The Laurentian Great Lakes, often resembling inland seas, exhibit complex interactions between wind and waves that influence lake conditions. Monitoring these dynamics currently relies on buoy-based measurements and model predictions. Recent advancements in commercial satellite technology now provide high-revisit, high-resolution Synthetic Aperture Radar (SAR) imagery, such as ICEYE’s X-band data from the NASA Commercial Satellite Data Acquisition (CSDA) program. SAR offers all-weather, day-and-night capabilities, making it a valuable tool for Great Lakes monitoring. In this study, we assess the effectiveness of ICEYE SAR for lake monitoring, focusing on western Lake Erie. We evaluate multiple imaging modes and find that Stripmap Mode provides the highest radiometric and geometric accuracy for large water bodies. We show that this high-resolution SAR enables effective monitoring of wind speed across the western basin and captures short-period (∼4 s) wave activity—capabilities that surpass lower-resolution SAR sensors such as Sentinel-1. We also explore additional applications. First, we present a speculative case study comparing harmful algal bloom (HAB) detection using Electro-optical (EO) and ICEYE SAR imagery, suggesting SAR can detect HABs through small-scale wave-damping effects, offering a complementary tool for water-quality monitoring. Second, we examine ice-covered Saginaw Bay and find that X-band SAR returns can identify different Great Lakes ice types. Together, this represents the first operational demonstration of ICEYE for inland waters. Future work should further evaluate the robustness of SAR-derived products to improve environmental monitoring across the Great Lakes and other freshwater systems.

Water clarity, as measured by Secchi disk depth (ZSD) or diffuse attenuation (Kd), is an important indicator of a lake's ecosystem state and can be reliably retrieved using satellite remote sensing. By combining data from multiple satellite missions, the European Space Agency's Ocean Colour Climate Change Initiative (OC-CCI) aims to deliver stable, long-term, satellite data products suitable for trend assessments. Here we demonstrate the value of OC-CCI products for reporting on water clarity status and long-term trends in North America's largest lakes. Extensive matchups between the OC-CCI Kd at 490 nm (Kd490) and in situ Z SD observations spanning 25 years enabled robust multi-lake validation of Z SD retrievals over a wide range of water clarity conditions (R2 = 0.9, MAPE = 29.6 %, BIAS = 6.6 %, N = 4297) providing a transferable model for large-scale mapping of inland water clarity. Significant differences in Z SD retrieval uncertainty were observed between years, missions, and specific periods marking changes in the sensor datasets contributing to the OC-CCI products. Bias-correction of the OC-CCI Kd 490 provided confidence in the assumption of seamless continuity in this multi-mission dataset, thereby allowing long-term time-series analyses. Seasonal, inter-annual and inter-decadal variability and trends in lake-wide average Z SD were subsequently evaluated for nine large lakes across Canada and the U.S. over the 1998-2023 period, capturing the timing and magnitude of significant shifts in water clarity conditions. Observations agree well with documented periods of ecosystem change in response to the cumulative impacts from harmful algal blooms, nutrient status, invasive species, and hydrological events.

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies.

Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community going forward, while offering a vision of what global phytoplankton community composition will look like through the “eyes” of PACE.

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Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies.

Monitoring large bodies of water, such as the Laurentian Great Lakes in North America, can be challenging and costly. The bathymetry, the diffuse attenuation coefficient for downwelling irradiance ( $K_{d}$ ), and the particulate backscattering coefficient ( $b_{\text {bp}}$ ) are important metrics in monitoring water quality in lakes and have typically been measured in two ways: 1) via in situ sampling campaigns, which are expensive, time-consuming, and have a low spatial resolution; and 2) via passive optical imagery, which can have errors in excess of 50%. Recently, Ice, Cloud, and land Elevation Satellite-2 (ICESAT-2), an active light detection and ranging (LiDAR)-based satellite, has proven effective in deriving the bathymetry, $K_{d}$ , and $b_{\text {bp}}$ in the global oceans. However, validation of such metrics has never been done on satellite flyovers taken on the same day as in situ measurements. Likewise, studies on freshwater environments have been limited. Here, we compare in situ data sampled from Lake Michigan and Big Glen Lake between August 13th and 14th, 2021, and results derived from an ICESat-2 flyover in the same region on August 14th, 2021. We find excellent agreement between the in situ values and the satellite-derived values for all three metrics. This suggests that ICESat-2 and other future LiDAR-based satellites will be powerful tools for monitoring large freshwater lakes.

The Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in 2006 with the primary goal of measuring the properties of clouds and aerosols in Earth’s atmosphere using LiDAR. Since then, numerous studies have shown the viability of using CALIPSO to observe day/night differences in subsurface optical properties of oceans and large seas from space. To date no studies have been done on using CALIPSO to monitor the subsurface optical properties of large, freshwater-lakes. This is likely due to the limited spatial resolution of CALIPSO, which makes the mapping of subsurface properties of regions smaller than large seas impractical. Still, CALIPSO does pass over some of the world’s largest, freshwater-lakes, yielding important information about the water. Here we use the entire CALIPSO data record (approximately 15 years) to measure the particulate backscatter coefficient ( b bp , m −1 ) across Lake Michigan. We then compare the LiDAR derived values of b bp to optical imagery values obtained from MODIS and to in situ measurements. Critically, we find that the LiDAR derived b bp aligns better in non-summer months with in situ values when compared to the optically imagery. However, due to both high cloud coverage and high wind speeds on Lake Michigan, this comes with the caveat that the CALIPSO product is limited in its usability. We close by speculating on the roll that spaceborne LiDAR, including CALIPSO and other satitlites, have on the future of monitoring the Great Lakes and other large bodies of fresh water.

Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance SAV study and management, especially if these approaches can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present an uncrewed aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM), primarily using a six-band Tetracam multispectral camera, flown over sites in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. We analyzed water chemistry and light data and found our sites clustered into sites with higher and lower water clarity, although all sites had relatively high water clarity. The overall average accuracy achieved was 76.7%, with 78.7% producer’s and 77.6% user’s accuracy for the EWM. These accuracies were higher than previously reported from other studies that used remote sensing to map SAV. Our study found that two tested scale parameters did not lead to significantly different classification accuracies between sites with higher and lower water clarity. The EWM classification methodology described here should be applicable to other SAV species, especially if they have growth patterns that lead to high amounts of biomass relative to other species in the upper water column, which can be detected with the type of red-edge and infrared sensors deployed for this study.

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry.

ABSTRACT Freshwater phytoplankton carbon fixation is an important water quality parameter that can provide information about the health of lake ecosystems as well as their impacts on regional carbon cycling dynamics. Traditional field methods for measuring and monitoring primary production are unable to capture the necessary spatial and temporal variability at the global scale due to the sheer number and diversity of the world’s lakes. Satellite remote sensing offers a potential tool to quantify freshwater lake primary production on a global scale. A new straightforward remote sensing approach was developed to estimate global freshwater carbon fixation from satellite observable lakes using a straightforward depth-integrated model (DIM). A key component of this approach is the estimation of the light utilization index, ψ, for freshwater systems. A significant negative linear model to estimate growing season ψ as a function of latitude was developed from data acquired through an exhaustive literature review. In conjunction with a previous remote sensing generated freshwater chlorophyll-a concentration data set, the DIM was used to compute growing season carbon fixation for 80,000 freshwater lakes. While these estimates are rough and could exhibit large errors for any given lake, they provide a reasonable synoptic global estimate of freshwater carbon fixation. In general, growing season areal carbon fixation was shown to decrease with increasing latitude in both northern and southern hemispheres. Carbon fixation rates in the southern hemisphere were found to be significantly higher than in the northern hemisphere, with the African continent exhibiting the highest rates. Total daily carbon fixation (areal rate × lake area) was estimated at 1.03 teragrams of carbon per day (Tg C day−1) with approximately 71% occurring in the northern hemisphere. Total fixation was highest in North America, which was dominated by a very large number of Canadian Shield lakes. In general, total carbon fixation was well explained by total lake surface area, except in the far northern latitudes where lakes are more oligotrophic due to limited nutrient availability. This analysis resulted in a new freshwater carbon fixation product that provides new insights into the role freshwater lakes play in the global carbon budget.

A new hyperspectral bio-optical framework is being developed for improved freshwater harmful algal bloom mapping in preparation for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Preliminary results for Lake Erie are presented.

