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 a 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 datasets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux datasets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern U.S. 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) than net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE < 180 g C m-2), and captured the spatial variability of surface SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Our RCTM simulations indicated slightly enhanced SOC stocks during the past decade, which is mainly driven by an increase in precipitation. Regression analysis identified slope, soil texture, and climate factors as the main controls on model-predicted C sequestration rate. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of rangeland vegetation biomass, C fluxes, and SOC stocks.

Recently intensified forest fires in the Amazon region have led to large-scale forest losses, particularly in Brazil, after more than a decade of effective forest conservation policy. Analysis of the time course of fire impacts on water and carbon cycling is required for accurate measurement of changes in the forest-atmosphere interactions. Moreover, measurements must also account for natural variations associated with vegetation phenology, and generally direct and indirect effects of environmental changes at annual, seasonal and sub-annual time scales. Here, we study the recovery of two contrasting terra firme forests affected by fire in eastern (sub-montane ombrophile forests) and western (bamboo dominated forests) Amazonia in terms of water and carbon fluxes utilizing remote sensing (Moderate Resolution Imaging Spectroradiometer, MODIS) and climate reanalysis data (Global Land Data Assimilation System, GLDAS). Our results showed that fires significantly increased land surface temperature and air temperature in the forests over different time scales. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles based on the comparison of the periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., evapotranspiration) sub-annual fluctuations. Understanding the mechanisms controlling the forest-atmosphere interactions are essential for assessing how forest fires will influence the exchanges of water and carbon in the future. Improving data and theory about the impacts of fire and other disturbances on the energy balance is essential to improve earth systems models for forecasting the role of tropical forest fires in climate change. Within this context, our approach and, consequently, the results obtained here will help improve the understanding of how fires in terra firme Amazonian forests impact land-atmosphere coupling at different spatial and temporal scales.