摘要
本文首次基于OCO-2卫星观测数据,采用高精度曲面建模(High Accuracy Surface Modeling, HASM)的方法来模拟大范围高精度的二氧化碳柱浓度(XCO_2)的空间分布。首先,探讨分析HASM方法应用于模拟OCO-2卫星观测XCO_2的空间分布的可行性。从2014年9月至2015年8月OCO-2观测的12个月的XCO_2数据中,分别随机选取其各个月90%的XCO_2数据用于空间插值,剩余10%作为精度验证点。自验证结果表明,12个月的平均绝对值误差为0.34 ppm。由此可见,HASM适用于模拟OCO-2卫星观测XCO_2的空间分布。然后,采用HASM对OCO-2在2014年9月至2015年8月的各个月观测数据进行空间插值,获取空间分辨率为0.5°×0.5°的各个月均值XCO_2的空间分布,同时基于地基观测TCCON(Total Carbon Column Observing Network)站的XCO_2数据对HASM模拟结果进行交叉验证。验证结果表明,HASM模拟的XCO_2与TCCON站对应观测数据相比,其平均绝对值误差为0.81 ppm,相关系数为0.88。因此,HASM在模拟OCO-2卫星观测的XCO_2空间分布上具有很大的优势。
In this study, high accuracy surface modeling （HASM） is used to simulate the spatial distribution of XCO2 with high accuracy in a wild range of regions based on OCO-2 satellite＇s observations. Firstly, we discussed and analyzed the feasibility of HASM simulation of the spatial distribution of XCO： observed by OCO-2 satellite. For the OCO-2＇s XCO2 from September 2014 to August 2015, 90% of each month＇s XCO： data were randomly selected as the input of spatial interpolation and the remaining 10% were used to verify the result of spatial interpolation.The results show that the mean absolute error of 12 months is 0.34 ppm. Therefore, HASM is suitable for simulating the spatial distribution of XCO： observed by OCO-2 satellite. Then we use HASM to simulate monthly XCO2 distribution at the spatial resolution of 0.5°×0.5° based on OCO-2＇s observations from September 2014 to August 2015. Simultaneously, the corresponding TCCON （Total Carbon Column Observing Network） observations are used to verify HASM＇s simulations. The result shows that the mean absolute error between HASM simulations and TCCON observations is 0.81 ppm and the correlation coefficient between them is 0.88. Therefore, HASM has great advantages in simulating the spatial distribution of XCO2 observed by OCO-2 satellite.
作者
张丽丽
赵明伟
赵娜
岳天祥
ZHANG Lili;ZHAO Mingwei;ZHAO Na;YUE Tianxiang(Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101;Chuzhou University,Chuzhou 239000;Institute of Geographic Sciences and Natural Resources Research,Beijing 100101;University of Chinese Academy of Sciences,Beijing 100049)
出处
《地球信息科学学报》
CSCD
北大核心
2018年第9期1316-1326,共11页
Journal of Geo-information Science
基金
国家自然科学基金重大项目课题(41590844)
国家自然科学基金创新团体项目(41421001)
