The NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original 5-year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA-20 and NOAA-21 satellites enable potential continuity of long-term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model-data fusion, we combined an 18-year record of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from MOD17 and the new VNP17 for 2012-2018 is 127 ±2.8 Pg C year-1 (58 ±1.1 Pg C year-1), which compares well with independent top-down and bottom-up estimates. Both MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000-2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year-2 but slowing to 0.35-0.44 (0.11-0.13) Pg C year-2 over 2012-2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.

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.

In the northern hemisphere, terrestrial ecosystems transition from net sources of CO2 to the atmosphere in winter to net ecosystem carbon sinks during spring. The timing (or phase) of this transition, determined by the balance between ecosystem respiration (RECO) and primary production, is key to estimating the amplitude of the terrestrial carbon sink. We diagnose an apparent phase bias in the RECO and net ecosystem exchange (NEE) seasonal cycles estimated by the Terrestrial Carbon Flux (TCF) model framework and investigate its link to soil respiration mechanisms. Satellite observations of vegetation canopy conditions, surface meteorology, and soil moisture from the NASA SMAP Level 4 Soil Moisture product are used to model a daily carbon budget for a global network of eddy covariance flux towers. Proposed modifications to TCF include: the inhibition of foliar respiration in the light (the Kok effect); a seasonally varying litterfall phenology; an O2 diffusion limitation on heterotrophic respiration (RH); and a vertically resolved soil decomposition model. We find that RECO phase bias can result from bias in RECO magnitude and that mechanisms which reduce northern spring RECO, like substrate and O2 diffusion limitations, can mitigate the phase bias. A vertically resolved soil decomposition model mitigates this bias by temporally segmenting and lagging RH throughout the growing season. Applying these model enhancements at Continuous Soil Respiration (COSORE) sites verifies their improvement of RECO and NEE skill compared to in situ observations (up to \(\Delta\)RMSE \(=-0.76\,g\,C\,m^{-2}\,d^{-1}\)). Ultimately, these mechanisms can improve prior estimates of NEE for atmospheric inversion studies.