An algorithm to retrieve chlorophyll,dissolved organic carbon,and suspended minerals from Great Lakes satellite data

An algorithm that utilizes individual lake hydro-optical (HO) models has been developed for the Great Lakes that uses SeaWiFS, MODIS, or MERIS satellite data to estimate concentrations of chlorophyll, dissolved organic carbon, and suspended minerals. The Color Producing Agent Algorithm (CPA-A) uses a specific HO model for each lake. The HO models provide absorption functions for the Color Producing Agents (CPAs) (chlorophyll (chl), colored dissolved organic matter (as dissolved organic carbon, doc), and suspended minerals (sm)) as well as backscatter for the chlorophyll, and suspended mineral parameters. These models were generated using simultaneous optical data collected with in situ measurements of CPAs collected during research cruises in the Great Lakes using regression analysis as well as using specific absorption and backscatter coefficients at specific chl, doc, and sm concentrations. A single average HO model for the Great Lakes was found to generate insufficiently accurate concentrations for Lakes Michigan, Erie, Superior and Huron. These new individual lake retrievals were evaluated with respect to EPA in situ field observations, as well as compared to the widely used OC3 MODIS retrieval. The new algorithm retrievals provided slightly more accurate chl values for Lakes Michigan, Superior, Huron, and Ontario than those obtained using the OC3 approach as well as providing additional concentration information on doc and sm. The CPA-A chl retrieval for Lake Erie is quite robust, producing reliable chl values in the reported EPA concentration ranges. Atmospheric correction approaches were also evaluated in this study.     

Using satellite observations to assess the spatial representativeness of the GLNPO water quality monitoring program

The U.S. EPA's Great Lakes National Program Office (GLNPO) annual water quality survey (WQS) collects data at a relatively small number of stations in each lake. The survey was designed to measure conditions in the open-water regions of the lakes where an assumption of spatial homogeneity was thought likely to be met and the measured variables could be characterized by simple statistics. Here we use satellite observations to assess how well statistics based on samples collected in the GLNPO sampling network represent the lake-wide values of two variables, surface chlorophyll concentration and Secchi depth. We find strong linear relationships between the mean values calculated from the samples and the corresponding averages based on the subsets of the full satellite images. Although overall the means of the values from the sample locations agree well with means calculated from most of the non-coastal regions of the lakes, in terms of water depth, the GLNPO station averages best represent the regions of Lake Huron deeper than 30?m, of Lakes Michigan and Superior deeper than 90?m, and of Lake Ontario deeper than 60?m. When the lake regions are defined by distance offshore rather than by depth, the GLNPO station chlorophyll means in Lakes Huron, Ontario, and Superior are closest to the means for the area of the lakes >10?km offshore. In Lake Michigan the closest correspondence is with the >20?km offshore region. On a whole-lake basis in Lake Erie the GLNPO station chlorophyll averages are closest to the average calculated from the entire lake.     

Satellite ocean color algorithms: A review of applications to the Great Lakes

We review the literature dealing with retrievals of chlorophyll concentrations in the Great Lakes from satellite observations. Most studies show that the satellite estimates of chlorophyll concentrations are linearly related to the observed concentrations, though they tend to overestimate concentrations at lower values and underestimate them at higher values. Deviations from a consistent, accurate, linear relationship can be attributed to temporal and spatial variations in the inherent optical properties of the color producing agents in the water as well as to varying concentrations of non-algal substances that interfere with the retrievals. We confirmed these results by using a simple optical model to examine the sensitivity of the retrieved chlorophyll values to the concentrations of interfering substances and to differences in model parameters. Because the spatial and temporal optical properties of the Great Lakes are unpredictable, no retrieval method is likely to produce accurate results all the time. The papers we reviewed show that simple band ratio algorithms can provide chlorophyll estimates that are proportional to in situ concentrations. The published literature suggests that the band ratio methods will be of most value in regions where the concentrations of non-algal interfering substances are minimal. Because of these limitations we recommend that future papers presenting chlorophyll analysis based on satellite data provide confirming field observations that include measurements of chlorophyll, suspended particles and dissolved organic carbon. We also recommend that Great Lakes scientists explore novel methods for retrieving chlorophyll concentrations from satellite observations that have proven useful in other optically complex waters.     

