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.

Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.

Gross Primary Productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and inter-annual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites - R series (GOES-R) to test the ability of sub-daily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We optimized parameters for three models to estimate GPP. A light response curve (LRC) achieved the lowest test mean absolute error for winter (1.82 µmol CO2 m-2 s-1), spring (2.51 µmol CO2 m-2 s-1), summer (1.45 µmol CO2 m-2 s-1), and fall (1.25 µmol CO2 m-2 s-1). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation towards the morning hours during the dry summers. The LRC and the light-use efficiency model were in agreement with these patterns of increasing shift of GPP towards the morning hours during the summer months. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.