基于主成分分析的CO<sub>2</sub>统计反演方法

桑浩; 王先华; 叶函函; 蒋芸

doi:doi:10.3969/j.issn.1673-6141.2017.03.006

大气与环境光学学报, 2017, 12 (3): 202, 网络出版: 2017-06-09

基于主成分分析的CO₂统计反演方法

Statistic Retrieval Method of Carbon Dioxide Based on Principal Component Analysis

论文大纲

桑浩王先华 ^*叶函函蒋芸

作者单位

中国科学院安徽光学精密机械研究所中国科学院通用光学定标与表征技术重点实验室, 安徽合肥 230031

光学遥感 CO2反演主成分分析统计反演温室气体 optical remote sensing CO2 retrieval principal component analysis statistical retrieval greenhouse gas

摘要

二氧化碳(CO2)卫星遥感中,大气环境因素是影响反演精度的重要原因,目前反演条件通常限制在气溶胶光学厚度小于0.3的情况。我国大气气溶胶高值情况较为普遍,对大气条件的较高要求将严重影响我国CO2卫星遥感数据的应用能力。针对这种情况,利用主成分分析法对中国京津地区高气溶胶光学厚度的大气CO2进行反演,得到的CO2柱浓度与2013年、 2014年GOSAT-Level2产品进行对比分析,均方根误差分别为0.65%和0.46%,相关性分别为0.77和0.93。

Abstract

In the satellite remote sensing of carbon dioxide (CO2), atmospheric environmental factor is the important factor affecting the inversion accuracy. The inversion condition is usually limited to the situation of the aerosol optical thickness less than 0.3. The higher requirement of the atmospheric conditions will seriously affect the application ability of our country’s CO2 satellite remote sensing data. For this kind of situation, based on principal component analysis (PCA), atmospheric CO2 of Beijing and Tianjin areas of China is inversed of high aerosol optical thickness, the CO2 column concentration obtained is compared with the product of GOSAT-Level2 in 2013, 2014. The root mean square error is 0.65% and 0.46%, respectively, and the correlation is 0.77 and 0.93, respectively.

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桑浩, 王先华, 叶函函, 蒋芸. 基于主成分分析的CO₂统计反演方法[J]. 大气与环境光学学报, 2017, 12(3): 202. SANG Hao, WANG Xianhua, YE Hanhan, JIANG Yun. Statistic Retrieval Method of Carbon Dioxide Based on Principal Component Analysis[J]. Journal of Atmospheric and Environmental Optics, 2017, 12(3): 202.

基于主成分分析的CO₂统计反演方法

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