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

Remote sensing of chlorophyll-a in small inland waters

Small inland waters (SIWs) – waterbodies smaller than 100 km2 – are the predominant form of lakes globally, yet they are highly subject to water quality degradation, especially due to harmful algae blooms (HABs). Space-borne remote sensing has proven its capability to detect and map HABs in coastal waters as well as large waterbodies mostly through estimating chlorophyll-a (Chla). However, remote retrieval of near-surface Chla concentration in SIWs is challenging due to adjacency effects in remotely sensed signals and substantial in situ optical interferences of various water constituents. Although various algorithms have been developed or adapted to estimate Chla from moderate-resolution terrestrial missions (~ 10 – 60 m), there remains a need for robust algorithms to retrieve Chla in SIWs. Here, we introduce and evaluate new approaches to retrieve Chla in small lakes in a large lake catchment using Sentinel-2 and Landsat-8 imagery. In situ Chla data used in this study originate from various sources with contrasting measurement methods, ranging from field fluorometry to high-performance liquid chromatography (HPLC). Our analysis revealed that in vivo Chla measurements are not consistent with in vitro measurements, especially in high Chla amounts, and should be calibrated before being fed into retrieval models. Calibrated models based on phycocyanin (PC) fluorescence and environmental factors, such as turbidity, significantly decreased Chla retrieval error and increased the range of reconstructed Chla values. The proposed calibration models were then employed to build a consistent dataset of in situ Chla for Buffalo Pound Lake (BPL) – 30 km length and 1 km width – in the Qu’Appelle River drainage basin, Saskatchewan, Canada. Using this dataset for training and test, support vector regression (SVR) models were developed and reliably retrieved Chla in BPL. SVR models outperformed well-known commonly used retrieval models, namely ocean color (OC3), 2band, 3band, normalized difference chlorophyll index (NDCI), and mixture density networks (MDN) when applied on ~200 matchups extracted from atmospherically-corrected Sentinel-2 data. SVR models also performed well when applied to Landsat-8 data and data processed through various atmospheric correction (AC) processors. The proposed models also suggested good transferability over two optical water types (OWTs) found in BPL. Based on prior evaluations of the models’ transferability over OWTs in BPL, locally trained machine-learning (ML) models were extrapolated for regional retrieval of Chla in the Qu’Appelle River drainage basin. The regional approach was trained on in situ Chla data from BPL and retrieved Chla in other six lakes in the drainage basin. The proposed regional approach outperformed a recently developed global approach (MDN) in terms of accuracy, and showed more applicability than local models given the scarcity of in situ data in most lakes. In addition, ML models, e.g., SVR, performed consistently better than other models when employed in the regional approach. A rare phenomenon of marked blue discoloration of ice and water in winter 2021 in Pasqua Lake, a small lake in Qu’Appelle Watershed, provided an opportunity to assess the regional approaches in estimating chlorophyll-a for waterbodies where enough training data is not available. Therefore, using a developed model based on data from BPL, we produced Chla maps and could successfully relate the discoloration event to a late fall bloom in Pasqua Lake. We included the details of that study in Appendix A. Altogether, the models and approaches introduced in this thesis can serve as first steps toward developing a remote-sensing-based early warning system for monitoring HABs in small inland waters. Results showed that the development of an early warning system for SIWs based on Chla monitoring is currently possible, thanks to advancements in medium-resolution satellite sensors, in situ data collection methods, and machine learning algorithms. However, further steps need to be taken to improve the accuracy and reliability of systems: (a) in situ data need to be consistent for being fed into remote sensing models, (b) retrieval models and AC processors should be improved to provide better estimations of Chla, and (c) regional approaches might be developed as alternatives for local and global approaches in the absence of accurate AC processors and scarcity of in situ Chla data

Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

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

Estimación de los parámetros de calidad de agua y su relación con la reflectividad del superficie del Satélite Landsat 8 en el Lago Chinchaycocha - Junín

Para Estimar el modelo estadístico de parámetros de calidad del agua en el lago Chinchaycocha utilizando imágenes satelitales Landsat 8, se analizaron imágenes del 2013 al 2019, con un porcentaje menor al 10% de nubosidad que están relacionadas con las fechas de monitoreo in situ del lago, los niveles de reflectividad por bandas espectrales las cuales se determinó que tiene un valor de significancia menor a p < 0.05, nos indica una variación mínima entre las muestras analizadas. Para encontrar una mayor correlación entre los resultados de las observaciones in situ y los niveles de reflectancia, se vincularon ciertas bandas espectrales. En el caso del agua SST, se mostró un valor R2 de 0.9718, que está altamente correlacionado con la banda b5. CE está asociado con las bandas b2, b3, b4 y b6, representando R2 con un valor de 0,8522. En cuanto al pH, se asocia a las bandas b3, b4, b5 y b6 con un valor de R2 de 0,7697. La DO de las bandas b1, b3, b4, b5 y b7 se correlaciona con un R2 de 0,8577. La temperatura relativa al rango térmico de los sensores B10 y B11, que representa R2 es 0,6307

