Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Accurate soil organic carbon (SOC) maps are needed to predict the terrestrial SOC feedback to climate change, one of the largest remaining uncertainties in Earth system modeling. Over the last decade, global scale models have produced varied predictions of the size and distribution of SOC stocks, ra...

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Bibliographic Details
Main Authors: Gavin McNicol, Chuck Bulmer, David D’Amore, Paul Sanborn, Sari Saunders, Ian Giesbrecht, Santiago Gonzalez Arriola, Allison Bidlack, David Butman, Brian Buma
Format: Article
Language:English
Published: IOP Publishing 2019-01-01
Series:Environmental Research Letters
Subjects:
machine learning
digital soil mapping
coastal temperate rainforest
soil organic carbon
biogeochemistry
soil science
Online Access:https://doi.org/10.1088/1748-9326/aaed52

Internet

https://doi.org/10.1088/1748-9326/aaed52

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