The USDA FIA Carbon Pools image service was developed using data from over 213,000 national forest inventory plots measured during the period 2014-2018 from the USFS Forest Inventory and Analysis (FIA) program, in conjunction with other auxiliary information. Roughly 4,900 Landsat 8 OLI scenes, collected during the same time period, were processed to extract information about vegetation phenology. This information, along with climatic and topographic raster data, were used in an ecological ordination model of tree species. The model produced a feature space of ecological gradients that was then used to impute FIA plots to pixels. The plots imputed to each pixel were then used to assign values (tons per pixel) for each of the forest carbon pools that FIA tracks: live tree (aboveground),  live tree (belowground), down dead, litter, soil organic, standing dead, understory (aboveground), and understory (belowground). The sum of these pools is total forest carbon. This information taken from <a href='https://fia-usfs.hub.arcgis.com/'>https://fia-usfs.hub.arcgis.com/</a>
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For more information about the methods used to produce this dataset please see the following references:
<ul>
    <li>Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29-46.</li>
    <li>Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management. 8:1. doi:10.1186/1750-0680-8-1</li>
    <li>Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182-198.</li>
    <li>Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research. 32: 725-741</li>
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