Coupling remote sensing with a process model for the simulation of rangeland carbon dynamics

Authors: XIA, Y. - Columbia University; SANDERMAN, J. - Woodwell Climate Research Center; WATT, J.D. - Woodwell Climate Research Center; MACHMULLER M.B., - Colorado State University; MULLEN, A. - Woodwell Climate Research Center; RIVARD, C. - Woodwell Climate Research Center; ENDSLEY, A. - University Of Montana; HERNANDEZ, H. - Woodwell Climate Research Center; KIMBALL, J. - University Of Montana; EWING, S. - Montana State University; Scott, Russell; Flerchinger, Gerald

Submitted to: Journal of Advances in Modeling Earth Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2025
Publication Date: 3/18/2025
Citation: Xia, Y., Sanderman, J., Watt, J., Machmuller M.B.,, Mullen, A., Rivard, C., Endsley, A., Hernandez, H., Kimball, J., Ewing, S., Scott, R.L., Flerchinger, G.N. 2025. Coupling remote sensing with a process model for the simulation of rangeland carbon dynamics. Journal of Advances in Modeling Earth Systems. 17. Article e2024MS004342. https://doi.org/10.1029/2024MS004342.
DOI: https://doi.org/10.1029/2024MS004342

Interpretive Summary: Rangelands play a crucial role in providing various ecosystem services, including the potentially significant but highly uncertain benefits associated with climate mitigation through increased SOC storage. The monitoring of long-term C storage and changes are challenged, however, by the diversity in rangelands and limited field observations currently available. In this work, we leveraged multiple publicly available datasets, including remote sensing observations, tower-based measurements from over 60 rangeland sites in the Western and Midwestern U.S., and other environmental datasets, to build a process-based Rangeland Carbon Tracking and Monitoring (RCTM) modeling system, for the simulation of 20 years of change in rangeland C. The regionally calibrated RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. RCTM simulation results revealed increased SOC storage and rangeland productivity that is well represented by remote sensing signals and driven by annual precipitation patterns. Since the RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, it will be well-suited for informing rangeland C management strategies and improving broad-scale policy making.

Technical Abstract: Rangelands may provide significant environmental benefits through ecosystem services including 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 established a Rangeland Carbon Tracking and Management (RCTM) system to estimate long-term SOC and C flux changes by leveraging remote sensing inputs and environmental variable datasets with algorithms adapted from existing process-based models. Bayesian calibration was conducted against quality-controlled C flux datasets obtained from 61 Ameriflux and NEON sites, to parameterize vegetation type-specific classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture), for the Western and Midwestern U.S. rangelands. The RCTM system obtained better model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE < 390 g C m-2) than net ecosystem exchange (NEE) (R2 > 0.4, RMSE < 180 g C m-2), and captured the spatial variability of surface SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. The RCTM simulated slightly enhanced SOC stocks during the past decade, which is mainly driven by an increase in precipitation. Regression analysis identified slope, soil texture, and climate factors as the main controls on model-predicted C sequestration rate. Future calibration and validation of the RCTM system will benefit from emerging network-based measures of rangeland C dynamics, together with long-term experimental results on SOC changes.