Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

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) ( R 2 > 0.6, RMSE <390 g C m −2 ) relative to net ecosystem exchange of CO 2 (NEE) ( R 2 > 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 R 2 = 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.

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Publication Details

Journal
Journal of Advances in Modeling Earth Systems
Published
2025-03-01
DOI
https://doi.org/10.1029/2024ms004342
Citations
3
Primary Topic
Rangeland and Wildlife Management
Type
article
Field-Weighted Citation Impact
5.15

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article

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Russell L. Scott, Zhongming Gao, Jennifer D. Watts, Bhaskar Mitra et al.
3 citations
Journal of Advances in Modeling Earth Systems
Rangeland and Wildlife Management
5.15
article

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Russell L. Scott, Zhongming Gao, Jennifer D. Watts, Bhaskar Mitra, E. N. Jack Brookshire, David Cook, M. E. Litvak, John S. Kimball, Paul C. Stoy, Patrick E. Clark, N. A. Brunsell, C. E. Tweedie, Jonathan Sanderman, Margaret S. Torn, Tomer Duman, Charlotte Rivard, G. N. Flerchinger, T. Thienelt, Marguerite Mauritz, K. Arthur Endsley, Jiquan Chen, Yushu Xia, S. A. Ewing, P. Krishnan, Quan Zhang, Binayak P. Mohanty, Megan B. Machmuller, D. P. Billesbach, Haydee Hernandez, Andrew Mullen, Anam M. Khan, Michael Abraha, Heping Liu, Tilden P. Meyers
article en
3 citations

Abstract

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) ( R 2 > 0.6, RMSE <390 g C m −2 ) relative to net ecosystem exchange of CO 2 (NEE) ( R 2 > 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 R 2 = 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.

Journal of Advances in Modeling Earth SystemsVol. 17(3)
University of Nebraska–Lincoln (US), Brookings Institution (US), The Nature Conservancy (US), Argonne National Laboratory (US), National Oceanic and Atmospheric Administration (US), Agricultural Research Service (US), Woodwell Climate Research Center (US), University of Wisconsin–Madison (US), University of Kansas (US), Lawrence Berkeley National Laboratory (US), Lamont-Doherty Earth Observatory (US), James Hutton Institute (GB), Sun Yat-sen University (CN), The University of Texas at El Paso (US), University of New Mexico (US), Montana State University (US), Wuhan University (CN), Northwest Watershed Research Center (US), Southwest Watershed Research Center (US), Indiana University Indianapolis (US), Indiana University – Purdue University Indianapolis (US), University of Montana (US), Martin Luther University Halle-Wittenberg (DE), Washington State University (US), Columbia University (US), Michigan State University (US), Texas A&M University (US), Colorado State University (US)
J.M. Kaplan Fund
Openalex Percentile: Top 6%
Rangeland and Wildlife Management
5.15
DOIOpen Access PDFOpenAlex
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