Journal Article

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

Brookshire, E. N. Jack, Cook, David R., Endsley, Arthur, Machmuller, Megan B., Krishnan, Praveena, Scott, Russell L., Khan, Anam M., Kimball, John, Xia, Yushu, Flerchinger, Gerald N., Chen, Jiquan, Brunsell, Nathaniel A., Mullen, Andrew L., Rivard, Charlotte, Stoy, Paul C., Liu, Heping, Billesbach, Dave, Sanderman, Jonathan, Clark, Patrick E., Mohanty, Binayak, Watts, Jennifer D., Hernandez, Haydee, Duman, Tomer, Tweedie, Craig E., Ewing, Stephanie A., Mitra, Bhaskar, Abraha, Michael, Thienelt, Thomas, Mauritz‐Tozer, Marguerite, Litvak, Marcy, Torn, Margaret S., Meyers, Tilden, Gao, Zhongming, Zhang, Quan

2025

Overview

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. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. 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, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Algorithms / Bayesian theory / Biomass / Calibration / Carbon / Carbon dioxide / Carbon dioxide exchange / Climate change / Datasets / Denitrification‐Decomposition / digital soil mapping / DNDC / DSM / ecosystem respiration / Ecosystem services / Ecosystems / ENVIRONMENTAL SCIENCES / Estimates / Fluctuations / flux towers / fPAR / fraction of absorbed photosynthetically active radiation / GEE / Google Earth Engine / GPP / Grasses / gross primary productivity / HOC / humus organic carbon / L4C / leave‐one‐out cross‐validation / Level 4 Carbon / light use efficiency / LOOCV / LUE / MBE / mean bias error / Midwestern United States / model validation / modeling / National Ecological Observatory Network / National Land Cover Database / NDVI / near infrared band / NEE / NEON / net ecosystem exchange / net ecosystem exchange of carbon dioxide / net primary productivity / NIR / NLCD / NLDAS / Normalized Difference Vegetation Index / normalized Root Mean Square Error / North American Land Data Assimilation System / NPP / nRMSE / Organic carbon / particulate organic carbon / POC / Primary production / principal investigator / Probability theory / Productivity / quality control / rangeland / Rangeland Analysis Platform / Rangeland Carbon Tracking and Management / Rangelands / RAP / RCTM / RECO / relative bias / Remote sensing / resistant organic carbon / RMSE / ROC / Root Mean Square Error / Rothamsted Carbon / RothC / SMAP / SMLR / SOC / Soil Moisture Active‐Passive / soil organic carbon / soil organic matter / SOM / Spatial and Temporal Adaptive Reflectance Fusion Model / Spatial data / Spatial variability / Spatial variations / STARFM / stepwise multiple linear regression / Temporal variability / temporal variation / Temporal variations / Towers / vapor pressure deficit / Vegetation / vegetation types / VPD