Soil organic carbon concentration of Canada, at six soil depths, 250m spatial resolution
doi:10.4121/16619749.v1
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doi: 10.4121/16619749
doi: 10.4121/16619749
Datacite citation style:
Camile Sothe; Gonsamu, Alemu; Joyce Arabian; James Snider (2021): Soil organic carbon concentration of Canada, at six soil depths, 250m spatial resolution. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16619749.v1
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Dataset
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categories
geolocation
Canada
time coverage
2000-2019
licence
CC0
This dataset contains maps with the spatial distribution of soil carbon concentration (g/kg) in Canada at 6 soil depths: 0cm, 5cm, 15cm, 30cm, 60cm and 100cm. It is being made public to act as supplementary data for the publication 'Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations', published in Geoderma: https://doi.org/10.1016/j.geoderma.2021.115402
The study is part of our project that aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. The soil organic carbon concentration maps are intermediate products to generate the soil organic carbon stock map in kg/m2.
To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model.
The study is part of our project that aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. The soil organic carbon concentration maps are intermediate products to generate the soil organic carbon stock map in kg/m2.
To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model.
history
- 2021-09-15 first online, published, posted
publisher
4TU.ResearchData
associated peer-reviewed publication
Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations
funding
- World Wildlife Fund Canada
organizations
McMaster UniversityWorld Wildlife Fund Canada
DATA
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README_McMaster_WWFCanada_soil_carbon_concentration_250m.txt - 6,866,404,820 bytesMD5:
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McMaster_WWFCanada_soil_carbon_concentration_250m.zip -
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