10.3334/ORNLDAAC/1837
Yang, Y.
Y.
Yang
Saatchi, S.S.
S.S.
Saatchi
CMS: Terrestrial Carbon Stocks, Emissions, and Fluxes for Conterminous US: 2001-2016
Carbon Monitoring System (CMS)
ORNL Distributed Active Archive Center
2021
LAND SURFACE, SOILS, SOIL RESPIRATION, HETEROTROPHIC RESPIRATION (Rh)
LAND SURFACE, SOILS, CARBON, NET ECOSYSTEM CO2 EXCHANGE (NEE)
LAND SURFACE, SOILS, CARBON, SOIL ORGANIC CARBON (SOC)
LAND SURFACE, SOILS, SOIL RESPIRATION, AUTOTROPHIC RESPIRATION (Ra)
BIOSPHERE, ECOSYSTEMS, TERRESTRIAL ECOSYSTEMS,
BIOSPHERE, VEGETATION, LITTER CHARACTERISTICS,
BIOSPHERE, VEGETATION, BIOMASS,
LAND SURFACE, SOILS, SOIL PRODUCTIVITY, GROSS PRIMARY PRODUCTION (GPP)
LAND SURFACE, SOILS, CARBON,
DEM, SRTM
ALOS-2, PALSAR-2
ALOS, PALSAR
Aqua, MODIS
LANDSAT-8, OLI
LANDSAT-5, TM
Environmental Modeling, Computer
FIA plot data
aboveground biomass
model input data
carbon fluxes
carbon pools
CARDAMOM
ORNL DAAC
0000-00-00
0000-00-00
2001-01-01/2016-12-31
en
https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=33
Data Files
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This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon. Carbon fluxes include gross primary production (GPP), net primary production (NPP), net biome exchange, autotrophic respiration, and heterotrophic respiration. The modeled estimates are provided as monthly averages over the 16-year period, 2001 through 2016. The data were derived from the CARbon DAta MOdel fraMework (CARDAMOM) that included climate data, and above and below ground biomass maps of CONUS for the years 2005, 2010, 2015 and 2016 as input data sources to this model-data fusion framework. The input data were integrated into the CARDAMOM model to constrain on the terrestrial carbon and to specifically attribute changes of forest carbon stocks and spatial distributions of carbon emissions and removals across forested lands. United States Forest Service's Forest Inventory and Analysis (FIA) plot data were used to train models for the prediction of forest above-ground biomass (AGB).
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