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

Authors: XIA, YUSHU - Woodwell Climate Research Center; SANDERMAN, JONATHAN - Woodwell Climate Research Center; WATTS, JENNIFER - Woodwell Climate Research Center; MACHMULLER, MEGAN - Colorado State University; MULLEN, ANDREW - Woodwell Climate Research Center; RIVARD, CHARLOTTE - Woodwell Climate Research Center; ENDSLEY, ARTHUR - University Of Montana; HERNANDEZ, HAYDEE - Woodwell Climate Research Center; KIMBALL, JOHN - University Of Montana; EWING, STEPHANIE - Montana State University; LITVAK, MARCY - University Of New Mexico; DUMAN, TOMER - University Of New Mexico; KRISHNAN, PRAVEENA - National Oceanic & Atmospheric Administration (NOAA); MEYERS, TILDEN - National Oceanic & Atmospheric Administration (NOAA); BRUNSELL, NATHANIEL - University Of Kansas; MOHANTY, BINAYAK - Texas A&M University; LIU, HEPING - Washington State University; GAO, ZHONGMING - Washington State University; CHEN, JIQUAN - Michigan State University; ABRAHA, MICHAEL - Michigan State University; Scott, Russell; Flerchinger, Gerald; Clark, Patrick; STOY, PAUL - University Of Wisconsin; KHAN, ANAM - University Of Wisconsin; BROOKSHIRE, E.N. JACK - Montana State University; ZHANG, QUAN - Indiana University; COOK, DAVID - Argonne National Laboratory; THIENELT, THOMAS - Martin Luther University; MITRA, BHASKAR - The James Hutton Institute; MAURITZ-TOZER, MARGUERITE - University Of Texas - El Paso; TWEEDIE, CRAIG - University Of Texas - El Paso; TORN, MARGARET - Lawrence Berkeley National Laboratory; BILLESBACH, DAVE - University Of Nebraska

Submitted to: Journal of Advances in Modeling Earth Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2025
Publication Date: 3/15/2025
Citation: Xia, Y., Sanderman, J., Watts, J.D., Machmuller, M.B., Mullen, A.L., Rivard, C., Endsley, A., Hernandez, H., Kimball, J., Ewing, S.A., Litvak, M., Duman, T., Krishnan, P., Meyers, T., Brunsell, N.A., Mohanty, B., Liu, H., Gao, Z., Chen, J., Abraha, M., Scott, R.L., Flerchinger, G.N., Clark, P., Stoy, P.C., Khan, A.M., Brookshire, E., Zhang, Q., Cook, D.R., Thienelt, T., Mitra, B., Mauritz-Tozer, M., Tweedie, C.E., Torn, M.S., 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). 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 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 US 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.