晏星 - 北京师范大学 - 全球变化与地球系统科学研究院

个人简介

晏星，副教授，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50余篇，SCI论文30余篇，其中第一作者/通讯作者22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。主持包括国家自然基金青年项目，北京市自然基金面上项目，参与国家重点研发计划等项目。担任Journal of Remote Sensing首届青年编委，同时担任Remote Sensing of Environment, The Lancet Planetary Health，Atmospheric Environment，Journal of Geophysical Research: Atmosphere等期刊审稿人。

研究领域

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

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Xing Yan, Chen Zuo, ZhanqingLi, Hans W. Chen, Yize Jiang, Bin He, Huiming Liu, Jiayi Chen, Wenzhong Shi (2023). Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attentionmechanism. Environmental Pollution, 327, 121509. Chen Zuo, Jiayi Chen, Yue Zhang, Yize Jiang, Mingyuan Liu, Huiming Liu, Wenji Zhao, Xing Yan*. (2023). Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China. Atmospheric Environment,305, 119795. Yan, X., Zang, Z., Li, Z.*, Luo, N., Zuo, C., Jiang, Y., Li, D., Guo, Y., Zhao, W., Shi, W., and Cribb, M.(2022). A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches. Earth System Science Data, 14(3): 1193-1213. Luo, N., Zang, Z., Yin, C., Liu, M., Jiang, Y., Zuo, C., Zhao, W., Shi, W. & Yan, X.* (2022). Explainable and spatial dependence deep learning model for satellite-based O3 monitoring in China. Atmospheric Environment, 119370. 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. 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. Zang, Z., Guo, Y., Jiang, Y., Chen, Z., Li, D., Shi, W., & Yan, X.* (2021). Tree-Based Ensemble Deep Learning Model for Spatiotemporal Surface Ozone (O3) Prediction and Interpretation. International Journal of Applied Earth Observation and Geoinformation, 103, 102516. 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, vol. 18, no. 6, pp. 944-948. 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. 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. 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, vol. 58, no. 12, pp. 8427-8437 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. 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. 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.