This dataset shows the concentration of cyanobacteria cells/ml in fresh water bodies and estuaries of the Ohio and Florida derived from 300x300 meter MEdium Resolution Imaging Spectrometer (MERIS) satellite imagery. This dataset was produced through partnership with the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the United States Environmental Protection Agency (USEPA). This cyanobacteria dataset was derived using the European Space Agency (ESA) Envisat satellite and MERIS instrument. MERIS is a 68.5 degree field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). MERIS imagery was used to identify long-wavelength spectral bands (from red through near-infrared portion of the spectrum) to locate algal blooms within freshwaters and estuaries of the continental United States. This dataset is associated with the following publication: Urquhart, E., B. Schaeffer, R. Stumpf, K. Loftin, and J. Wedell. .A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 67: 144-152, (2017).

ESA Envisat MERIS 300mx300m raster ocean color data using cyanobacteria index (CI) algorithm. This dataset is associated with the following publication: Coffer, M., B. Schaeffer, J. Darling, E. Urquhart, and W. Salls. Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 111: 105976, (2020).

This dataset shows the concentration of cyanobacteria cells/ml in fresh water bodies and estuaries of the Ohio and Florida derived from 300x300 meter MEdium Resolution Imaging Spectrometer (MERIS) satellite imagery. This dataset was produced through partnership with the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the United States Environmental Protection Agency (USEPA). This cyanobacteria dataset was derived using the European Space Agency (ESA) Envisat satellite and MERIS instrument. MERIS is a 68.5 degree field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). MERIS imagery was used to identify long-wavelength spectral bands (from red through near-infrared portion of the spectrum) to locate algal blooms within freshwaters and estuaries of the continental United States. This dataset is associated with the following publication: Clark, J., B. Schaeffer, J. Darling, E. Urquhart, J. Johnston, A. Ignatius, M. Myer, K. Loftin, J. Werdell, and R. Stumpf. Methods for Monitoring Cyanobacterial Harmful Algal Bloom Frequency in Recreational Waters and Drinking Water Sources with Satellites. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 80: 84-95, (2017).

This dataset shows the concentration of cyanobacteria cells/ml in fresh water bodies and estuaries of the Ohio and Florida derived from 300x300 meter MEdium Resolution Imaging Spectrometer (MERIS) satellite imagery. This dataset was produced through partnership with the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the United States Environmental Protection Agency (USEPA). This cyanobacteria dataset was derived using the European Space Agency (ESA) Envisat satellite and MERIS instrument. MERIS is a 68.5 degree field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). MERIS imagery was used to identify long-wavelength spectral bands (from red through near-infrared portion of the spectrum) to locate algal blooms within freshwaters and estuaries of the continental United States. This dataset is not publicly accessible because: The dataset describing locations of surface drinking water intakes was obtained through Office of Water's Office of Ground Water and Drinking Water. This dataset is not publicly available for security reasons. While location data were used in our analysis, no intake locations were revealed and data were handled according to security specifications provided by OW. This dataset will therefore not be made available to the public through ScienceHub or any other outlet. It can be accessed through the following means: Contact corresponding author for additional information. Format: Assessing temporal frequency of cyanobacterial blooms at drinking water intakes using imagery from the Sentinel-3A satellite sensor. This dataset is associated with the following publication: Clark, J., B. Schaeffer, J. Darling, E. Urquhart, J. Johnston, A. Ignatius, M. Myer, K. Loftin, J. Werdell, and R. Stumpf. Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 80: 84-95, (2017).

MERIS and OLCI satellite data with CI_cyano algorithm quantify the spatial extent of cyanobacteria. This dataset is associated with the following publication: Schaeffer, B., E. Urquhart, M. Coffer, W. Salls, R. Stumpf, K. Loftin, and P.J. Werdell. Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 140: 108990, (2022).

Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data.

OLCI satellite data with CI_cyano algorithm quantify cyanobacteria. Air temperature, precipitation data from PRISM Climate group. This dataset is associated with the following publication: Schaeffer, B., N. Reynolds, H. Ferriby, W. Salls, D. Smith, J. Johnston, and M. Myer. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 349: 119518, (2024).

OLCI satellite data with CI_cyano algorithm quantify cyanobacteria. Air temperature, precipitation data from PRISM Climate group. This dataset is associated with the following publication: Schaeffer, B., N. Reynolds, H. Ferriby, W. Salls, D. Smith, J. Johnston, and M. Myer. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 349: 119518, (2024).

Data represents satellite derived measures of cyanobacteria biomass across largest US lakes. This dataset is not publicly accessible because: Already publicly available at link below. It can be accessed through the following means: Original satellite files are publicly available at https://oceancolor.gsfc.nasa.gov/about/projects/cyan/. Format: GeoTIFF data, HE5 files. This dataset is associated with the following publication: Coffer, M., B. Schaeffer, W. Salls, J. Minucci, and O. Cronin-Golomb. Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations. INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, USA, 47(1): 177-199, (2026).

Full resolution (300m), weekly MERIS data were obtained over the contiguous United States for 2008 through 2011. Seven day composite images were created by retaining the maximum value detected for each pixel within the time period and then the monthly mean of the maximums was calculated. A spatial mosaic composed of 54 individual scenes was generated for each week resulting in a total of 208 CONUS images from 2008 to 2011. Weekly OLCI data were also retrieved from January 2017 through December 2017. MERIS and OLCI data were processed by the NASA OBPG, using the SeaWiFS Data Analysis System, SRTM static land mask, and a transformation to Albers Equal Area with an area-weighted interpolation to match the projections of the National Hydrography Dataset High Resolution. The SRTM land mask and SeaDAS processing is static in relation to waterbody size and did not account for periods of drought nor flood during the study period. Any water pixel adjacent to the SRTM static land mask was automatically flagged and excluded from analysis to reduce potential for mixed land-water pixels and land adjacency effects. To avoid pixel misclassification due to artifacts such as bridges, catchment facilities, and islands, “valid” water pixels were determined by examining the ESRI World Imagery Basemap. This manual operator pixel selection process lowered the potential that land pixels were not misclassified as water or vice versa. Each 300m satellite pixel in a weekly CONUS map represents a provisional maximum Cyanobacteria Index (CI) value retrieved in the specific time period. The provisional CI was calculated using a spectral shape (SS) algorithm detailed and validated elsewhere. Conventional methods to distinguish between ice and water often fail due to high ice reflectance as well as the possibility of cyanobacterial biomass formation under the ice. Therefore, weekly MERIS data were masked for the presence of ice and snow using Interative Multisensor Snow and Ice Mapping System (IMS) Northern Hemisphere Snow and Ice Analysis data (Version 1, 4km). Daily snow and ice data were obtained from the National Snow and Ice Data Center then cropped to the extent of CONUS. Snow and ice data were temporally binned into maximum weekly time composites to match the MERIS data and then converted from raster to shapefile format. If MERIS or OLCI CI values were within the spatial area of the snow and ice mask, they were removed from further analysis. This dataset is associated with the following publication: Urquhart, E., and B. Schaeffer. Envisat MERIS and Sentinel-3 OLCI satellite lake biophysical water quality flag dataset for the contiguous United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 28: 104826, (2020). NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person.