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| 利用卫星遥感数据估算PM2.5浓度的应用研究进展 |
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王子峰,曾巧林, 陈良富, 陶金花1,2,3,4,5
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1.中国科学院遥感与数字地球研究所，遥感科学国家重点实验室，北京 100101;2.中国科学院空天信息研究院，北京 100094;3.重庆邮电大学计算机科学与技术学院，重庆 400065;4.重庆市空间大数据智能技术工程研究中心，重庆 400065;5.中国科学院大学，北京 100049
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| 摘要: |
| 近年来，PM2.5已成为中国大气污染的首要污染物，危害人体健康。为弥补地基监测站点在空间分布上的局限性，借助卫星遥感技术估算PM2.5浓度已成为研究热点。文章总结了利用卫星估算PM2.5浓度的各种研究方法，探讨了不同方法的优势和不足，指出不同方法对不同应用目的的选择性差异较大。提出，应针对不同应用目的选择相应的方法，从而取得满足各方面需求的研究成果，为未来PM2.5浓度估算应用工作提供参考。 |
| 关键词: 细颗粒物 卫星遥感 比例因子 半经验 统计模型 机器学习 |
| DOI： |
| 分类号:X515；X87 |
| 文献标识码:A |
| 基金项目:中国科学院战略性先导科技专项(A类)“地球大数据科学工程”基金资助项目（XDA19040201） |
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| Research Progress of Methodology and Applications of PM2.5Estimation Using Satellite Remote |
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WANG Zi-feng，ZENG Qiao-lin，CHEN Liang-fu，TAO Jin-hua1,2,3,4,5
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1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, China Academy of Sciences, Beijing 100101, China;2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;3. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065,China;4. Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing 400065,China;5.University of China Academy of Sciences, Beijing 100049, China
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| Abstract: |
| PM2.5(Fine Particulate Matter) has become the primary air pollution pollutant in China, endangering human health. To make up for the limitations of spatial distribution of ground based monitoring sites, it has become a research hotspot by using the advantages of remote sensing satellite technology to estimate PM2.5 concentrations. The methods for estimating PM2.5 are summarized, and the advantages and disadvantages of each method are discussed, also there is a great difference in the selectivity of different application goals. It is suggested that appropriate methods should be selected for different application purpose to meet the needs of various aspects. It provides reference of PM2.5 estimation for future. |
| Key words: PM2.5 Remote sensing Scaling factor Semi empirical Statistical model Machine learning |
