality variable. Also, the bio-physical parameters associated with the water quality of the two important water bodies of Lake Okeechobee and Florida Bay were investigated based on remotely sensed data. The principal objective of this part of the study is to monitor and assess the spatial and temporal changes of water quality using the application of integrated remote sensing, GIS data, and statistical techniques. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of a waterbody and observed data. The developed MLR models appeared to be promising for monitoring and predicting the spatiotemporal dynamics of optically active and inactive water quality characteristics in Lake Okeechobee and Florida Bay. It is believed that the results of this study could be very useful to local authorities for the control and management of pollution and better protection of water quality in the most important water bodies of South Florida

Remote sensing of phytoplankton biomass in oligotrophic and mesotrophic lakes: addressing estimation uncertainty through machine learning

Phytoplankton constitute the bottom of the aquatic food web, produce half of Earth’s oxygen and are part of the global carbon cycle. A measure of aquatic phytoplankton biomass therefore functions as a biological indicator of water status and quality. The abundance of phytoplankton in most lakes on Earth is low because they are weakly nourished (i.e., oligotrophic). It is practically infeasible to measure the millions of oligotrophic lakes on Earth through field sampling. Fortunately, phytoplankton universally contain the optically active pigment chlorophyll-a, which can be detected by optical sensors. Earth-orbiting satellite missions carry optical sensors that provide unparalleled high spatial coverage and temporal revisit frequency of lakes. However, when compared to waters with high nutrient loading (i.e., eutrophic), the remote sensing estimation of phytoplankton biomass in oligotrophic lakes is prone to high estimation uncertainties. Accurate retrieval of phytoplankton biomass is severely constrained by imperfect atmospheric correction, complicated inherent optical property (IOP) compositions, and limited model applicability. In order to address and reduce the current estimation uncertainties in phytoplankton remote sensing of low - moderate biomass lakes, machine learning is used in this thesis. In the first chapter the chlorophyll-a concentration (chla) estimation uncertainty from 13 chla algorithms is characterised. The uncertainty characterisation follows a two-step procedure: 1. estimation of chla from a representative dataset of field measurements and quantification of estimation uncertainty, 2. characterisation of chla estimation uncertainty. The results of this study show that estimation uncertainty across the dataset used in this chapter is high, whereby chla is both systematically under- and overestimated by the tested algorithms. Further, the characterisation reveals algorithm-specific causes of estimation uncertainty. The uncertainty sources for each of the tested algorithms are discussed and recommendations provided to improve the estimation capabilities. In the second chapter a novel machine learning algorithm for chla estimation is developed by combining Bayesian theory with Neural Networks (NNs). The resulting Bayesian Neural Networks (BNNs) are designed for the Ocean and Land Cover Instrument (OLCI) and MultiSpectral Imager (MSI) sensors aboard the Sentinel-3 and Sentinel-2 satellites, respectively. Unlike established chla algorithms, the BNNs provide a per-pixel uncertainty associated with estimated chla. Compared to reference chla algorithms, gains in chla estimation accuracy > 15% are achieved. Moreover, the quality of the provided BNN chla uncertainty is analysed. For most observations (> 75%) the BNN uncertainty estimate covers the reference in situ chla value, but the uncertainty calibration is not constantly accurate across several assessment strategies. The BNNs are applied to OLCI and MSI products to generate chla and uncertainty estimates in lakes from Africa, Canada, Europe and New Zealand. The BNN uncertainty estimate is furthermore used to deal with uncertainty introduced by prior atmospheric correction algorithms, adjacency affects and complex optical property compositions. The third chapter focuses on the estimation of lake biomass in terms of trophic status (TS). TS is conventionally estimated through chla. However, the remote sensing of chla, as shown in the two previous chapters, can be prone to high uncertainty. Therefore, in this chapter an algorithm for the direct classification of TS is designed. Instead of using a single algorithm for TS estimation, multiple individual algorithms are ensembled through stacking, whose estimates are evaluated by a higher-level meta-learner. The results of this ensemble scheme are compared to conventional switching of reference chla algorithms through optical water types (OWTs). The results show that estimation of TS is increased through direct classification rather than indirect estimation through chla. The designed meta-learning algorithm outperforms OWT switching of chla algorithms by 5-12%. Highest TS estimation accuracy is achieved for high biomass waters, whereas for low biomass waters extremely turbid waters produced high TS estimation uncertainty. Combining an ensemble of algorithms through a meta-learner represents a solution for the problem of algorithm selection across the large variation of global lake constituent concentrations and optical properties