데이터셋 상세
미국
2018 extent CI data
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.
데이터 정보
연관 데이터
CONUS extent
공공데이터포털
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).
Lake cyanoHAB occurrence
공공데이터포털
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).
Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources
공공데이터포털
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).
Cyanobacteria Index (MERIS)
공공데이터포털
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).
CONUS extent
공공데이터포털
The dataset provided here contains the added pixel quality assurance (QA) flags that help ensure validity of satellite-derived water quality estimates in freshwater lakes and reservoirs. This work builds upon a study by Urquhart et al. [1] where inland lake and waterbody satellite data was processed and flagged for California, Ohio, and Florida. Added pixel QA flags include land-adjacent pixels, unresolvable waterbody pixels, and snow/ice pixels. Any water pixel adjacent to land is flagged to caution potential for mixed land-water pixels and land adjacency effects. A weekly QA flag mask is provided for snow/ice presence over lakes. The unresolvable QA flag mask contains inland waterbodies smaller than 27 hectares and/or with less than three 300m resolvable satellite pixels. An updated version of the Shuttle Radar Topography Mission (SRTM) Waterbody Data (SWBD) is provided to fix a land-waterbody mask error identified in Rhode Island and Massachusetts. The Research Environments MEaSUREs SRTM, used in the NASA data pre-processing, includes the Water Body Data Shapefiles (~30m) product. Version 3.0 of the SRTM contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data, in shapefile and rasterized formats [4]. Version 4.0 of the SRTM presented here fixes the land-waterbody mask error identified in Rhode Island and Massachusetts. Version 4.0 of the SRTM has been adopted into the NASA pre-processing of the MERIS and OLCI satellite datafiles described above. 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).
Assessing temporal frequency of cyanobacterial blooms using imagery from the Sentinel-3A satellite sensor.
공공데이터포털
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).
Assessing temporal frequency of cyanobacterial blooms using imagery from the Sentinel-3A satellite sensor.
공공데이터포털
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).
Data for satellites predict lakes at risk from cyanobacteria and microcystin toxins
공공데이터포털
Data for analysis for modeling probability of measuring microcystin toxin, cyanobacteria cell abundance, and chlorophyll a concentration in ~2,200 lakes based on cyanobacteria summer bloom magnitude measured by satellite imagery. This dataset is associated with the following publication: Handler, A., J. Compton, R. Hill, S. Leibowitz, and B. Schaeffer. Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 869: 161784, (2023).
INLA CONUS forecast
공공데이터포털
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).
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