cff-version: 1.2.0
abstract: "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>"
authors:
  - family-names: Sothe
    given-names: Camile
    orcid: "https://orcid.org/0000-0001-5259-3838"
  - family-names: Gonsamo
    given-names: Alemu
  - family-names: Snider
    given-names: James
  - family-names: Arabian
    given-names: Joyce
  - family-names: Kurz
    given-names: Werner A.
  - family-names: Finkelstein
    given-names: Sarah
    orcid: "https://orcid.org/0000-0002-8239-399X"
title: "Soil organic carbon stock and uncertainties, 1m depth, at 250m spatial resolution in Canada"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/14573526.v1
license: CC0
date-released: 2021-05-18