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Cyanobacteria Assessment Network (CyAN)

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An EPA, NASA, NOAA, and USGS Project

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 develop an early warning indicator system to detect algal blooms in U.S. freshwater systems. This research supports federal, state, tribal, and local partners in their monitoring efforts to assess water quality to protect aquatic and human health.

Project Overview

The mission of the CyAN's project is to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying algal blooms and related water quality using satellite data records. 

Goals

  • Develop a uniform and systematic approach for identifying cyanobacteria blooms across the contiguous U.S. using ocean satellites.
  • Create a strategy for evaluation and refinement of algorithms across satellite platforms.
  • Identify landscape linkage postulated causes of chlorophyll a and cyanobacterial blooms in freshwater systems.
  • Characterize exposure and human health effects using ocean color satellites in drinking water sources and recreational waters.
  • Characterize responses and economic value of an early warning system using ocean satellites and a mobile dissemination platform.
  • Disseminate satellite data through a mobile application and EnviroAtlas.

Planned Outcomes

  • Create a standard and uniform approach for early identification of algal blooms that is useful and accessible to stakeholders of freshwater systems using the new set of satellites: Ocean Land Colour Instrument (OLCI) on Sentinel-3, Sentinel-2, Landsat and future NASA missions.
  • Develop an information dissemination system for expedient public health advisory postings.
  • Better understand connections between health, economic, and environmental conditions to cyanobacteria and phytoplankton blooms.

Project Timeline

The CyAN project officially started October 1, 2015. It provided continental U.S. coverage using the MERIS archive from 2002-2012 in Fiscal Year 2017. Weekly composites of the European Space Agency Copernicus Sentinel-3 OLCI sensor data are now available to collaborators for initial review and validation. Landsat surface water temperature product is now publicly available through USGS EROS.

Project Components and Fiscal Year Updates

[Validation] [Satellite Algorithms] [Cross-Satellite Platforms] [Environmental Assessment] [Human Health] [Economics] [Decision Support]

Validation

In situ validation data will primarily come from our federal and state collaborators. Sources of data will include, but are not limited to, federal, state, and local government agencies, universities, private research groups, and published peer-reviewed journals. Minimum data reporting requirements include sample station identification, cyanobacteria counts, abundance, or phycocyanin pigment concentration, latitude, longitude, depth, and date. Additional information that are not required but are considered beneficial include chlorophyll a concentration (especially), temperature, secchi depth, turbidity, and other available water quality measures. Data sets will undergo quality review by confirming that all methods used were documented and widely accepted.

  • Figure 1. CONUS streetmap with site data from CyAN Field Integrated Exploratory Lakes Database.Figure 1. CONUS streetmap with site data from CyAN Field Integrated Exploratory Lakes Database. Fiscal Year 2019 Update: A field sampling effort was undertaken in Kansas, Minnesota, North Carolina, and Wisconsin to increase the number of in situ match-ups for validating the Cyanobacteria Index algorithm. The in situ sampling suite included radiometry, phytoplankton species composition, chlorophyll, cyanotoxins, and secchi depth and resulted in at least two dozen match-ups across Sentinel-3A and –3B. The CyAN Field Integrated Exploratory Database (FIELD) is going through internal review for public release and data for the Great Lakes is now being collated. The code for the CyAN FIELD tool has been provisionally released and the Land Change Monitoring, Assessment, and Projection (LCMAP) product is currently being evaluated for consistency in a watershed in Kansas for annual landcover/land change using Landsat.
  • Fiscal Year 2018 Update: CyAN has developed appropriate statistical metrics for satellite algorithm comparison and validation. The metrics for algorithm comparison are critical as an evaluation tool for managers interested to know which algorithms may preform best in their areas of interest. The open access CyAN Field Integrated Exploratory Lakes Database (CyAN FIELD) tool continues development using R-script, R-Studio, Shiny app interface to visualize and QA field and laboratory measurements for parameters relevant to validation of remote sensing products. The database has automated QA queries to avoid having to manually check each record and to check for inter-parameter agreement.
  • Fiscal Year 2017 Update: As seen in Figure 1, the CyAN Field Integrated Exploratory Lakes Database (CyAN FIELD) functionality has been expanded to include integrated tools for interactive quality control and basic exploratory analysis to evaluate data trends. Automated quality control queries have been added to evaluate internal consistency of sample location information, and that interrelated chemical, biological, and physical data are internally consistent.

