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SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, 1984 - 2016 (ongoing)

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DataCite Commons2023-04-07 更新2025-04-15 收录
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https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sbc.74.10
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This data file represents a time series of canopy biomass of the giant kelp, Macrocystis pyrifera, derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) satellite imagery, along with relevant metadata. The kelp canopy is composed of the portions of fronds floating on the surface of the water. Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Año Nuevo to San Diego, CA, including the Northern and Southern Channel Islands. Data were derived from the three Landsat sensors listed above. Observations are made on a 16 day repeat cycle, for each instrument, but the temporal coverage is irregular because of cloud cover, instrument failure, and the mission length of each sensor (TM: 1984 – 2011, ETM+: 1999 – present, OLI: 2013 – present). Estimates of kelp canopy biomass are derived from the relationship between satellite surface reflectance and empirical measurements of kelp canopy biomass in long-term SBC LTER study plots obtained using SCUBA. The different Landsat sensors were calibrated to each other using simulated Landsat data derived from hyperspectral imagery. Missing data due to the ETM+ scan line corrector error were filled using a synchrony-based gap filling method. Data are organized into a single NetCDF file and contain the quarterly means for each Landsat pixel across the three sensors. Relevant metadata such as number of Landsat estimates from which the mean was derived, the number of estimates from each sensor, standard error for each quarterly estimate, spatial coordinates, and date are all included in the file. For assistance with the data, please contact sbclter@msi.ucsb.edu.
提供机构:
Environmental Data Initiative
创建时间:
2017-11-13
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