Assessing the application of SeaWiFS ocean color algorithms to Lake Erie

The feasibility of satellite-based monitoring of phytoplankton chlorophyll a concentrations in Lake Erie is assessed by applying globally calibrated, ocean-derived color algorithms to spatially and temporally collocated measurements of SeaWiFS remote sensing reflectance. Satellite-based chlorophyll a retrievals were compared with fluorescence-based measurements of chlorophyll a from 68 field samples collected across the lake between 1998 and 2002. Twelve ocean-derived color algorithms, one regional algorithm derived for the Baltic Sea's Case 2 waters, and a set of regional algorithms developed for the western, central and eastern basins of Lake Erie were considered. While none of the ocean-derived algorithms performed adequately, the outlook for the success of regionally calibrated and validated algorithms, with forms similar to the ocean-derived algorithms, is promising over the eastern basin and possibly the central basin of the lake. In the western basin, each of the regional algorithms considered performed poorly, indicating that alternative approaches to algorithm development, or to satellite data screening and analysis procedures will be needed.     

A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan

Accurate methods to track changes in lake productivity through time and space are critical to fisheries management. Chlorophyll a is the most widely studied proxy for ecosystem primary production and has been the topic of many studies. The main sources of chlorophyll a measurements are ship-based measures or multi-spectral satellite data. Autonomous underwater vehicles can survey large spatial extents approaching the scale of satellite data, but with the accuracy of ship-based water sampling methods. We use several statistical measures to compare measures of chlorophyll a collected in Lake Michigan with spatiotemporally matched satellite-derived measures of chlorophyll a from the MODIS Aqua multi-spectral sensor using NASA's OC3 and the Great Lakes Fit algorithms. Our findings show a near one to one relationship between AUV data and both satellite-derived data sets when the AUV data are coarsened to the resolution of the satellite data. A comparison of satellite-based chlorophyll a to AUV-derived chlorophyll summarized in discrete water depth bins suggested that, based on decreasing coefficients of determination, satellite estimates of chlorophyll accounted for the most variability in chlorophyll a concentrations in the upper 10 m of the water column, even though satellite sensors may detect past this depth.     

Verification and Application of a Bio-optical Algorithm for Lake Michigan Using SeaWiFS: a 7-year Inter-annual Analysis

In this paper we utilize 7 years of SeaWiFS satellite data to obtain seasonal and interannual time histories of the major water color-producing agents (CPAs), phytoplankton chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals (sm) for Lake Michigan. We first present validation of the Great Lakes specific algorithm followed by correlations of the CPAs with coincident environmental observations. Special attention is paid to the satellite observations of the extensive episodic event of sediment resuspension and calcium carbonate precipitation out of the water. We then compare the obtained time history of the CPA's spatial and temporal distributions throughout the lake to environmental observations such as air and water temperature, wind speed and direction, significant wave height, atmospheric precipitation, river runoff, and cloud and lake ice cover. Variability of the onset, duration, and spatial extent of both episodic events and seasonal phenomena are documented from the SeaWiFS time series data, and high correlations with relevant environmental driving factors are established. The relationships between the CPAs retrieved from satellite data and environmental observations are then used to speculate on the future of Lake Michigan under a set of climate change scenarios.     

