Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads

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

Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature

Freshwater accounts for just 2.5% of the earth’s water resources, and its quality and availability are becoming an issue of global concern in the 21st century. Growing human population, over-exploitation of water sources and pressures of global warming mean that both water quantity and quality are affected. In order to effectively manage water quality there is a need for increased monitoring and predictive modelling of freshwater resources. To address these concerns in New Zealand inland waters, an approach which integrates biological and physical sciences is needed. Remote sensing has the potential to allow this integration and vastly increase the temporal and spatial resolution of current monitoring techniques, which typically involve collecting grab-samples. In a complementary way, lake modelling has the potential to enable more effective management of water resources by testing the effectiveness of a range of possible management scenarios prior to implementation. Together, the combination of remote sensing and modelling data allows for improved model initialisation, calibration and validation, which ultimately aid in understanding of complex lake ecosystem processes. This study investigated the use of remote sensing using empirical and semi-analytical algorithms for the retrieval of chlorophyll a (chl a), tripton, suspended minerals (SM), total suspended sediment (SS) and water surface temperature. It demonstrated the use of spatially resolved statistical techniques for comparing satellite estimated and 3-D simulated water quality and temperature. An automated procedure was developed for retrieval of chl a from Landsat Enhanced Thematic Mapper (ETM+) imagery, using 106 satellite images captured from 1999 to 2011. Radiative transfer-based atmospheric correction was applied to images using the Second Simulation of the Satellite in the Solar Spectrum model (6sv). For the estimation of chl a over a time series of images, the use of symbolic regression resulted in a significant improvement in the precision of chl a hindcasts compared with traditional regression equations. Results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a. Bio-optical models were applied to quantify the physical processes responsible for the relationship between chl a concentrations and subsurface irradiance reflectance used in regression algorithms, allowing the identification of possible sources of error in chl a estimation. While the symbolic regression model was more accurate than traditional empirical models, it was still susceptible to errors in optically complex waters such as Lake Rotorua, due to the effect of variations of SS and CDOM on reflectance. Atmospheric correction of Landsat 7 ETM+ thermal data was carried out for the purpose of retrieval of lake water surface temperature in Rotorua lakes, and Lake Taupo, North Island, New Zealand. Atmospheric correction was repeated using four sources of atmospheric profile data as input to a radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN) v.3.7. The retrieved water temperatures from 14 images between 2007 and 2009 were validated using a high-frequency temperature sensor deployed from a mid-lake monitoring buoy at the water surface of Lake Rotorua. The most accurate temperature estimation for Lake Rotorua was with radiosonde data as an input into MODTRAN, followed by Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2, Atmospheric Infrared Sounder (AIRS) Level 3, and NASA data. Retrieved surface water temperature was used for assessing spatial heterogeneity of surface water temperature simulated with a three-dimensional (3-D) hydrodynamic model (ELCOM) of Lake Rotoehu, located approximately 20 km east of Lake Rotorua. This comparison demonstrated that simulations reproduced the dominant horizontal variations in surface water temperature in the lake. The transport and mixing of a geothermal inflow and basin-scale circulation patterns were inferred from thermal distributions from satellite and model estimations of surface water temperature and a spatially resolved statistical evaluation was used to validate simulations. This study has demonstrated the potential of accurate satellite-based thermal monitoring to validate water surface temperature simulated by 3-D hydrodynamic models. Semi-analytical and empirical algorithms were derived to determine spatial and temporal variations in SS in Lake Ellesmere, South Island, New Zealand, using MODIS band 1. The semi-analytical model and empirical model had a similar level of precision in SS estimation, however, the semi-analytical model has the advantage of being applicable to different satellite sensors, spatial locations, and SS concentration ranges. The estimations of SS concentration (and estimated SM concentration) from the semi-analytical model were used for a spatially resolved validation of simulations of SM derived from ELCOM-CAEDYM. Visual comparisons were compared with spatially-resolved statistical techniques. The spatial statistics derived from the Map Comparison Kit allowed a non-subjective and quantitative method to rank simulation performance on different dates. The visual and statistical comparison between satellite estimated and model simulated SM showed that the model did not perform well in reproducing both basin-scale and fine-scale spatial variation in SM derived from MODIS satellite imagery. Application of the semi-analytical model to estimate SS over the lifetime of the MODIS sensor will greatly extend its spatial and temporal coverage for historical monitoring purposes, and provide a tool to validate SM simulated by 1-D and 3-D models on a daily basis. A bio-optical model was developed to derive chl a, SS concentrations, and coloured dissolved organic matter /detritus absorption at 443 nm, from MODIS Aqua subsurface remote sensing reflectance of Lake Taupo, a large, deep, oligotrophic lake in North Island, New Zealand. The model was optimised using in situ inherent optical properties (IOPs) from the literature. Images were atmospherically corrected using the radiative transfer model 6sv. Application of the bio-optical model using a single chl a-specific absorption spectrum (a*ϕ(λ)) resulted in low correlation between estimated and observed values. Therefore, two different absorption curves were used, based on the seasonal dominance of phytoplankton phyla with differing absorption properties. The application of this model resulted in reasonable agreement between modelled and in situ chl a concentrations. Highest concentrations were observed during winter when Bacillariophytes (diatoms) dominated the phytoplankton assemblage. On 4 and 5 March 2004 an unusually large turbidity current was observed originating from the Tongariro River inflow in the south-east of the lake. In order to resolve fine details of the plume, empirical relationships were developed between MODIS band 1 reflectance (250 m resolution) and SS estimated from MODIS bio-optical features (1 km resolution) were used estimate SS at 250 m resolution. Complex lake circulation patterns were observed including a large clockwise gyre. With the development of this bio-optical model MODIS can potentially be used to remotely sense water quality in near real time, and the relationship developed for B1 SS allows for resolution of fine-scale features such turbidity currents

Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters

Ocean color modeling: Parameterization and interpretation

The ocean color as observed near the water surface is determined mainly by dissolved and particulate substances, known as optically-active constituents, in the upper water column. The goal of ocean color modeling is to interpret an ocean color spectrum quantitatively to estimate the suite of optically-active constituents near the surface. In recent years, ocean color modeling efforts have been centering upon three major optically-active constituents: chlorophyll concentration, colored dissolved organic matter, and scattering particulates. Many challenges are still being faced in this arena. This thesis generally addresses and deals with some critical issues in ocean color modeling. In chapter one, an extensive literature survey on ocean color modeling is given. A general ocean color model is presented to identify critical candidate uncertainty sources in modeling the ocean color. The goal for this thesis study is then defined as well as some specific objectives. Finally, a general overview of the dissertation is portrayed, defining each of the follow-up chapters to target some relevant objectives. In chapter two, a general approach is presented to quantify constituent concentration retrieval errors induced by uncertainties in inherent optical property (IOP) submodels of a semi-analytical forward model. Chlorophyll concentrations are retrieved by inverting a forward model with nonlinear IOPs. The study demonstrates how uncertainties in individual IOP submodels influence the accuracy of the chlorophyll concentration retrieval at different chlorophyll concentration levels. The important finding for this study shows that precise knowledge of spectral shapes of IOP submodels is critical for accurate chlorophyll retrieval, suggesting an improvement in retrieval accuracy requires precise spectral IOP measurements. In chapter three, three distinct inversion techniques, namely, nonlinear optimization (NLO), principal component analysis (PCA) and artificial neural network (ANN) are compared to assess their inversion performances to retrieve optically-active constituents for a complex nonlinear bio-optical system simulated by a semi-analytical ocean color model. A well-designed simulation scheme was implemented to simulate waters of different bio-optical complexity, and then the three inversion methods were applied to these simulated datasets for performance evaluation. In chapter four, an approach is presented for optimally parameterizing an irradiance reflectance model on the basis of a bio-optical dataset made at 45 stations in the Tokyo Bay and nearby regions between 1982 and 1984. (Abstract shortened by UMI.)

The Observation, Modeling, and Retrieval of Bio-Optical Properties for Coastal Waters of the Southern Chesapeake Bay

