大气与环境光学学报 ›› 2025, Vol. 20 ›› Issue (5): 622-636.doi: 10.3969/j.issn.1673-6141.2025.05.006

• 光学遥感 • 上一篇    下一篇

基于多源卫星的大气CO2浓度不确定性和融合研究

田文杰 1,2, 张丽丽 1,3,4,5*, 余涛 1,3,5, 张文豪 6, 臧文乾 1,5, 王春梅 1   

  1. 1 中国科学院空天信息创新研究院, 遥感卫星应用国家工程实验室, 北京 100094; 2 中国科学院大学, 北京 100049; 3 海南空天信息研究院, 海南省地球观测重点实验室, 海南 三亚 572029; 4 中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京 100101; 5 廊坊空基信息技术研发服务中心, 河北 廊坊 065001; 6 北华航天工业学院, 遥感信息工程学院, 河北 廊坊 065000
  • 收稿日期:2023-03-20 修回日期:2023-05-06 出版日期:2025-09-28 发布日期:2025-09-24
  • 通讯作者: E-mail: zhangll@lreis.ac.cn E-mail:zhangll@lreis.ac.cn
  • 作者简介:田文杰 (2001- ), 江苏连云港人, 硕士研究生, 主要从事多源二氧化碳时空融合方面的研究。E-mail: tianwenjie221@mails.ucas.ac.cn
  • 基金资助:
    海南省自然科学基金 (423MS113), 河北省自然科学基金 (D2022103002)

Uncertainty and fusion of atmospheric CO2 concentration based on multi-source satellites

TIAN Wenjie 1,2, ZHANG Lili 1,3,4,5*, YU Tao 1,3,5, ZHANG Wenhao 6, ZANG Wenqian 1,5, WANG Chunmei 1   

  1. 1 National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 2 University of Chinese Academy of Sciences, Beijing 100094, China; 3 Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China; 4 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; 5 Langfang Air-based Information Technology Research and Development Service Center, Langfang 065001, China; 6 School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
  • Received:2023-03-20 Revised:2023-05-06 Online:2025-09-28 Published:2025-09-24
  • Contact: ZHANG LiLi E-mail:zhangll@lreis.ac.cn

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摘要： CO2作为重要的温室气体， 其浓度的变化对全球气候有着重要的影响。卫星遥感监测因具有连续、稳定、大尺 度等特点， 是大气CO2浓度分布信息的重要来源。但由于卫星载荷设置及大气中云和气溶胶等因素的影响， 目前单一 碳卫星很难获取全球连续的高时空分辨的CO2浓度分布信息， 因此， 为更好地确定多源卫星CO2融合方法， 需要对不 同卫星产品进行不确定性分析。本文基于2019―2021 年地基TCCON (Total Carbon Column Observing Network) 数据， 对GOSAT (Greenhouse Gases Observing Satellite)、OCO-2 (Orbiting Carbon Observatory-2) 和GOSAT2 三颗卫星的CO2 精度进行不确定性分析， 并基于分析结果， 使用结合单位权思想的误差反距离权重法以及克里金插值法建立了全球 多源CO2融合模型， 进一步分析了其时空分布规律。分析结果表明OCO-2 的不确定性最低， 均方根误差ERMS为1.10 × 10-6， GOSAT居其次， ERMS为1.88 × 10-6， GOSAT2 不确定性最高， ERMS为3.02 × 10-6。所建立的融合模型具有良好的精 度， 平均绝对误差均值为0.91 × 10-6， 平均绝对误差百分比为0.22%。在空间分布上， 研究发现北半球CO2浓度高于南 半球， 在部分地区出现高值区； 而在季节变化方面， 春冬季CO2浓度高于夏秋季， 其中春季CO2浓度最高。

关键词: 大气二氧化碳, 多源卫星遥感, 不确定分析, 融合模拟, 时空分布特征

Abstract: As an important greenhouse gas, CO2 has a significant impact on the global climate due to its concentration changes. The continuous, stable, and large-scale characteristics of satellite remote sensing make it an effective tool for monitoring atmospheric CO2. However, due to the influence of satellite payload settings and factors such as clouds and aerosols in the atmosphere, it is currently difficult for a single carbon satellite to obtain continuous high-resolution global CO2 concentration distribution information. Therefore, in order to better determine the multi-source satellite CO2 fusion method, it is necessary to analyze the uncertainty of different satellite products. This paper utilizes ground-based Total Carbon Column Observing Network (TCCON) data from 2019 to 2021 to conduct an uncertainty analysis of CO2 retrieval accuracy for the GOSAT, OCO-2, and GOSAT-2 satellites. Based on the analysis results, a global multi-source CO2 fusion model was established using the error inverse distance weighting method incorporating unit weight principles and the Kriging interpolation method. The spatiotemporal distribution patterns of the fused CO2 were then further analyzed. The analysis results show that the uncertainty of OCO-2 is the lowest, with a root mean square error ERMS of 1.10 × 10-6, followed by GOSAT with an ERMS of 1.88 × 10-6, and GOSAT2 has the highest uncertainty, with an ERMS of 3.02 × 10-6. The fusion model established has good accuracy, with a mean absolute error of 0.91 × 10-6 and a mean absolute error percentage of 0.22%. In terms of CO2 spatial distribution, it is found that the concentration of CO2 in the northern hemisphere is higher than that in the southern hemisphere, with high-value areas appearing in some regions. While in terms of seasonal changes, the CO2 concentration is higher in spring and winter than in summer and autumn, with the highest concentration in spring.

Key words: atmospheric CO2, multi-source satellite remote sensing, uncertainty analysis, fusion simulation, spatial-temporal distribution characteristics

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