A model for determining satellite-derived primary productivity estimates for Lake Michigan

A new MODIS based satellite algorithm to estimate primary production (PP) has been generated and evaluated for Lake Michigan. The Great Lakes Primary Productivity Model (GLPPM) is based on previous models that required extensive in situ data but it can utilize remotely sensed observations as input for some model variables and therefore allows greater spatial resolution for primary productivity estimates. The Color Producing Agent Algorithm (CPA-A) is utilized to obtain robust chlorophyll a values and the NASA KD2M approach is used to obtain the diffuse attenuation coefficient (K_d). Only incident PAR and carbon fixation rates are additionally needed to generate the primary productivity estimate. Comparisons of the satellite derived PP estimates from single monthly images to average monthly field measurements made by NOAA/GLERL found good agreement between estimates. Satellite derived PP estimates were used to estimate a preliminary Lake Michigan annual primary production of 8.5 Tg C/year. The new algorithm can be easily adapted to work on all the Great Lakes and therefore can be used to generate time series dating back to late 1997 (launch of SeaWiFs). These time series can contribute to improved assessment of Great Lakes primary productivity changes as a result of biological events, such as Dreissenid mussel invasions, climatic change and anthropogenic forcing.     

U.S. EPA Great Lakes National Program Office monitoring of the Laurentian Great Lakes: Insights from 40 years of data collection

The U.S. EPA Great Lakes National Program Office (GLNPO) implements long-term monitoring programs to assess Great Lakes ecosystem status and trends for many interrelated ecosystem components, including offshore water quality as well as offshore phytoplankton, zooplankton and benthos; chemical contaminants in air, sediments, and predator fish; hypoxia in Lake Erie's central basin; and coastal wetland health. These programs are conducted in fulfillment of Clean Water Act mandates and Great Lakes Water Quality Agreement commitments. This special issue presents findings from GLNPO's Great Lakes Biology Monitoring Program, Great Lakes Water Quality Monitoring Program, Lake Erie Dissolved Oxygen Monitoring Program, Integrated Atmospheric Deposition Network, Great Lakes Fish Monitoring and Surveillance Program, and Great Lakes Sediment Surveillance Program. These GLNPO programs have generated temporal and spatial datasets for all five Great Lakes that form the basis for assessment of the state of these lakes, including trends in nutrients, key biological indicators, and contaminants in air, sediments and fish. These datasets are used by researchers and managers across the Great Lakes basin for investigating physical, chemical and biological drivers of ongoing ecosystem changes; some of these analyses are presented in this special issue, along with discussion of new methods and approaches for monitoring.     

A brief history of the U.S. EPA Great Lakes National Program Office's water quality survey

The U.S. Environmental Protection Agency Great Lakes National Program Office (GLNPO) water quality survey (WQS) constitutes the longest-running, most extensive monitoring of water quality and the lower trophic level biota of the Laurentian Great Lakes, and has been instrumental in tracking shifts in nutrients and the lower food web over the past several decades. The initial impetus for regular monitoring of the Great Lakes was provided by the 1972 Great Lakes Water Quality Agreement (GLWQA) which asked the parties to develop monitoring and surveillance programs to ensure compliance with the goals of the agreement. The resulting monitoring plan, eventually known as the Great Lakes International Surveillance Plan (GLISP), envisioned a nine-year rotation of intensive surveys of the five lakes. A broadening of the scope of the GLWQA in 1978 and the completion of the first nine-year cycle of sampling, prompted reappraisals of the GLISP. During this pause, and using knowledge gained from GLISP, GLNPO initiated an annual WQS with the narrower focus of tracking water quality changes and plankton communities in the offshore waters of the lakes. Beginning in 1983 with lakes Erie, Huron, and Michigan, the WQS added Lake Ontario in 1986 and Lake Superior in 1992, evolving into its current form in which all five lakes are sampled twice a year. The WQS is unique in that all five lakes are sampled by one agency, using one vessel and one principal laboratory for each parameter group, and represents an invaluable resource for managing and understanding the Great Lakes.     

