Developing a Rangeland Carbon Tracking and Monitoring System Using Remote Sensing Imagery Coupled With a Modeling Approach

Rangelands play a crucial role in providing various ecosystem services and have significant potential for carbon sequestration. However, monitoring soil organic carbon (SOC) stocks in rangelands is challenging due to the large size of ranches and the high spatial variability influenced by climate and management factors. To address these challenges, we have developed the Rangeland Carbon Tracking and Management (RCTM) system, which integrates remote sensing inputs, survey data sources, and both empirical and process-based SOC models. In this work, we will introduce the structure of RCTM v1.0, its data input requirements, data processing pipelines, and the resulting data outputs. Additionally, we will discuss the high-resolution soil moisture data layers, baseline SOC maps, and the targeted field sampling plan generated through an empirical digital soil mapping approach. The Bayesian calibration and validation scheme for obtaining grassland plant functional type (PFT)-specific parameters using flux tower network data will also be explained. After calibration, the RCTM system generated estimates of rangeland carbon fluxes across PFTs (R2 between 0.6 and 0.7) and surface depth SOC stocks (R2 = 0.6) with moderate accuracy at the regional scale. The visualization of modeling results associated with long-term rangeland C dynamics at different scales will be demonstrated using the Google Earth Engine platform to inform management decisions and policymaking.