%0 Generic
%A Sothe, Camile
%A Gonsamo, Alemu
%A Snider, James
%A Arabian, Joyce
%A Kurz, Werner A.
%A Finkelstein, Sarah
%D 2021
%T Soil organic carbon stock and uncertainties, 1m depth, at 250m spatial resolution in Canada
%U https://data.4tu.nl/articles/dataset/Soil_organic_carbon_stock_and_uncertainties_1m_depth_at_250m_spatial_resolution_in_Canada/14573526/1
%R 10.4121/14573526.v1
%K soil carbon stock estimate
%K soil carbon storage
%K terrestrial ecosystem
%X This project aimed to produce the first
wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m
spatial resolution. This dataset contains the map with the soil organic carbon (SOC) in kg/m² for entire Canada in 1m depth, and the uncertainty in SOC predictions. The SOC stock map was produced using 6,533 ground soil samples, long-term climate data, remote
sensing observations and a machine learning model. The soil samples containing the x and y coordinates, depth and SOC (in g/kg) information were
overlaid with the stacked covariates (soil forming factors) to compose the
regression matrix. Random forest models were trained using a recursive feature
elimination scheme and a cross-validation assessment. The best model was used
for spatial prediction of SOC over Canada in intermediate depths between 0 and
1 m. Afterwards, the SOC content maps were corrected with bulk density and
coarse fragment information to compute the total carbon stock for each
horizon. The horizons have been added to compose the 0-1m depth interval multiplied by root depths fraction to discount shallow soils. Water and ice/snow areas were removed using a mask based on the Land Cover of Canada map. The SOC stock uncertainty map was built using the first and third quantiles of RF quantile regression approach.<br>
%I 4TU.ResearchData