Origin and Evolution of the Great Lakes

This paper presents a synthesis of traditional and recently published work regarding the origin and evolution of the Great Lakes. It differs from previously published reviews by focusing on three topics critical to the development of the Great Lakes: the glaciation of the Great Lakes watershed during the late Cenozoic, the evolution of the Great Lakes since the last glacial maximum, and the record of lake levels and coastal erosion in modern times.The Great Lakes are a product of glacial scour and were partially or totally covered by glacier ice at least six times since 0.78 Ma. During retreat of the last ice sheet large proglacial lakes developed in the Great Lakes watershed. Their levels and areas varied considerably as the oscillating ice margin opened and closed outlets at differing elevations and locations; they were also significantly affected by channel downcutting, crustal rebound, and catastrophic inflows from other large glacial lakes.Today, lake level changes of about a 1/3 m annually, and up to 2 m over 10 to 20 year time periods, are mainly climatically-driven. Various engineering works provide small control on lake levels for some but not all the Great Lakes. Although not as pronounced as former changes, these subtle variations in lake level have had a significant effect on shoreline erosion, which is often a major concern of coastal residents.     

Mapping cyanobacterial blooms in the Great Lakes using MODIS

Toxin-producing Cyanobacteria have been documented in Lake Erie and Ontario in the last several years. We developed algorithms to discriminate potentially toxic cyanobacterial blooms from other harmless phytoplankton blooms and to extract relative phycocyanin abundances from Moderate Resolution Imaging Spectrometer (MODIS) satellite data. Lee's quasi-analytical algorithm was used to calculate total absorption and backscatter from the 250 m, 500 m and 1 km bands of MODIS scenes. A non-negative least square algorithm was then utilized to discern relative concentrations of Chlorophyta (green algae), phycocyanin-rich Cyanobacteria (blue-green algae), and colored dissolved organic matter and suspended sediments combined in lake waters using published absorption spectra for these components. MODIS-derived cyanobacterial concentrations and/or bloom distributions from 10 scenes acquired in the summers of 2004 and 2005 were successfully verified against contemporaneous calibrated measurements of pigments that were acquired from measurements made using continuous fluorimetric measurements of surface water (1 m depth) from six cruises, and three additional cyanobacterial blooms reported in the scientific literature between 2002 and 2006. These results demonstrate that this methodology could be used to develop a cost-effective practical screening method for rapid detection and warning of potentially toxic cyanobacterial blooms in the lower Great Lakes.     

Genetic diversity of lake whitefish in lakes Michigan and Huron; sampling,standardization, and research priorities

We combined data from two laboratories to increase the spatial extent of a genetic data set for lake whitefish Coregonus clupeaformis from lakes Huron and Michigan and saw that genetic diversity was greatest between lakes, but that there was also structuring within lakes. Low diversity among stocks may be a reflection of relatively recent colonization of the Great Lakes, but other factors such as recent population fluctuation and localized stresses such as lamprey predation or heavy exploitation may also have a homogenizing effect. Our data suggested that there is asymmetrical movement of lake whitefish between Lake Huron and Lake Michigan; more genotypes associated with Lake Michigan were observed in Lake Huron. Adding additional collections to the calibrated set will allow further examination of diversity in other Great Lakes, answer questions regarding movement among lakes, and estimate contributions of stocks to commercial yields. As the picture of genetic diversity and population structure of lake whitefish in the Great Lakes region emerges, we need to develop methods to combine data types to help identify important areas for biodiversity and thus conservation. Adding genetic data to existing models will increase the precision of predictions of the impacts of new stresses and changes in existing pressures on an ecologically and commercially important species.     

Wind Fields over the Great Lakes Measured by the SeaWinds Scatterometer on the QuikSCAT Satellite

