Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

Wang, Zhuonan; Kumar, Jitendra; Weintraub‐Leff, Samantha R.; Todd‐Brown, Katherine; Mishra, Umakant; Sihi, Debjani

doi:10.1029/2023JG007702

Title: Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

Journal Article · Sun Feb 04 00:00:00 EST 2024 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1029/2023JG007702· OSTI ID:2320276

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Department of Environmental Sciences Emory University Atlanta GA USA
Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge TN USA
National Ecological Observatory Network Battelle Boulder CO USA
Department of Environmental Engineering Science University of Florida Gainesville FL USA
Computational Biology &, Biophysics Sandia National Laboratories Livermore CA USA

Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R ² = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R ² = 0.23) and SoilGrids 2.0 ( R ² = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS); National Science Foundation (NSF)

Grant/Contract Number:: AC05-00OR22725; DEB-2106137; DEB-2106138

OSTI ID:: 2320276

Alternate ID(s):: OSTI ID: 2301629; OSTI ID: 2317677

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Vol. 129 Journal Issue: 2; ISSN 2169-8953

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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