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

Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake

The 10-year archive of MEdium Resolution Imaging Spectrometer (MERIS) data is an invaluable resource for studies on lake system dynamics at regional and global scales. MERIS data are no longer actively acquired but their capacity for global scale monitoring of lakes from satellites will soon be re-established through the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development and validation of in-water algorithms for the accurate retrieval of biogeochemical parameters is thus of key importance if the potential of MERIS and OLCI data is to be fully exploited for lake monitoring. This study presents the first extensive validation of algorithms for chlorophyll-a (chl-a) retrieval by MERIS in the highly turbid and productive waters of Lake Balaton, Hungary. Six algorithms for chl-a retrieval from MERIS over optically complex Case 2 waters, including band-difference and neural network architectures, were compared using the MERIS archive for 2007-2012. The algorithms were locally-tuned and validated using in situ chl-a data (n = 289) spanning the five year processed image time series and from all four lake basins. In general, both band-difference algorithms tested (Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI)) performed well, whereas the neural network processors were generally found to much less accurately retrieve in situ chl-a concentrations. The Level 1b FLH algorithm performed best overall in terms of chl-a retrieval (R2 = 0.87; RMSE = 4.19 mg m- 3; relative RMSE = 30.75%) and particularly at chl-a concentrations of ≥ 10 mg m- 3 (R2 = 0.85; RMSE = 4.81 mg m- 3; relative RMSE = 20.77%). However, under mesotrophic conditions (i.e., chl-a < 10 mg m- 3) FLH was outperformed by the locally-tuned FUB/WeW processor (relative FLH RMSE < 10 mg m- 3 = 57.57% versus relative FUB/WeW RMSE < 10 mg m- 3 = 46.96%). An ensemble selection of in-water algorithms is demonstrated to improve chl-a retrievals

Phenology of cyanobacterial blooms in three catchments of the Laurentian Great Lakes

This dissertation discusses the cyanobacterial bloom phenology in three anthropogenically impacted regions of the Great Lakes: western Lake Erie, Saginaw Bay, and Green Bay. A detection algorithm was applied to ocean color satellite imagery, and a timeseries was constricted from each of the basins using either data from the MODIS sensor (Saginaw Bay), the MERIS sensor (Green Bay), or a combination of the two (western Lake Erie). The sensors have a high temporal resolution, collecting imagery several times a week. The algorithm used, the Cyanobacterial Index (CI), was applied to the imagery. The CI imagery was then sampled into fifteen 10-day composites throughout the bloom season (defined here as June 1 – October 31). Each of the five months will have three composites (each spanning ~10 days). From this point the bloom climatology is shown and the variability of each region is addressed. The interannual variability of the cyanobacterial blooms can be low (factor of ~2 in Saginaw Bay) or high (differing by a factor of ~20 in Green Bay and western Lake Erie). Various ancillary datasets describing the physical environment of each region were assembled including: field data, modeled data, remotely sensed data, or some combination therein. Impacts of associated cyanobacterial biotoxins were addressed and statistical models were formulated to explain any variability. The dissertation will also cross compare the three basins with one another in an effort to determine the similarities as well as differences among the regions. Management recommendations are given at the end of each of the three subsequent chapters to deter potential detrimental impacts of the blooms and their associated toxins

Using Remote Sensing and Environmental Precursors to Detect and Predict Cyanobacteria Harmful Algal Blooms in Northeastern US Waterbodies

Cyanobacteria harmful algal blooms (cyanoHABs) are a global problem with human health, environmental, and economic concerns. The severity and frequency of toxic cyanoHABs are expected to increase with climate change. Remote sensing has proven to be a useful tool in monitoring cyanoHABs. This study uses remote sensing observations from Sentinel-3Ocean Land Color Imager (OLCI)combined with the Spectral Shape Algorithm (SSA) to detect the presence of cyanobacteria in numerous waterbodies throughout the Northeast United States over 2016to 2020. The ACOLITE processor was used for the atmospheric correction of the Sentinel-3 OLCI data, as it has been shown to provide more accurate results over water bodies. Citizen scientist data from two EPA apps, BloomWatch and CyanoScope, were used to guide the cyanoHAB detection results. This study focuses on three different aspects of using the remote sensing methods to detect cyanobacteria harmful algal blooms: (1) examine two different reflectance center band combinations of the SSA at 665 nm and 681 nm, (2) compare SSA controls for non-bloom periods in winter/spring to the summer bloom periods, and (3) examine bloom areas within waterbodies. The SSA method with the center band at 665 nm was found to perform better than the SSA method with the center band at 681 nm. Additionally, cyanoHAB environmental precursors were determined and their effects on the formation of cyanoHABs were assessed, with maximum air temperature having the greatest impact on cyanoHABs formation