Bio-geo-optical properties and remote sensing of CDOM in optically complex inland waters

A substantial number of studies demonstrate the sensitivity of lakes to climate change and show that physical, chemical, and biological lake properties respond rapidly to climate-related changes. The indicators include variables such as temperature, dissolved organic carbon (DOC) or plankton composition. DOC is also known to play a primary role in protecting freshwater organisms from exposure to UV radiation and a big fraction of it is typically represented by dissolved organic matter (DOM). Moreover, the conservative properties between the coloured fraction of DOM (CDOM) and DOC, and the possibility of remotely estimating CDOM from space given its optical properties, makes it often used as a proxy for DOC. The development and validation of remote-sensing-based approaches for the retrieval of CDOM concentrations requires a comprehensive understanding of the sources and magnitude of variability in the optical properties of dissolved material within lakes. The present study aims to contribute with the knowledge of remote sensing of CDOM in inland water bodies with the specific objectives of characterising the link between CDOM absorption and DOC content in inland waters, investigating how changes in CDOM absorption can be used to infer information on its concentration, sources and decomposition and finally, to present an extensive CDOM algorithm validation exercise. The results of this Thesis indicate that the relationship between CDOM and DOC can vary remarkably. Strongest relationships have been found in waters with low anthropogenic influence, whereas waters more influenced by human activity present less clear linkages between the two parameters. As aromaticity increases in more productive waters we can then infer low CDOM to DOC relationships to them. 8 Remote-sensing models for DOC estimation based on the relationship between CDOM and DOC should therefore consider local variability and optical complexity, considering at least groups of water types according to their absorption features. In-lake spatial and seasonal variability in the quantity and quality of CDOM should also be taken into account. Photobleaching has been found to be a major factor controlling the in-lake transformation and degradation of CDOM, and a key process influencing the spatial structure CDOM throughout the system. These results also provide an insight on the potential contribution of wetlands to DOM and CDOM in lakes, not only in terms of the concentration of CDOM, but also in terms of its seasonality. All this leads to understand that CDOM content in complex inland waters usually present a wide range given their surrounding terrestrial characteristics and seasonal differences. The complexity of inland water bodies is currently a challenge to current remote sensing algorithms used to estimate parameters such as CDOM absorption (aCDOM). The accuracy of remote sensing-based retrievals of aCDOM at 440 nm (aCDOM (440)) can improve, mostly by targeting specific OWTs in algorithm development. For hypereutrophic waters with cyanobacterial blooms and abundant vegetation Blue-Green ratio based algorithms. For moderately productive waters with cyanobacteria presence, a double Blue-Green ratio based empirical algorithm is recommended. A double Blue-Green ratio and a Red-Green ratio for application in clear waters, turbid waters with high organic content, high productive waters with high cyanobacteria abundance and high reflectance at red/near-infrared spectral region. For waters high in CDOM, cyanobacteria presence and high absorption by NAP (Non-Algal Particles), a Green-Red ratio based algorithm. And finally, a semi analytic algorithm worked best for waters with high Rrs at short wavelengths

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

Assessment of Atmospheric Correction Algorithms for the Remote Sensing of Water Quality in Southeastern U.S. Estuaries

Water quality is a key indicator in understanding and representing an environment\u27s overall health. Through developments in remote sensing, we can utilize satellite imagery to measure water parameters in each aquatic system. When accurate atmospheric correction is performed, remote sensing can account for atmospheric attenuation and scattering effects to better measure the reflectance and estimate optically active constituents (OAC) present in upper water columns. Atmospheric Correction for OLI lite (ACOLITE) is an atmospheric correction algorithm designed specifically for robust atmospheric correction of water surfaces, in comparison to algorithms designed more for land surfaces such as the European Space Agency’s (ESA) Sen2Cor. An evaluation of atmospheric correction methods for coastal water quality for Georgia, USA, where contributions from pigments, inorganic matter, and organic matter are quite variable, has not been performed. This project analyzes the application and accuracy of atmospheric correction methods for several Georgia estuaries with spatially and temporally variable concentrations of water quality constituents using satellite imagery, in situ close-range spectral reflectance remote sensing match-up data, and field and laboratory analysis of water variables. The objectives of this study are: (1) Characterize study sites and individual water samples based on their concentrations of chlorophyll-a pigments, inorganic matter, and color- dissolved organic matter based on hyperspectral close-range reflectance, multispectral Sentinel-2 MultiSpectral Instrument (MSI) reflectance, and analysis bulk water samples and; (2) Evaluate and compare the accuracy of spectral reflectance data with no atmospheric correction, and ACOLITE and Sen2Cor atmospheric correction algorithms. It was found that hierarchical clustering had inconclusive results at characterizing optical water types, and some variation in optical water types were even seen within study sites. Further, ACOLITE and Sen2Cor atmospheric correction algorithms performed comparably at each specific wavelength in these environments (ACOLITE R²=0.215 (band 5) to 0.33 (band 2), Sen2cor R²=0.061 (band 5) to 0.299 (band 3)), and further validation would be required for a deeper understanding of their performance on more than a band-to-band comparison