Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning

Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning

This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m3) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). A total of 45,616 SOCD observations and various Earth observation (EO) feature layers were used to pr...

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Bibliographic Details
Main Authors: Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl
Format: Article
Language:English
Published: PeerJ Inc. 2025-07-01
Series:PeerJ
Subjects:
Soil organic carbon density
Machine learning
Earth observation
Uncertainty
Spatial aggregation
Time series
Online Access:https://peerj.com/articles/19605.pdf
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