Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

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

Floodwater impact on Galveston Bay phytoplankton taxonomy, pigment composition and photo-physiological state following Hurricane Harvey from field and ocean color (Sentinel-3A OLCI) observations

Phytoplankton taxonomy, pigment composition and photo-physiological state were studied in Galveston Bay (GB), Texas (USA), following the extreme flooding associated with Hurricane Harvey (25-29 August 2017) using field and satellite ocean color observations. The percentage of chlorophyll a (Chl a) in different phytoplankton groups was determined from a semi-analytical IOP (inherent optical property) inversion algorithm. The IOP inversion algorithm revealed the dominance of freshwater species (diatom, cyanobacteria and green algae) in the bay following the hurricane passage (29 September 2017) under low salinity conditions associated with the discharge of floodwaters into GB. Two months after the hurricane (29-30 October 2017), under more seasonal salinity conditions, the phytoplankton community transitioned to an increase in small-sized groups such as haptophytes and prochlorophytes. Sentinel-3A Ocean and Land Colour Instrument (OLCI)-derived Chl a obtained using a red / NIR (near-infrared) band ratio algorithm for the turbid estuarine waters was highly correlated (R-2 \u3e 0.90) to the (high-performance liquid chromatography) HPLC-derived Chl a. Long-term observations of OLCI-derived Chl a (August 2016-December 2017) in GB revealed that hurricane-induced Chl a declined to background mean state in late October 2017. A non-negative least squares (NNLS) inversion model was then applied to OLCI-derived Chl a maps of GB to investigate spatiotemporal variations of phytoplankton diagnostic pigments pre- and post-hurricane; results appeared consistent with extracted phytoplankton taxonomic composition derived from the IOP inversion algorithm and microplankton pictures obtained from an Imaging FlowCytobot (IFCB). OLCI-derived diagnostic pigment distributions also exhibited good agreement with HPLC measurements during both surveys, with R-2 ranging from 0.40 for diatoxanthin to 0.96 for Chl a. Environmental factors (e.g., floodwaters) combined with phytoplankton taxonomy also strongly modulated phytoplankton physiology in the bay as indicated by measurements of photosynthetic parameters with a fluorescence induction and relaxation (FIRe) system. Phytoplankton in well-mixed waters (mid-bay area) exhibited maximum PSII photochemical efficiency (F-v/F-m) and a low effective absorption cross section (sigma(PSII)), while the areas adjacent to the shelf (likely nutrient-limited) showed low F-v/F-m and elevated sigma(PSII) values. Overall, the approach using field and ocean color data combined with inversion models allowed, for the first time, an assessment of phytoplankton response to a large hurricane-related floodwater perturbation in a turbid estuarine environment based on its taxonomy, pigment composition and physiological state

Phytoplankton and Carbon Dynamics in the Estuarine-Coastal Waters of the Northern Gulf of Mexico from Field Data and Ocean Color Remote Sensing

In this study, phytoplankton community and carbon dynamics were examined in the optically complex estuarine-coastal regions of the northern Gulf of Mexico (nGOM) from field and satellite ocean color observations. As part of this study, bio-optical ocean color algorithms for i) dissolved organic carbon (DOC), ii) phytoplankton pigment composition, iii) adaptive estimation of Chl a and iv) phytoplankton size fractions were developed to facilitate the study of biogeochemical cycling in the nGOM. The phytoplankton based algorithms were applied to Sentinel 3A/B-OLCI oean color data to assess phytoplankton community dynamics to extreme river discharge conditions as well as hurricanes in the nGOM. This study revealed that the effects of hurricanes on phytoplankton community dynamics were dependent on background nutrient conditions, as well as the intensity, track and translational speed of storms: 1) Strong flooding associated with Hurricane Harvey (2017) shifted the dominance of phytoplankton community in Galveston Bay from cyanobacteria and dinoflagellate to diatom and chlorophyte; 2) high levels of organic matter delivered from estuaries to shelf waters after Hurricane Michael (2018) fueled a red tide mixed with coccolithophore bloom in the nGoM; 3) the physical and chemical environment after hurricanes are favorable for the growth and dominance of coccolithophores in shelf waters. Further, microphytoplankton mainly controlled by freshwater inflows showed dominance in estuaries of the nGoM, with highest/lowest values observed in spring/fall. In comparison, phytoplankton size fraction (PSF) dynamics in the midshelf and offshore waters of the nGoM are strongly influenced by Loop Current (LC) expansion, and eddy shedding with highest picophytoplankton fraction observed in the warm waters of LC. DOC dynamics was studied using an empirical algorithm that was developed and applied to multiple satellite sensors (Landsat 5 TM and MODIS-Aqua) to assess multi-decadal (1985-2012) DOC trends in Barataria Basin. The linkages between DOC and environmenal variations were investigated. The relationships between satellite-derived DOC and land cover variations (1985–2011) derived from Landsat-5 TM supervised classification indicate soil loss in the salt marsh to be an important DOC source in the wetland-estuary system, and overall strong land use/land loss impact on the long-term DOC trends in the Barataria Basin

