22 Jan 2024
 | 22 Jan 2024

An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling

Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael Viscarra Rossel

Abstract. Spatially explicit prediction of soil organic carbon (SOC) serves as a crucial foundation for effective land management strategies aimed at mitigating soil degradation and assessing carbon sequestration potential. Here, using more than 1000 in-situ observations, we trained two machine learning models (random forest, and K-means coupled with multiple linear regression), and one process-based model (the vertically resolved MIcrobial-MIneral Carbon Stabilization (MIMICS)) to predict SOC content of the top 30 cm of soil in Australia. Parameters of MIMICS were optimized for different site groupings, using two distinct approaches, plant functional types (MIMICS-PFT), and the most influential environmental factors (MIMICS-ENV). We found that at the continental scale, soil bulk density and mean annual temperature are the dominant controls of SOC variation, and that dominant controls vary for different vegetation types. All models showed good performance in SOC predictions with R2 greater than 0.8 during out-of-sample validation with random forest being the most accurate, and SOC in forests is more predictable than that in non-forest soils. Parameter optimization approaches made a notable difference in the performance of MIMICS SOC prediction with MIMICS-ENV performing better than MIMICS-PFT especially in non-forest soils. Digital maps of terrestrial SOC stocks generated using all the models showed similar spatial distribution with higher values in southeast and southwest Australia, but the magnitude of estimated SOC stocks varied. The mean ensemble estimate of SOC stocks was 30.08 t/ha with K-means coupled with multiple linear regression generating the highest estimate (mean SOC stocks at 38.15 t/ha) and MIMICS-PFT generating the lowest estimate (mean SOC stocks at 24.29 t/ha). We suggest that enhancing process-based models to incorporate newly identified drivers that significantly influence SOC variations in different environments could be key to reducing the discrepancies in these estimates. Our findings underscore the considerable uncertainty in SOC estimates derived from different modelling approaches and emphasize the importance of rigorous out-of-sample validation before applying any one approach in Australia.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael Viscarra Rossel

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-3016', Anonymous Referee #1, 14 Mar 2024
    • AC1: 'Reply on RC1', Lingfei Wang, 10 Apr 2024
    • AC3: 'Reply on RC1', Lingfei Wang, 11 Apr 2024
  • RC2: 'Comment on egusphere-2023-3016', Anonymous Referee #2, 19 Mar 2024
    • AC2: 'Reply on RC2', Lingfei Wang, 10 Apr 2024
    • AC4: 'Reply on RC2', Lingfei Wang, 11 Apr 2024
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael Viscarra Rossel
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael Viscarra Rossel


Total article views: 501 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
351 126 24 501 11 12
  • HTML: 351
  • PDF: 126
  • XML: 24
  • Total: 501
  • BibTeX: 11
  • EndNote: 12
Views and downloads (calculated since 22 Jan 2024)
Cumulative views and downloads (calculated since 22 Jan 2024)

Viewed (geographical distribution)

Total article views: 515 (including HTML, PDF, and XML) Thereof 515 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 16 Jun 2024
Short summary
Effective managements of soil organic carbon require accurate knowledge of its existing distribution and influential factors of carbon dynamics. We identify the importance of variables on carbon variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on SOC distribution derived from a single approach.