北京师范大学全球变化与地球系统科学研究院
北京师范大学全球变化与地球系统科学研究院
   
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个人简介

晏星,男,1988年7月出生,2017年11月聘为北京师范大学讲师,硕士生导师。香港理工大学摄影测量与遥感专业博士,并在该校开展博士后研究工作1年;2015-2016, 美国马里兰大学大气海洋科学系访问博士。主要研究方向包括:大气环境遥感、人工智能解译与应用等。发表论文40余篇,SCI论文30篇,其中第一作者/通讯作者19篇。近年来研究工作主要集中在细模态气溶胶遥感反演算法,PM2.5实时监测,可解释性深度学习模型的构建与应用等领域,相关研究相继发表在Remote Sensing of Environment、Environment International、Environmental Pollution、Atmospheric Environment、Atmospheric Research、Science of Total Environment等SCI刊物上。主持国家自然基金青年项目1项,参与国家重点研发计划等项目。现担任Remote Sensing of Environment, Atmospheric Environment,Journal of Geophysical Research: Atmosphere等期刊审稿人。2022年拟招硕士生1名。

 

主要研究内容

主要研究内容:
大气环境遥感
GIS在环境研究中的应用
深度学习模型的回归与可解释性问题

 

代表性论著

1.Yan, X., Zang, Z. Liang, C., Luo, N., Ren, R., Cribb, M., Li, Z.* (2021). New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals. Environmental Pollution, 276, 116707.
2.Yan, X., Zang, Z., Zhao, C.*, Husi, L. (2021). Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach. Environment International, 149,106392.
3.Yan, X.*,Zang, Z., Jiang, Y., Shi, W., Guo, Y., Li, D., Zhao, C., Husi, L. (2021). A Spatial-Temporal Interpretable Deep Learning Model for Improving Interpretability and Predictive Accuracy of Satellite-based PM2.5. Environmental Pollution,273, 116459.
4.Liang, C., Zang, Z., Li, Z., & Yan, X.* (2021). An Improved Global Land Anthropogenic Aerosol Product Based on Satellite Retrievals From 2008 to 2016. IEEE Geoscience and Remote Sensing Letters, 18(6), 944-948.
5.Yan, X.,Zang, Z.,Luo, N., Jiang, Y., & Li, Z.*(2020). New Interpretable Deep Learning Model to Monitor Real-Time PM2.5 Concentrations from Satellite Data. Environment International, 144,106060.
6.Yang, X., Zhao, C., Luo, N., Zhao, W., Shi, W., & Yan, X.* (2020). Evaluation and Comparison of Himawari-8 L2 V1. 0, V2. 1 and MODIS C6. 1 aerosol products over Asia and the oceania regions. Atmospheric Environment, 220, 117068.
7.Yan, X., Liang, C., Jiang, Y., Luo, N., Zang, Z., & Li, Z.* (2020). A Deep Learning Approach to Improve the Retrieval of Temperature and Humidity Profiles From a Ground-Based Microwave Radiometer. IEEE Transactions on Geoscience and Remote Sensing, 58, 8427-8437.
8.Yan, X.*, Luo, N., Liang, C., Zang, Z., Zhao, W., & Shi, W. (2020). Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval. Atmospheric Environment, 224, 117362.
9.Yan, X., Li, Z.*, Luo, N., Shi, W., Zhao, W., Yang, X., Liang, C., Zhang, F. & Cribb, M. (2019). An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia. Remote Sensing of Environment, 222, 90-103.
10.Nana Luo, Wenzhong Shi, Chen Liang, Zhengqiang Li, Haofei Wang, Wenji Zhao, Yingjie Zhang, Yuying Wang, Zhanqing Li, Xing Yan* (2019). Characteristics of atmospheric fungi in particle growth events along with new particle formation in the central North China Plain, Science of The Total Environment, 683, 389-398.
11.Yan, X., Li, Z.*, Luo, N., Shi, W., Zhao, W., Yang, X., & Jin, J. (2018). A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8. Atmospheric Research, 207, 14-27.
12.Yan, X., Li, Z.*, Shi, W., Luo, N., Wu, T., & Zhao, W. (2017). An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: algorithm development. Remote Sensing of Environment, 192, 87-97.
13.Yan, X., Shi, W.*, Zhao, W., & Luo, N. (2015). Mapping dustfall distribution in urban areas using remote sensing and ground spectral data. Science of the Total Environment, 506, 604-612.

 

 

 

学生培养情况:

 

  • 李丹,2020级硕士
  • 郭昱杉,2020级硕士
  • 臧洲,2019级硕士
  • 梁晨,2017级硕士(联合培养,已毕业)

 

联系方式:

 

  • Email:yanxing@bnu.edu.cn
  • 北京师范大学京师科技大厦B座302