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Land Carbon verification has long been a challenge in the carbon creditmarket. Carbon verification methods currently available are expensive, and maygenerate low-quality credit. Scalable and accurate remote sensing techniquesenable new approaches to monitor changes in Above Ground Biomass (AGB) and SoilOrganic Carbon (SOC). The majority of state-of-the-art research employs remotesensing on AGB and SOC separately, although some studies indicate a positivecorrelation between the two. We intend to combine the two domains in ourresearch to improve state-of-the-art total carbon estimation and to provideinsight into the voluntary carbon trading market. We begin by establishingbaseline model in our study area in Scotland, using state-of-the-artmethodologies in the SOC and AGB domains. The effects of feature engineeringtechniques such as variance inflation factor and feature selection on machinelearning models are then investigated. This is extended by combining predictorvariables from the two domains. Finally, we leverage the possible correlationbetween AGB and SOC to establish a relationship between the two and proposenovel models in an attempt outperform the state-of-the-art results. We comparedthree machine learning techniques, boosted regression tree, random forest, andxgboost. These techniques have been demonstrated to be the most effective inboth domains.

Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in Scotland