CHARACTERIZING FRESHWATER PHYTOPLANKTON DYNAMICS WITH ELECTRO-OPTICAL REMOTE SENSING

Freshwater lakes are an important component contributing to ecosystem health and biodiversity on local, regional, and global scales. And while lakes only represent \u3c5% of the global surface area, they are often very productive systems which contribute significantly to carbon cycling dynamics and freshwater fish production on a number of spatial scales. Due to the remote location and sheer size of some of these lakes it has proven difficult to adequately document changes in water quality. Significant challenges exist to adequately monitor water quality, and in particular phytoplankton dynamics, over large spatial and temporal scales using traditional in situ methods. Satellite electro-optical remote sensing offers a potential tool to provide better characterization of phytoplankton dynamics for a variety of freshwater systems. This work resulted in an approach to quantify global summer phytoplankton abundance using a newly developed remote sensing derived chlorophyll-a product. This product was also used in conjunction with a newly created carbon fixation model to assess global freshwater phytoplankton production which provided new insights into the role freshwater systems play in the global carbon budget. Spatial and temporal assessments of specific populations of phytoplankton and cyanobacteria were established through the development of a new remote sensing algorithm to isolate high biomass assemblages in the Laurentian Great Lakes (Lake Erie, Lake Huron, Lake Michigan). The algorithm was developed to facilitate the fusion of multiple remote sensing data sources (SeaWiFS and MODIS) in order to generate a new 20-year time-series data product to better understand the factors controlling bloom dynamics. Finally, a spatio-temporal analysis documenting the variability of inherent optical properties (IOPs) in Lake Erie established a seasonal progression of phytoplankton/cyanobacteria community structures for two years over the vegetative season, the findings of which are critical for the development of the next generation of hyperspectral remote sensing algorithms to improve phytoplankton community characterizations from space. These documented results clearly show the utility of electro-optical remote sensing to provide characterization of phytoplankton dynamics and insights at both community and population scales in freshwater systems

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

Extreme Climate Anomalies Enhancing Cyanobacterial Blooms in Eutrophic Lake Taihu, China

Climate warming in combination with nutrient enrichment can greatly promote phytoplankton proliferation and blooms in eutrophic waters. Lake Taihu, China, is a large, shallow and eutrophic system. Since 2007, this lake has experienced extensive nutrient input reductions aimed at controlling cyanobacterial blooms. However, intense cyanobacterial blooms have persisted through 2017 with a record-setting bloom occurring in May 2017. Causal analysis suggested that this bloom was sygenerically driven by high external loading from flooding in 2016 in the Taihu catchment and a notable warmer winter during 2016/2017. High precipitation during 2016 was associated with a strong 2015/2016 El Niño in combination with the joint effects of Atlantic Multi-decadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while persistent warmth during 2016/2017 was strongly related to warm phases of AMO and PDO. The 2017 blooms elevated water column pH and led to dissolved oxygen depletion near the sediment, both of which mobilized phosphorus from the sediment to overlying water, further promoting cyanobacterial blooms. Our finding indicates that regional climate anomalies exacerbated eutrophication via a positive feedback mechanism, by intensifying internal nutrient cycling and aggravating cyanobacterial blooms. In light of global expansion of eutrophication and blooms, especially in large, shallow and eutrophic lakes, these regional effects of climate anomalies are nested within larger scale global warming predicted to continue in the foreseeable future

Analyse der Wasserfarbe von Seen mithilfe räumlich hoch und mittel auflösender Satelliten

