{"data":{"id":"10.4121/16619749","type":"dois","attributes":{"doi":"10.4121/16619749","prefix":"10.4121","suffix":"16619749","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Sothe, Camile","nameType":"Personal","givenName":"Camile","familyName":"Sothe","nameIdentifiers":[{"nameIdentifier":"0000-0001-5259-3838","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Gonsamu, Alemu","givenName":"Alemu","familyName":"Gonsamu","affiliation":[],"nameIdentifiers":[]},{"name":"Arabian, Joyce","givenName":"Joyce","familyName":"Arabian","affiliation":[],"nameIdentifiers":[]},{"name":"Snider, James","givenName":"James","familyName":"Snider","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Soil organic carbon concentration of Canada, at six soil depths, 250m spatial resolution"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2021,"subjects":[{"subject":"Soil Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0503"},{"subject":"FOS: Agriculture, forestry and fisheries","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Soil Organic Carbon Contents"},{"subject":"machine Learning Methods Enable Predictive Modeling"},{"subject":"satellite data analysis"},{"subject":"terrestrial ecosystem models"},{"subject":"Collection: Carbon storage and distribution in terrestrial ecosystems of Canada"},{"subject":"Time: 2000-2019"}],"contributors":[{"name":"McMaster University","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"World Wildlife Fund Canada","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2021-09-15","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.geoderma.2021.115402","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://doi.org/10.1016/j.geoderma.2021.115402","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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\u003cbr\u003eThe 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.\u003cbr\u003eTo 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.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[{"geoLocationPlace":"Canada"}],"fundingReferences":[{"awardTitle":"World Wildlife Fund 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