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.

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

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).

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.

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

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.