This paper demonstrates the utility of satellite scatterometer measurements for wind retrieval over the Great Lakes on a daily basis. We use data acquired by the SeaWinds Scatterometer on the QuikSCAT (QSCAT) satellite launched in June 1999 to derive wind speeds and directions over the lakes at a resolution of 12.5 km, which is two times finer than the QSCAT standard ocean wind product at a resolution of 25 km. To evaluate QSCAT performance for high-resolution measurements of lake wind vectors, we compare QSCAT results with Great Lakes Coastal Forecasting System (GLCFS) nowcast wind fields and with standard QSCAT measurements of ocean wind vectors. Although the satellite results over the Great Lakes are obtained with an ocean model function, QSCAT and GLCFS wind fields compare well together for low to moderate wind conditions (4–32 knots). For wind speed, the analysis shows a correlation coefficient of 0.71, a bias of 2.6 knots in mean wind speed difference (nowcast wind is lower) with a root-mean-square (rms) deviation of 3.8 knots. For wind direction, the correlation coefficient is 0.94 with a very small value of 1.3° in mean wind direction bias and an rms deviation of 38° for all wind conditions. When excluding the low wind range of 4–12 knots, the rms deviation in wind direction reduces to 22°. Considering QSCAT requirements designed for ocean wind measurements and actual evaluations of QSCAT performance over ocean, results for high-resolution lake wind vectors indicate that QSCAT performs well over the Great Lakes. Moreover, we show that wind fields derived from satellite scatterometer data before, during, and after a large storm in October 1999, with winds stronger than 50 knots, can monitor the storm development over large scales. The satellite results for storm monitoring are consistent with GLCFS nowcast winds and lake buoy measurements. A geophysical model function can be developed specifically for the Great Lakes using long-term data from satellite scatterometers, to derive more accurate wind fields for operational applications as well as scientific studies.     

Retrieval of ice/water observations from synthetic aperture radar imagery for use in lake ice data assimilation

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.     

Results from the U.S. EPA's Biological Open Water Surveillance Program of the Laurentian Great Lakes: I. Introduction and Phytoplankton Results

The Great Lakes National Program Office of the U.S. EPA has been conducting biological monitoring of the Laurentian Great Lakes since 1983. This paper presents synoptic survey data of phytoplankton communities from all five lakes. These communities were highly diverse, each lake typi-cally supporting over 100 species during both the spring and summer surveys. Much of that diversity was contributed by diatoms, which dominated the plankton of all lakes except Lake Superior in the spring. Summer communities shifted away from diatoms, toward chrysophytes in the upper lakes and chloro-phytes in the lower lakes. Ordination analyses indicated the close similarity of communities in the upper lakes, in particular Lakes Huron and Michigan, and a diverse range of communities in Lake Erie. Floristically, Lake Ontario was fundamentally different from all other lakes.     

Historical changes and current status of crayfish diversity and distribution in the Laurentian Great Lakes

Despite increasing recognition of the importance of invertebrates, and specifically crayfish, to nearshore food webs in the Laurentian Great Lakes, past and present ecological studies in the Great Lakes have predominantly focused on fishes. Using data from many sources, we provide a summary of crayfish diversity and distribution throughout the Great Lakes from 1882 to 2008 for 1456 locations where crayfish have been surveyed. Sampling effort was greatest in Lake Michigan, followed by lakes Huron, Erie, Superior, and Ontario. A total of 13 crayfish species occur in the lakes, with Lake Erie having the greatest diversity (n = 11) and Lake Superior having the least (n = 5). Five crayfish species are non-native to one or more lakes. Because Orconectes rusticus was the most widely distributed non-native species and is associated with known negative impacts, we assessed its spread throughout the Great Lakes. Although O. rusticus has been found for over 100 years in Lake Erie, its spread there has been relatively slow compared to that in lakes Michigan and Huron, where it has spread most rapidly since the 1990s and 2000, respectively. O. rusticus has been found in both lakes Superior and Ontario for 22 and 37 years, respectively, and has expanded little in either lake. Our broad spatial and temporal assessment of crayfish diversity and distribution provides a baseline for future nearshore ecological studies, and for future management efforts to restore native crayfish and limit non-native introductions and their impact on food web interactions.     

