利用卫星图像对苏格兰地上生物量和土壤有机碳进行总碳估算的联合研究
Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in Scotland
摘要：
土地碳核查长期以来一直是碳信用市场的一个挑战。目前可用的碳核查方法费用昂贵，而且可能产生低质量的信贷。可扩展和准确的遥感技术使新的方法来监测地上生物量（AGB）和土壤有机碳（SOC）的变化。大多数最新研究分别对AGB和SOC进行遥感，尽管一些研究表明两者之间存在正相关性。我们打算在我们的研究中结合这两个领域，以提高最先进的总碳估计，并提供深入了解自愿碳交易市场。我们首先在苏格兰的研究区域建立基线模型，使用SOC和AGB领域中最先进的方法。特征工程技术，如方差膨胀因子和机器学习模型上的特征选择的影响，然后进行了研究。这是通过组合来自两个域的预测变量来扩展的。最后，我们利用AGB和SOC之间可能的相关性来建立两者之间的关系，并提出新的模型，试图超越最先进的结果。我们比较了三种机器学习技术，增强回归树，随机森林和xgboost。这些技术已被证明是最有效的在这两个领域。
英文摘要：
Land Carbon verification has long been a challenge in the carbon credit market. Carbon verification methods currently available are expensive, and may generate low-quality credit. Scalable and accurate remote sensing techniques enable new approaches to monitor changes in Above Ground Biomass (AGB) and Soil Organic Carbon (SOC). The majority of state-of-the-art research employs remote sensing on AGB and SOC separately, although some studies indicate a positive correlation between the two. We intend to combine the two domains in our research to improve state-of-the-art total carbon estimation and to provide insight into the voluntary carbon trading market. We begin by establishing baseline model in our study area in Scotland, using state-of-the-art methodologies in the SOC and AGB domains. The effects of feature engineering techniques such as variance inflation factor and feature selection on machine learning models are then investigated. This is extended by combining predictor variables from the two domains. Finally, we leverage the possible correlation between AGB and SOC to establish a relationship between the two and propose novel models in an attempt outperform the state-of-the-art results. We compared three machine learning techniques, boosted regression tree, random forest, and xgboost. These techniques have been demonstrated to be the most effective in both domains.
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