Assessing temporal frequency of cyanobacterial blooms using imagery from the Sentinel-3A satellite sensor. This dataset is associated with the following publication: Coffer, M., B. Schaeffer, W. Salls, E. Urquhart, K.A. Loftin, R.P. Stumpf, P.J. Werdell, and J. Darling. Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 128: 107822, (2021).

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).

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).

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).

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).

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).

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).

Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical, and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States. Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models. The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria incidence and proliferation. Data is provided in a comma-separated values (CSV) format.

Data support the publication "Spatio-Temporal Modeling For Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida". This dataset is associated with the following publication: Myer, M., E. Urquhart, B. Schaeffer, and J. Johnston. Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida. Frontiers in Environmental Science. Frontiers, Lausanne, SWITZERLAND, 8: 581091, (2020).