The states of Michigan and Ohio issued shutdown orders in mid-March 2020 in an attempt to slow the spread of the coronavirus (COVID-19), resulting in widespread disruption to economic and human activity. This study, which was commissioned by NASA headquarters, utilized satellite remote sensing data from the Visible Infrared Imaging Radiometer Suite sensor onboard the Suomi National Polar-orbiting Partnership satellite to investigate whether these changes in activity led to any short-term changes in water quality in the Great Lakes region by comparing 2020 data to a historic baseline. The water quality parameters examined included chlorophyll-a (CHL) and total suspended solids (TSS) concentrations, water clarity, and harmful algal bloom (HAB) extent. These parameters were investigated in two Great Lakes basins which experience significant anthropogenic pressure: the western basin of Lake Erie (WBLE) and Saginaw Bay in Lake Huron (SBLH). TSS concentrations in April 2020 were below the historic baseline in both basins, and largely remained low until September. SBLH also experienced elevated CHL concentrations in April which persisted through the summer. Additionally, the WBLE HAB extent was down in 2020 after an early end to the growing season. However, this investigation found that the COVID-19 shutdowns were likely not a direct driver of these short-term anomalies. Instead, recent trends in the indicators and co-occurring anomalies in hydrological and meteorological conditions (e.g., lake temperature, river discharge, and wind speed) appeared to be more responsible for the detected water quality changes. Future work will investigate whether the shutdowns have a long-term or delayed impact on Great Lakes water quality.

Large freshwater lakes provide immense value to the surrounding populations, yet there is limited understanding of how these lakes will respond to climate change and other factors. This study uses satellite remote sensing to estimate annual, lake-wide primary production in 11 of the world’s largest lakes from 2003–2018. These lakes include the five Laurentian Great Lakes, the three African Great Lakes, Lake Baikal, and Great Bear and Great Slave Lakes. Mean annual production in these lakes ranged from under 200 mgC/m2/day to over 1100 mgC/m2/day, and the lakes were placed into one of three distinct groups (oligotrophic, mesotrophic, or eutrophic) based on their level of production. The analysis revealed only three lakes with significant production trends over the study period, with increases in Great Bear Lake (24% increase over the study period) and Great Slave Lake (27%) and a decline in Lake Tanganyika (−16%). These changes appear to be related to climate change, including increasing temperatures and solar radiation and decreasing wind speeds. This study is the first to use consistent methodology to study primary production in the world’s largest lakes, allowing for these novel between-lake comparisons and assessment of inter-annual trends.

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015-2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 hours) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.

Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (>18 mg/m(3)) combined with water temperature >20 degrees C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the 20-year period from 1998 to 2017. The resulting classified maps were validated by comparison with historical in situ measurements, exhibiting good agreement (81% accuracy). Trends in the annual mean and maximum total submerged and surface scum extents demonstrated significant positive increases from 1998 to 2017. There was also an apparent 76% increase in year-to-year variability of mean annual extent between the 1998-2010 and 2011-2017 periods. The 1998-2017 time-series was also compared with several different river discharge nutrient loading metrics to assess the ability to predict annual cyanoHAB extents. The prediction models displayed significant relationships between spring discharge and cyanoHAB area; however, substantial variance remained unexplained due in part to the presence of very large blooms occurring in 2013 and 2015. This new multi-sensor time-series and associated statistics extend the current understanding of the extent, location, duration, and temporal patterns of cyanoHABs in western Lake Erie. (C) 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatiotemporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (<4.9 m/s) and wave heights (<027 m). Once wind speed and wave height exceeded these thresholds the assumption of vertically uniform cyanobacteria populations was justified. These findings suggest that remote sensing can provide adequate estimates of water column cyanoHAB biomass in most conditions; however, the incorporation of bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

Lake Erie is a classic case of development, recovery from, and return to eutrophication, hypoxia, and harmful algal blooms. Forecast models are used annually to predict bloom intensity for the whole Western lake Erie Basin, but do not necessarily reflect nearshore conditions or regional variations, which are important for local stakeholders. In this study we: 1) developed relationships between observed whole basin and nearshore bloom sizes, and 2) updated and extended a Bayesian seasonal bloom forecast model to provide new regional predictions. The western basin was subdivided into 5 km near-shore regions, and bloom start date, size, and intensity were quantified with MODIS-derived images of chlorophyll concentrations for July-October 2002-2016 for each subdivision and for the entire basin. While bloom severity within each subdivision is temporally and spatially unique, it increased over the study period in each subdivision. The models for the 5 km subdivisions explained between 83 and 95% of variability between regional sizes and whole bloom size for US subdivisions and 51% for the Canadian subdivision. By linking predictive basin-wide models to regional regression estimates, we are now able to better predict potential bloom impacts at scales and in specific areas that are vital to the economic well-being of the region and allow for better management responses. (C) 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.