Coupling remote sensing with a process model for the simulation of rangeland carbon dynamics
Quantifying rangeland carbon is challenging due to high spatial and temporal variability. The Rangeland Carbon Tracking and Management system addresses this by integrating remote‑sensing inputs, environmental datasets and terrestrial C‑cycle algorithms. Bayesian calibration using 61 AmeriFlux and NEON sites across Western and Midwestern U.S. rangelands improved estimates of GPP (R² > 0.6) and NEE (R² > 0.4). The model also captured SOC spatial variability (R² = 0.6) and indicated slight SOC increases over the past decade, driven mainly by precipitation trends. These results highlight the value of long‑term carbon monitoring networks and refined modeling approaches for understanding SOC dynamics and supporting adaptive rangeland management.
Subject Tags
- Carbon storage
- Conservation Technology
- Soils
Abstract
Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern U.S. rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes and SOC stocks.
Citation
Xia, Y., Sanderman, J., Watts, J. D., Machmuller, M. B., Mullen, A. L., Rivard, C., ... & Billesbach, D. (2025). Coupling remote sensing with a process model for the simulation of rangeland carbon dynamics. Journal of Advances in Modeling Earth Systems, 17(3), e2024MS004342. https://doi.org/10.1029/2024MS004342
TNC Authors
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Haydee Hernandez-Yanez
Conservation Data Manager, Colorado
The Nature Conservancy
Email: h.hernandez@tnc.org