Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

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 comple...

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Bibliographic Details
Main Authors:	Ruhollah Taghizadeh-Mehrjardi, Karsten Schmidt, Alireza Amirian-Chakan, Tobias Rentschler, Mojtaba Zeraatpisheh, Fereydoon Sarmadian, Roozbeh Valavi, Naser Davatgar, Thorsten Behrens, Thomas Scholten
Format:	Article
Language:	English
Published:	MDPI AG 2020-03-01
Series:	Remote Sensing
Subjects:	digital soil mapping machine learning models stacking of models spatial block cross-validation deep learning
Online Access:	https://www.mdpi.com/2072-4292/12/7/1095

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