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 varia...
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American Geophysical Union (AGU)
2025-03-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004342 |
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| author | Yushu Xia Jonathan Sanderman Jennifer D. Watts Megan B. Machmuller Andrew L. Mullen Charlotte Rivard Arthur Endsley Haydee Hernandez John Kimball Stephanie A. Ewing Marcy Litvak Tomer Duman Praveena Krishnan Tilden Meyers Nathaniel A. Brunsell Binayak Mohanty Heping Liu Zhongming Gao Jiquan Chen Michael Abraha Russell L. Scott Gerald N. Flerchinger Patrick E. Clark Paul C. Stoy Anam M. Khan E. N. Jack Brookshire Quan Zhang David R. Cook Thomas Thienelt Bhaskar Mitra Marguerite Mauritz‐Tozer Craig E. Tweedie Margaret S. Torn Dave Billesbach |
| author_facet | Yushu Xia Jonathan Sanderman Jennifer D. Watts Megan B. Machmuller Andrew L. Mullen Charlotte Rivard Arthur Endsley Haydee Hernandez John Kimball Stephanie A. Ewing Marcy Litvak Tomer Duman Praveena Krishnan Tilden Meyers Nathaniel A. Brunsell Binayak Mohanty Heping Liu Zhongming Gao Jiquan Chen Michael Abraha Russell L. Scott Gerald N. Flerchinger Patrick E. Clark Paul C. Stoy Anam M. Khan E. N. Jack Brookshire Quan Zhang David R. Cook Thomas Thienelt Bhaskar Mitra Marguerite Mauritz‐Tozer Craig E. Tweedie Margaret S. Torn Dave Billesbach |
| author_sort | Yushu Xia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c4f7d9deb1b644bf81cc489d41a5808a |
| institution | OA Journals |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-c4f7d9deb1b644bf81cc489d41a5808a2025-08-20T02:10:41ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-03-01173n/an/a10.1029/2024MS004342Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon DynamicsYushu Xia0Jonathan Sanderman1Jennifer D. Watts2Megan B. Machmuller3Andrew L. Mullen4Charlotte Rivard5Arthur Endsley6Haydee Hernandez7John Kimball8Stephanie A. Ewing9Marcy Litvak10Tomer Duman11Praveena Krishnan12Tilden Meyers13Nathaniel A. Brunsell14Binayak Mohanty15Heping Liu16Zhongming Gao17Jiquan Chen18Michael Abraha19Russell L. Scott20Gerald N. Flerchinger21Patrick E. Clark22Paul C. Stoy23Anam M. Khan24E. N. Jack Brookshire25Quan Zhang26David R. Cook27Thomas Thienelt28Bhaskar Mitra29Marguerite Mauritz‐Tozer30Craig E. Tweedie31Margaret S. Torn32Dave Billesbach33Woodwell Climate Research Center Falmouth MA USAWoodwell Climate Research Center Falmouth MA USAWoodwell Climate Research Center Falmouth MA USANatural Resources and Ecology Laboratory Colorado State University Fort Collins CO USAWoodwell Climate Research Center Falmouth MA USAWoodwell Climate Research Center Falmouth MA USAWA Franke College of Forestry and Conservation The University of Montana Missoula MT USAWoodwell Climate Research Center Falmouth MA USAWA Franke College of Forestry and Conservation The University of Montana Missoula MT USADepartment of Land Resources and Environmental Sciences Montana State University Bozeman MT USADepartment of Biology University of New Mexico Albuquerque NM USADepartment of Biology University of New Mexico Albuquerque NM USAAtmospheric Turbulence and Diffusion Division National Oceanic and Atmospheric Administration Oak Ridge TN USAAtmospheric Turbulence and Diffusion Division National Oceanic and Atmospheric Administration Oak Ridge TN USADepartment of Geography and Atmospheric Science University of Kansas Lawrence KS USABiological and Agricultural Engineering Department Texas A&M University College Station TX USADepartment of Civil and Environmental Engineering Washington State University Pullman WA USADepartment of Civil and Environmental Engineering Washington State University Pullman WA USADepartment of Geography, Environment, and Spatial Sciences Michigan State University East Lansing MI USADepartment of Geography, Environment, and Spatial Sciences Michigan State University East Lansing MI USASouthwest Watershed Research Center USDA Agricultural Research Service Tucson AZ USANorthwest Watershed Research Center USDA Agricultural Research Service Boise ID USANorthwest Watershed Research Center USDA Agricultural Research Service Boise ID USADepartment of Biological Systems Engineering University of Wisconsin‐Madison Madison WI USADepartment of Forest and Wildlife Ecology University of Wisconsin‐Madison Madison WI USADepartment of Land Resources and Environmental Sciences Montana State University Bozeman MT USASchool of Public and Environmental Affairs Indiana University Indianapolis IN USADivision of Environmental Science Argonne National Laboratory Lemont IL USADepartment of Geoecology Martin Luther University Halle‐Wittenberg Wittenberg GermanyInformation and Computational Science James Hutton Institute Aberdeen UKDepartment of Biological Sciences The University of Texas at El Paso El Paso TX USADepartment of Biological Sciences The University of Texas at El Paso El Paso TX USAClimate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA USADepartment of Biological Systems Engineering University of Nebraska‐Lincoln Lincoln NE USAAbstract 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.https://doi.org/10.1029/2024MS004342CcarbonDNDCDenitrification‐DecompositionDSMdigital soil mapping |
| spellingShingle | Yushu Xia Jonathan Sanderman Jennifer D. Watts Megan B. Machmuller Andrew L. Mullen Charlotte Rivard Arthur Endsley Haydee Hernandez John Kimball Stephanie A. Ewing Marcy Litvak Tomer Duman Praveena Krishnan Tilden Meyers Nathaniel A. Brunsell Binayak Mohanty Heping Liu Zhongming Gao Jiquan Chen Michael Abraha Russell L. Scott Gerald N. Flerchinger Patrick E. Clark Paul C. Stoy Anam M. Khan E. N. Jack Brookshire Quan Zhang David R. Cook Thomas Thienelt Bhaskar Mitra Marguerite Mauritz‐Tozer Craig E. Tweedie Margaret S. Torn Dave Billesbach Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics Journal of Advances in Modeling Earth Systems C carbon DNDC Denitrification‐Decomposition DSM digital soil mapping |
| title | Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics |
| title_full | Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics |
| title_fullStr | Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics |
| title_full_unstemmed | Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics |
| title_short | Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics |
| title_sort | coupling remote sensing with a process model for the simulation of rangeland carbon dynamics |
| topic | C carbon DNDC Denitrification‐Decomposition DSM digital soil mapping |
| url | https://doi.org/10.1029/2024MS004342 |
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