Warmer and Drier Climates that Make Terminal Great Lakes

A recent empirical model of glacial-isostatic uplift showed that the Huron and Michigan lake level fell tens of meters below the lowest possible outlet about 7,900 ¹⁴C years BP when the upper Great Lakes became dependent for water supply on precipitation alone, as at present. The upper Great Lakes thus appear to have been impacted by severe dry climate that may have also affected the lower Great Lakes. While continuing paleoclimate studies are corroborating and quantifying this impacting climate and other evidence of terminal lakes, the Great Lakes Environmental Research Laboratory applied their Advanced Hydrologic Prediction System, modified to use dynamic lake areas, to explore the deviations from present temperatures and precipitation that would force the Great Lakes to become terminal (closed), i.e., for water levels to fall below outlet sills. We modeled the present lakes with pre-development natural outlet and water flow conditions, but considered the upper and lower Great Lakes separately with no river connection, as in the early Holocene basin configuration. By using systematic shifts in precipitation, temperature, and humidity relative to the present base climate, we identified candidate climates that result in terminal lakes. The lakes would close in the order: Erie, Superior, Michigan-Huron, and Ontario for increasingly drier and warmer climates. For a temperature rise of T°C and a precipitation drop of P% relative to the present base climate, conditions for complete lake closure range from 4.7T + P > 51 for Erie to 3.5T + P > 71 for Ontario.     

Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.     

Using Microfossil Remains in Lake Sediments to Examine the Invasion of Eubosmina coregoni (Cladocera,Bosminidae) in Lake of the Woods,Ontario, Canada

Many nonindigenous species (NIS) present in the Laurentian Great Lakes are expanding their ranges to inland lakes and streams. This study used cladoceran microfossils to examine the invasion history of Eubosmina coregoni, the first known nonindigenous zooplankter to invade Lake of the Woods (LOW), Ontario, Canada. Sediment cores from 16 sites in LOW were used to analyze broad-scale presence/absence of E. coregoni prior to human development (bottom sediment samples) in comparison with present-day distribution (top sediment samples). E. coregoni had the highest relative abundance in the northern and eastern regions of LOW and the abundance of all cladoceran remains was low in the southern region of the lake. A long core (time core) from Clearwater Bay provided a more detailed historical account of E. coregoni's abundance in the northern region of LOW, indicating that E. coregoni was first detected in the lake in the early 1990s, approximately 25 years after it was discovered in the Laurentian Great Lakes. Results obtained in this study have illuminated temporal and spatial patterns of colonization of this inland water body. Study of the early invasion dynamics of NIS in these inland lakes may aid in the prevention of future invasions of taxa that have already altered the food web dynamics in the Laurentian Great Lakes.     

Trends in Water Clarity of the Lower Great Lakes from Remotely Sensed Aquatic Color

Satellite observations of aquatic colour enable environmental monitoring of the Great Lakes at spatial and temporal scales not obtainable through ground-based monitoring. By merging data from the Coastal Zone Color Scanner (CZCS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly binned images of water-leaving radiance over the Great Lakes have been produced for the periods 1979–1985 and 1998–2006. This time-series can be interpreted in terms of changes in water clarity, showing seasonal and inter-annual variability of bright-water episodes such as phytoplankton blooms, re-suspension of bottom sediments, and whiting events. Variations in Secchi disk depth over Lakes Erie and Ontario are predicted using empirical relationships from coincident measurements of water transparency and remotely-sensed water-leaving radiance. Satellite observations document the extent to which the water clarity of the lower Great Lakes has changed over the last three decades in response to significant events including the invasion of zebra mussels. Results confirm dramatic reductions in Lake Ontario turbidity in the years following mussel colonization, with a doubling of estimated Secchi depths. Evidence confirms a reduction in the frequency/intensity of whiting events in agreement with suggestions of the role of calcium uptake by mussels on lake water clarity. Increased spring-time water clarity in the eastern basin of Lake Erie also corroborates previous observations in the region. Despite historical reports of localised increases in transparency in the western basin immediately following the mussel invasion, image analysis shows a significant increase in turbidity between the two study periods, in agreement with more recent reports of longer term trends in water clarity. Through its capacity to provide regular and readily interpretable synoptic views of regions undergoing significant environmental change, this work illustrates the value of remotely sensing water colour to water clarity monitoring in the lower Great Lakes.