基于OCO-2卫星数据的中国CO2浓度时空变化特征
Spatio-temporal Variation Characteristics of CO2 Concentration in China Based on OCO-2 Satellite Data
杨梅焕 1邓彦昊 1王涛 2姚明昊 1赵滢滢1
作者信息
- 1. 西安科技大学测绘科学与技术学院,西安 710054
- 2. 西安科技大学测绘科学与技术学院,西安 710054;西安科技大学国土空间研究所,西安 710054
- 折叠
摘要
大气CO2浓度增加引起的全球变暖问题是国内外学者关注的热点议题,但对CO2的监测一直存在较多的不确定性.利用2015-2022年OCO-2卫星观测的CO2柱浓度混合比数据(XCO2),基于克里金插值和标准差椭圆等方法,分析了中国CO2浓度时空分布与变化特征,有以下3个结论.1)基于OCO-2卫星数据的XCO2数据集精度较高,与地面监测站(瓦里关站、鹿林站)观测结果的均方根误差仅为1.75 ppm和1.58 ppm,相关系数分别为0.91和0.96.2)年际上,2015-2022年中国年均XCO2由399.52 ppm增至417.64 ppm,年均增速为2.56 ppm/a,高于过去10年全球CO2浓度平均增速(2.06 ppm/a),但在2019年之后XCO2增速呈下降趋势.季节上,XCO2具有明显的季节变化特征,春季XCO2最高,夏季最低.3)空间分布上,XCO2表现出东部高,西部、东北地区低的空间分布特征.XCO2浓度高值区域集中在京津冀和长三角等城市群.中国东北、西南地区XCO2增速较快,高于华东、华南等经济发达地区.
Abstract
The issue of climate warming caused by the increase of atmospheric CO2 is a hot issue for scholars,but there have been more uncertainties in the monitoring of CO2.In this paper,we analyze the spatial and temporal distribution and variation characteristics of CO2 concentration in China based on methods such as kriging interpolation and standard deviation ellipsoid using the CO2 column concentration mixing ratio data(XCO2)observed by OCO-2 satellite from 2015-2022.The results show that:1)the accuracy of the XCO2 dataset based on OCO-2 satellite data is high,and the root-mean-square errors is only 1.75 ppm and 1.58 ppm with correlation coefficients is 0.91 and 0.96 respectively,when compared with the observations from ground monitoring stations(Waliguan and Lulin stations).2)Interannually,the XCO2 in China increases from 399.52 ppm in 2010 to 417.64 ppm in 2022,with an average annual growth rate of 2.56 ppm/a,which is higher than the average growth rate of global CO2 concentration in the past 10 years(2.06 ppm/a),but the growth rate of XCO2 shows a decreasing trend after 2019.Seasonally,XCO2 has obvious seasonal variation characteristics,with the highest XCO2 in spring and the lowest in summer.3)XCO2 shows the spatial distribution characteristics of high in the east,low in the west and northeast.The areas with high values of XCO2 are urban clusters such as Beijing-Tianjin-Hebei and Yangtze River Delta.The growth rate of XCO2 in Northeast China and Southwest China is faster than that in economically developed areas such as East China and South China.
关键词
遥感数据反演/OCO-2/XCO2/时空分析Key words
remote sensing data inversion/OCO-2/XCO2/spatial-temporal analysis引用本文复制引用
基金项目
国家重点研发计划(2022YFE0119200)
国家自然科学基金(42271309)
出版年
2024
NETL
NSTL
万方数据