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

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake

To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSElog 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [aph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3

Algorithm to derive inherent optical properties from remote sensing reflectance in turbid and eutrophic lakes

Inherent optical properties play an important role in understanding the biogeochemical processes of lakes by providing proxies for a variety of biogeochemical quantities, including phytoplankton pigments. However, to date, it has been difficult to accurately derive the absorption coefficient of phytoplankton [aph(λ)] in turbid and eutrophic waters from remote sensing. A large dataset of remote sensing of reflectance [ Rrs (λ)] and absorption coefficients was measured for samples collected from lakes in the middle and lower reaches of the Yangtze River and Huai River basin (MLYHR), China. In the process of scattering correction of spectrophotometric measurements, the particulate absorption coefficients [ap(λ)] were first assumed to have no absorption in the near-infrared (NIR) wavelength. This assumption was corrected by estimating the particulate absorption coefficients at 750 nm [ap(750)] from the concentrations of chlorophyll-a (Chla) and suspended particulate matter, which was added to the ap(λ) as a baseline. The resulting mean spectral mass-specific absorption coefficient of the nonalgal particles (NAPs) was consistent with previous work. A novel iterative IOP inversion model was then designed to retrieve the total nonwater absorption coefficients [anw(λ)] and backscattering coefficients of particulates [bbp(λ)], aph(λ), and adg (λ) [absorption coefficients of NAP and colored dissolved organic matter (CDOM)] from Rrs (λ) in turbid inland lakes. The proposed algorithm performed better than previously published models in deriving anw(λ) and bbp(λ) in this region. The proposed algorithm performed well in estimating the aph(λ) for wavelengths \u3e 500 nm for the calibration dataset [N = 285, unbiased absolute percentage difference (UAPD) = 55.22%, root mean square error (RMSE) = 0.44 m−1] and for the validation dataset (N = 57, UAPD = 56.17%, RMSE = 0.71 m−1). This algorithm was then applied to Sentinel-3A Ocean and Land Color Instrument (OLCI) satellite data, and was validated with field data. This study provides an example of how to use local data to devise an algorithm to obtain IOPs, and in particular, a ph (λ), using satellite Rr s (λ) data in turbid inland waters

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

A transferable bio-optical model for quantification of inland water caynobacterial pigments

Indiana University-Purdue University Indianapolis (IUPUI)Cyanobacterial blooms are currently one of the most important issues faced by environmental agencies, water authorities and public health organizations. Remote sensing provides an advanced approach to monitor cyanobacteria by detecting and quantifying chlorophyll-a (Chl-a) and phycocaynin (PC). In this thesis, an analytical bio-optical model, more typically applied to ocean waters, was modified to accommodate the complexity of inland waters. The newly developed models work well to estimate inherent optical properties, including absorption and backscattering coefficients, in eight different study sites distributed around the globe. Based on derived absorption coefficients, Chl-a and PC concentrations were accurately retrieved for data sets collected annually from 2006 to 2010, and the estimation accuracy exceeded that of currently used algorithms. An important advantage of the model is that low concentrations of Chl-a and PC can be predicted more accurately, enabling early warning of cyanobacterial blooms. In addition, the results also indicated good spatial and temporal transferability of the algorithms, since no specific calibration procedures were required for data sets collected in a different sites and seasons. The compatibility of the newly developed algorithm with MERIS spectra provides the possibility for routine surveillance of cyanobacterial growth in inland waters