标题： 苏格兰使用卫星影像估算总碳时的地上生物量和土壤有机碳联合研究 显示英文标题

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

摘要： 土地碳核算长期以来一直是碳信用市场的一个挑战。目前可用的碳核证方法昂贵，并且可能产生低质量的碳信用额。可扩展且准确的遥感技术使监测地面上生物量（AGB）和土壤有机碳（SOC）的变化成为可能。 大多数最先进的研究分别使用遥感技术对AGB和SOC进行研究，尽管一些研究表明两者之间存在正相关关系。 我们打算在我们的研究中结合这两个领域，以改进最先进的总碳估算，并为自愿碳交易市场提供见解。 我们首先在苏格兰的研究区域内，使用最先进的SOC和AGB领域的技术建立基线模型。 然后研究特征工程技巧，例如方差膨胀因子和特征选择对机器学习模型的影响。 通过结合两个领域的预测变量进一步扩展。 最后，我们利用AGB和SOC之间的潜在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：提升回归树、随机森林和XGBoost。 这些技术在两个领域都被证明是最有效的。 显示英文摘要

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.