Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions...
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to...
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Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
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TN_cdi_doaj_primary_oai_doaj_org_article_85115f9d0d1e46b2afcc312307bd6cec
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_85115f9d0d1e46b2afcc312307bd6cec
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ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs12071095
