One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models.

中文翻译：

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使用数据融合和基于人工智能的时空模型缩小 XCO2 估算

人类活动产生的众所周知的温室气体 (GHG) 之一是二氧化碳 (CO2)。了解碳循环及其对生态系统的负面影响需要分析二氧化碳浓度的上升。这项工作的目的是绘制整个表面的二氧化碳浓度图，使其有助于区域碳循环分析。这里使用的是柱平均 CO2 干摩尔分数，称为 XCO2，由轨道碳观测站 2 (OCO-2) 卫星测量。由于云和气溶胶的光谱干扰，OCO-2 的 2 级带中有许多缺失的足迹，这使得理解与碳循环相关的任何评估都受到干扰。这项工作的目标是使用数据重采样和机器学习模型来预测 1 km2 XCO2。这项工作使用月度模型实现了 0.3990 和 0.8090 ppm 的最小平均绝对误差 (MAE) 和均方根误差 (RMSE)。