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

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Titel: Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning.
Autoren: Tian, Xuemeng, de Bruin, Sytze, Simoes, Rolf, Isik, Mustafa Serkan, Minarik, Robert, Ho, Yu-Feng, Şahin, Murat, Herold, Martin, Consoli, Davide, Hengl, Tomislav
Quelle: PeerJ; Jul2025, p1-56, 56p
Schlagwörter: MACHINE learning, RANDOM forest algorithms, REMOTE sensing, SPATIOTEMPORAL processes, SOIL mapping, HISTOSOLS, CONFIDENCE intervals
Geografische Kategorien: EUROPE
Abstract: 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 produce 30 m SOCD maps for the EU at four-year intervals (2000–2022) and four soil depth intervals (0–20 cm, 20–50 cm, 50–100 cm, and 100–200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided. Model evaluation indicates good overall accuracy (R2 = 0.63 and CCC = 0.76 for hold-out independent tests). Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. The PI validation confirmed effective uncertainty estimation, though with reduced accuracy for higher SOCD values. Shapley analysis identified soil depth as the most influential feature, followed by vegetation, long-term bioclimate, and topographic features. While pixel-level uncertainty is substantial, spatial aggregation reduces uncertainty by approximately 66%. Detecting SOCD changes remains challenging but offers a baseline for future improvements. Maps, based primarily on topsoil data from cropland, grassland, and woodland, are best suited for applications related to these land covers and depths. We recommend that users interpret the maps in conjunction with local knowledge and consider the accompanying uncertainty and extrapolation risk layers. All data and code are available under an open license at and . [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m<sup>3</sup>) 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 produce 30 m SOCD maps for the EU at four-year intervals (2000–2022) and four soil depth intervals (0–20 cm, 20–50 cm, 50–100 cm, and 100–200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided. Model evaluation indicates good overall accuracy (R<sup>2</sup> = 0.63 and CCC = 0.76 for hold-out independent tests). Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. The PI validation confirmed effective uncertainty estimation, though with reduced accuracy for higher SOCD values. Shapley analysis identified soil depth as the most influential feature, followed by vegetation, long-term bioclimate, and topographic features. While pixel-level uncertainty is substantial, spatial aggregation reduces uncertainty by approximately 66%. Detecting SOCD changes remains challenging but offers a baseline for future improvements. Maps, based primarily on topsoil data from cropland, grassland, and woodland, are best suited for applications related to these land covers and depths. We recommend that users interpret the maps in conjunction with local knowledge and consider the accompanying uncertainty and extrapolation risk layers. All data and code are available under an open license at and . [ABSTRACT FROM AUTHOR]
ISSN:21678359
DOI:10.7717/peerj.19605