High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia
Year: 2025
Type: article
Abstract: Digital Soil Modeling steps involved in data processing, algorithm development, and machine learning modelling, including final outputs (maps) • Resulting high-resolution maps reveal strong spatial heterogeneity in Patagonia. • Steppe grasslands hold highest SOC stocks due to large area and high bulk density. • Nothofagus pumilio forests store the highest SOC per unit area. • Better predictions in complex terrain with machine learning and remote sensing data. • Reliable SOC predictions provide baselines for land planning and climate resilience. Soil organic carbon (SOC) is critical for sustaining agricultural productivity, enhancing resilience to climate change, and supporting ecosystem functions, particularly in fragile regions facing increasing aridity like Patagonia. Knowledge of SOC is often represented by decades old, coarse-scale maps or sparse data, limiting its utility for land managers and policymakers. This study leverages a novel SOC database (1,724 samples) integrated with remote sensing and spatial variables in a machine learning model to produce high-resolution (30 m) SOC data that captures decision-relevant scales of variability across diverse land covers and uses. Results revealed that Random Forest modelling performed best in the NW Patagonian mountainous region. Feature selection procedures identified soil depth, spectral indices, and climatic factors such as evapotranspiration and aridity as important co-variates. We found significant heterogeneity in SOC distribution, ranging from the greatest SOC concentration in Nothofagus pumilio forests (132.4 ± 19.2 t ha −1 at 0–30 cm depth), to the lowest in the grasslands of the Monte ecoregion (27.6 ± 8.0 t ha −1 ). Due to landmass size, the grasslands of the Steppe ecoregion have the most carbon (276.5 million tons), followed by Nothofagus pumilio forests (103.7 million tons). These SOC (t ha −1 ) estimates agree with other studies, showing little difference for forests (10 %) and grasslands (14 %). The resulting maps of this study provide a critical baseline for evaluating SOC distribution, informing land management strategies, and guiding future climate resilience efforts in Patagonia and other similarly vulnerable regions across the globe.
Source: CATENA
Authors: Fabio D. Trinco, Mojtaba Zeraatpisheh, Hannah C. Turner, Verónica El Mujtar, Pablo A. Tittonell, Gillian L. Galford
Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, National Agricultural Technology Institute, University of Vermont, Gund Institute for Environment, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, University of Groningen, Université de Montpellier
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: hybrid
Estimated APC paid: $3,680
Award: G3417293300
OpenAlex ID: W4413143184