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Satellite Algorithms

A strategy for evaluation of algorithm updates has been established in large part through the open source availability of the NASA ocean color processing software (l2gen) and the SeaWiFS Data Analysis System (SeaDAS). This project will perform a complementary effort by using existing products for MERIS (Medium Resolution Imaging Spectrometer) and OLCI that have shown management value to establish algorithm development and data processing infrastructure. We propose to adopt second derivative spectral shape algorithms, which have been shown to be robust in the presence of poor atmospheric correction. For MERIS data, the bands at 620, 665, 681, 709, and 754 nanometers are used. The Cyanobacteria Index algorithm estimates cyanobacteria concentrations and the algorithm has been successfully transferred to MODIS (Moderate Resolution Imaging Spectroradiometer).

  • Fiscal Year 2019 Update: Algorithm updates included improved quality flags for invalid pixels and atmospheric stray light (sometimes called land adjacency) and a revised land mask. The color table for the standard image products was adapted to a perceptually uniform color based on a modified form of the viridis system, which is one of the default tables in python’s matplotlib. This form also addresses color deficiency, which is poorly handled with the “rainbow” method. CyAN now includes the addition of Alaska to the standard processing stream, generation daily images, and true color daily imagery for both the continental U.S. and Alaska at the request of the states. National chlorophyll validation with in situ data from the Water Quality Portal was underway.
  • Fiscal Year 2018 Update: The Sentinel-2 satellite constellation shows some potential for deriving chlorophyll a across lakes in the United States. Work is ongoing to better understand which conditions are appropriate for use of algorithms at higher resolution scales. Flags for non-bloom, non-water conditions are still factors limiting the MERIS and OLCI data sets. Several factors have been examined, including thin ice and atmospheric adjacency in extremely turbid atmospheres. Modifications to the flags are being developed to exclude these characteristics.
  • Fiscal Year 2017 Update: Standardized algorithm intercomparison metrics have been developed and were presented and incorporated into recommendations at the biannual International Ocean Colour Science team meeting. Toxin distributions are a concern to managers, however remote sensing cannot detect toxins. A strategy for estimating toxin levels with satellite data has been identified. In addition, the relative sensitivity of phycocyanin against chlorophyll for cyanobacteria detection was determined: two to four times as much phycocyanin is needed to detect the same amount of cyanobacteria biomass as can be detected with chlorophyll.

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    Cross-Satellite Platforms

    The reliable application of any remote-sensing algorithm over a large area requires a strategy for its evaluation, validation, and refinement on multiple spatial and temporal scales using field reference data. As our knowledge of the statistical and analytical relationships within the algorithm improve with time, successes and failures need to be understood, as does the ongoing need for refinement of algorithm parameterizations. Using in situ data as reference and data from multiple ocean color satellite instruments, we will compare (1) model output from in situ radiometry vs. in situ metrics for cyanobacteria, (2) satellite radiometry vs. in situ radiometry and model output from satellite radiometry vs. in situ metrics for cyanobacteria, and (3) model outputs from multiple satellite instruments (MERIS and Landsat).

    • Figure 2. Example of how CyAN divides the satellite images into smaller files. The numbered tiles each represent a spatial area that will be made into a separate file. The data file name will use the row and column number to identify the tile location. SaFigure 2. Example of how CyAN divides the satellite images into smaller files. The numbered tiles each represent a spatial area that will be made into a separate file. The data file name will use the row and column number to identify the tile location. Satellite data is from the European Space Agency Envisat MERIS and Copernicus Sentinel-3 Ocean and Land Colour Imager (OLCI) sensors Fiscal Year 2019 Update: Validation of the Sentinel-2 Maximum Chlorophyll Index (MCI) is complete. Comparison of Sentinel-2 chlorophyll measurements to in situ chlorophyll a measurements indicates that some error is present but that the two generally agree on whether concentrations are high or low.
    • Fiscal Year 2018 Update: CyAN has generated mission-long daily and weekly composites of MERIS and OLCI cyanobacteria and chlorophyll a data products for the continental U.S. A reprocessing of these time-series is anticipated in winter 2018. The reprocessing will encompass updates to the algorithm, the approach for identifying mixed land-water pixels, and the land mask. Improvements have been made to the geotiff data files so metadata is now contained in the geotiff files, and the format is compatible with ArcGIS.
    • Fiscal Year 2017 Update: As seen in Figure 2, CyAN generated MERIS CI composites for the full mission at 300 m for the full continental United States (CONUS). Temporal scales for these composites are 14-days for 2002-2007, when MERIS CONUS coverage was incomplete, and 7-days for 2008-2012. These MERIS CI data are available for project collaborators evaluation. As of August 30, 2017, CyAN is processing Sentinel-3 OLCI in forward stream.

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    Environmental Assessment

    The Environmental Component of the CyAN project focuses on the evaluation of the existing satellite data to document changes in land-cover composition, land-use activities, chlorophyll a, and cyanobacteria concentrations.

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