The primary purpose of this study was to develop an inverse method to retrieve the inherent optical properties (IOPs) and biogeochemical parameters (e.g. chlorophyll a concentration and salinity) appropriate to monitor the water quality and biogeochemical processes from remote sensing of the coastal waters in the southern Chesapeake Bay and coastal Mid-Atlantic Bight region (MAB) dominated by Case 2 waters. For this purpose, knowledge of the relationship between remote sensing reflectance (Rrs) and IOPs and the effect from bottom reflectance on Rrs, is required. A substantial investigation of IOPs has been conducted for the coastal waters of the southern Chesapeake Bay. Although phytoplankton are the dominant contributors to IOPs of oceanic Case 1 waters, colored dissolved organic matter (CDOM) derived from non-phytoplankton sources and sedimentary particles also play very important roles in coastal Case 2 waters. Strongly influenced by riverine discharge, the shallow coastal waters of the southern Chesapeake Bay provide challenges and opportunities to develop regionally specific IOP retrieval methods from remotely sensed ocean color imagery. A semi-analytical radiative transfer model (PZ06_Ed), based on the analysis of the simulated results of an exact radiative transfer model, Hydrolight® [Mobley, 1994], was developed to estimate the vertical distribution of downwelling plane irradiance [Ed(z)] from IOPs and sky conditions (e.g. cloud coverage and solar zenith angle). Compared to the significant overestimation of the simple Gordon [1989] model for particle-rich environments, PZ06_Ed agreed with Hydrolight® with \u3c 6% of the root-mean-square (RMS) error. Field observations from the coastal waters of the southern Chesapeake Bay validated the predictions of PZ06_ Edwith RMS error from 10% to 14%. The SeaWiFS imagery of the diffuse attenuation coefficient (Kd) estimated from PZ06_Ed is significantly improved from the Mueller [2000] model and displays obviously the coastal processes in the lower MAB, including the riverine outflow from the Chesapeake Bay and the mixing of the Gulf Stream with the local waters. The quadratic model (e.g. GSMO1) describing Rrs and IOPs has been widely used in bio-optics to retrieve inherent optical properties (IOPs). In this study, the derived coefficients (l1 and l2) by Gordon et al. [1988] were re-evaluated from Hydrolight® simulations and incorporated into a semi-analytical radiative transfer model (PZ06_ Rrs) that included bottom effects for optically shallow waters. Compared with Hydrolight® simulations and field observations in the Chesapeake Light Tower (CLT), Rrs calculated from PZ06_Rrs typically agreed within 5% and about 7% to 13% of RMS, respectively. Hydrolight ® simulations and field observations also confirmed that PZ06_Rrs improved the retrieval of biogeochemical-related parameters, including [Chl], adg(443), and bbp(443), compared to global ocean color algorithms (e.g. OC3M) and semi-analytic models without considering the bottom effects (e.g. GSM01-CLT). Finally, the relatively successful inverse modeling provides a promising method to study ecosystem-level biogeochemical and physical parameters from remote sensing for coastal waters of southern Chesapeake Bay and even lower MAB

Ocean Color Measurements with the Operational Land Imager on Landsat-8: Implementation and Evaluation in SeaDAS

The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a

Remote sensing and bio-geo-optical properties of turbid, productive inland waters: a case study of Lake Balaton

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches

Estimation of CDOM in Inland Waters via Water Bio-Optical Properties Using a Remote Sensing Approach

Monitoring of Colored dissolved organic matter (CDOM) in inland waters provides important information for tracing carbon cycle at the land-water interface and studying aquatic ecosystem. Remote sensing estimation of CDOM in the inland waters offers an alternative approach to field samplings in examining CDOM spatial-temporal dynamics. However, CDOM retrieval is a challenge due to the lack of algorithm for resolving bottom effect in shallow inland waters. Moreover, an effective approach based on multi-spectral, high spatial resolution and global coverage satellite images is in urgent need. To resolve these challenges, shallow water bio-optical properties (SBOP) algorithm was developed to overcome bottom reflectance effect on the total water leaving reflectance in shallow inland water. SBOP algorithm included the bottom reflectance in building underwater light transfer model. It was designed based on the field spectral data from four cruises in Lake Huron. SBOP algorithm had an obviously advantage over previous deep water CDOM algorithm (e.g. QAA-CDOM). In this study, Landsat-8 multi-spectral satellite imagery was selected to derive CDOM spatial-temporal dynamics in lake and river waters. The coastal blue band (443 nm), global coverage and high spatial resolution (30 m) of Landsat-8 images offered suitable data for inland water CDOM mapping. The SBOP algorithm was applied on Landsat-8 images in broad ranges of inland waters with high accuracy (Lake Huron (R2 = 0.87), 14 northeastern freshwater lakes (R2 = 0.80), and 6 large Arctic Rivers (R2 = 0.87)). Both the spatial patterns and seasonal dynamics were derived to study the multiple factors’ impact on terrestrially derived CDOM input to the rivers and lakes, including river discharge, watershed landcover, and temperature. This new satellite approach of CDOM estimation in inland waters provided high accuracy spatial-temporal information for studying land-water carbon cycle and aquatic environment