基于OCO-2/3卫星的高时空分辨率XCO_(2)数据插值算法研究

High Spatio-temporal Resolution XCO_(2)Data Interpolation Algorithm based on OCO-2/3 Satellite

CO_(2)是大气中重要的温室气体之一,自工业革命以来,大气CO_(2)浓度不断增加,对全球气候变化起着重要影响。鉴于碳中和、全球CO_(2)变化研究对高覆盖率及高分辨率大气CO_(2)数据的迫切需求,对比分析了先后发射的卫星OCO-2、OCO-3之间的差异,并利用两颗卫星形成联合数据集。由于联合数据集仍存在部分区域无观测数据,考虑到不同纬度的CO_(2)浓度的时空变化特点,将全球划分为6个区域,并选择合适的变异函数,利用克里金插值对无数据区域进行填补。结果表明:在3、8、15、30 d时间尺度上,XCO_(2)数据覆盖率分别提高了52.32%、46.77%、44.04%、33.81%。通过将月插值数据集与TCCON地基站点数据对比验证精度,得到其平均绝对误差为1.049 ppm,均方根误差为1.024 ppm,决定系数为0.82。该方法实现了对联合数据集空白区域的精确填补,提高了XCO_(2)数据的精度、覆盖度和时空分辨率,为研究碳源和碳汇的分布提供了新的数据源。

CO_(2)is one of the important greenhouse gases in the atmosphere.Since the Industrial Revolution,the concentration of CO_(2)in the atmosphere has been increasing continuously,which has an important impact on global climate change.High precision,high coverage and high temporal and spatial resolution CO_(2)data tends to be more significant in the study of carbon neutral and global CO_(2)change.Thus,in this study,we compared the XCO_(2)products between the satellites OCO-2 and OCO-3,and formed a joint data set from the two satellites.Because there are still some regions without observation data in the joint dataset,this study uses Kriging interpo⁃lation algorithm to fill the regions without data.Considering the temporal and spatial variation characteristics of CO_(2)concentration in different latitudes,the algorithm divides theworld into six regions and selects the appropri⁃ate variogram.The results show that the XCO_(2)data coverage increases by 52.32%,46.77%,44.04%,and 33.81%on the 3-day,8-day,15-day,and 30-day timescales,respectively.By comparing the monthly interpo⁃lation data set with the TCCON site data to verify the accuracy,the mean absolute error is 1.049 ppm,the root mean square error is 1.024 ppm,and the coefficient of determination is 0.82.It can be seen that this method can accurately fill in the blank area of the j-oint dataset,and improve the accuracy,coverage and spatiotemporal res⁃olution of the data.