Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics DOI Creative Commons
Yushu Xia, Jonathan Sanderman, Jennifer D. Watts

и другие.

Journal of Advances in Modeling Earth Systems, Год журнала: 2025, Номер 17(3)

Опубликована: Март 1, 2025

Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring (C) fluxes in rangelands are challenging due to the considerable spatial temporal variability tied rangeland C dynamics as well limited data availability. We developed Rangeland Carbon Tracking Management (RCTM) system track long‐term changes by leveraging remote sensing inputs variable sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled flux obtained from 61 Ameriflux NEON tower sites Western Midwestern US parameterize model according dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, grass‐tree mixture). The resulting RCTM produced higher accuracy for estimating cumulative gross primary productivity (GPP) ( R 2 > 0.6, RMSE <390 g m −2 ) relative net exchange of CO (NEE) 0.4, <180 ). Model performance varied season type. captured = 0.6 when validated against measurements across 13 sites. simulations indicated slightly enhanced during past decade, is mainly driven an increase precipitation. Future efforts refine will benefit network‐based biomass, fluxes, stocks.

Язык: Английский

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics DOI Creative Commons
Yushu Xia, Jonathan Sanderman, Jennifer D. Watts

и другие.

Journal of Advances in Modeling Earth Systems, Год журнала: 2025, Номер 17(3)

Опубликована: Март 1, 2025

Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring (C) fluxes in rangelands are challenging due to the considerable spatial temporal variability tied rangeland C dynamics as well limited data availability. We developed Rangeland Carbon Tracking Management (RCTM) system track long‐term changes by leveraging remote sensing inputs variable sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled flux obtained from 61 Ameriflux NEON tower sites Western Midwestern US parameterize model according dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, grass‐tree mixture). The resulting RCTM produced higher accuracy for estimating cumulative gross primary productivity (GPP) ( R 2 > 0.6, RMSE <390 g m −2 ) relative net exchange of CO (NEE) 0.4, <180 ). Model performance varied season type. captured = 0.6 when validated against measurements across 13 sites. simulations indicated slightly enhanced during past decade, is mainly driven an increase precipitation. Future efforts refine will benefit network‐based biomass, fluxes, stocks.

Язык: Английский

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