%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.

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