Remote sensing techniques can assist traditional lake monitoring approaches by supplying spatial information on optically active lake ecology indicators, i.e. chlorophyll-a (CHL), total suspended matter (TSM), coloured dissolved organic matter (CDOM), and, especially in optically shallow waters, water depth and substrate composition. The present thesis provides an overview on the current research status concerning lake remote sensing and the benefit of time series analyses for lake ecology. To investigate the suitability of Sentinel-2 and Landsat 8 for lake monitoring and their combination with other sensors this thesis focused on two study areas with highly different optical characteristics, i.e. the oligotrophic Lake Starnberg (southern Germany) and the mesotrophic-eutrophic Lake Kummerow (northern Germany). Using the bio-optical model WASI-2D, Sentinel-2A turned out to be suited for retrieving low TSM and CDOM values. The high spatial resolution enabled the differentiation between bare ground and areas covered by submerged aquatic vegetation. Water depth estimations performed well until half Secchi disk depth. Cross-sensor comparisons demonstrated high correlation of CHL among timely acquired, spatially high and medium resolved sensors. Evaluations with in situ data showed that most of the sensor-in situ match-ups were within an uncertainty range of in situ measurements. Analysing a 9-year MERIS time series with FUB/WeW revealed unprecedented information on temporal trends and seasonal behaviour of CHL, TSM and CDOM at the study area Lake Kummerow. Combining CHL, retrieved with the Modular Inversion and Processing System, from different satellite sensors (MODIS, Landsat 7/ 8, Sentinel-2A) enabled detailed observations of phytoplankton development. Such combinations are a step forward to future lake analyses which may integrate remote sensing data, in situ measurements and environmental modelling.Fernerkundungstechniken können das Seemonitoring mit räumlichen Informationen über optisch aktive Indikatoren der Gewässerökologie liefern, z.B. Chlorophyll-a (CHL), suspendierte Schwebstoffe (TSM), Gelbstoffe (CDOM) und insbesondere in optisch flachen Gewässern, Wassertiefe und Substratbedeckung. Die vorliegende Arbeit gibt einen Überblick über den aktuellen Forschungsstand zur Seefernerkundung und den Nutzen von Zeitreihenanalysen für die Seeökologie. Um die Eignung von Sentinel-2 und Landsat 8 für ein Seenmonitoring und deren Kombination mit anderen Sensoren zu untersuchen, konzentrierte sich diese Arbeit auf zwei Untersuchungsgebiete mit sehr unterschiedlichen optischen Eigenschaften: den oligotrophen Starnberger See (Süddeutschland) und den mesotroph-eutrophen Kummerower See (Norddeutschland). Mit dem bio-optischen Modell WASI-2D erwies sich Sentinel-2A als geeignet, um niedrige TSM- und CDOM-Werte zu bestimmen. Die hohe räumliche Auflösung ermöglichte eine Unterscheidung zwischen unbewachsenem und mit Makrophyten bewachsenem Untergrund. Die Wassertiefenbestimmung verlief bis zur halben Sichttiefe gut. Sensorübergreifende Vergleiche zeigten eine hohe Korrelation von CHL zwischen zeitnah erfassten, räumlich mittel und hoch aufgelösten Sensoren. Auswertungen mit in-situ-Daten zeigten, dass die meisten Sensor-in-situ-Match-ups innerhalb eines Unsicherheitsbereichs von in-situ-Messungen lagen. Die Analyse einer 9-jährigen MERIS-Zeitreihe mit FUB/WeW ergab neue Informationen über zeitliche Trends und saisonales Verhalten von CHL, TSM und CDOM im Untersuchungsgebiet Kummerow See. Die Kombination von CHL aus verschiedenen Satellitensensoren (MODIS, Landsat 7/ 8, Sentinel-2A) mit dem Modular Inversion and Processing System ermöglichte detaillierte Beobachtungen der Phytoplanktonentwicklung. Solche Kombinationen sind ein Schritt für zukünftigen Gewässeranalysen, die Fernerkundungsdaten, in-situ-Messungen und Umweltmodellierung integrieren sollten