已毕业硕士生5人（读博3人：加拿大多伦多大学，复旦大学，同济大学；2人工作），2025年招收考研硕士1名。

大气环境遥感

GIS分析与应用

深度学习建模

2013-2016 香港理工大学 博士

2011-2013 香港理工大学 硕士

2007-2011 首都师范大学 学士

研究生课程：

气溶胶卫星遥感技术及其应用，2018-2023

基于R语言的地学数据机器学习建模，2022-2023

本科生课程：

全球变化与大学生责任，2018-2021

天气探测与天气预报的科学认知，2022-至今

Yan,X.,Zang, Z., Li, Z., Chen, H.W., Chen, J., Jiang, Y., Chen, Y., He, B., Zuo, C.,Nakajima, T. and Kim, J., (2024). Deep Learning withPretrained Framework Unleashes the Power of Satellite-Based Global Fine-ModeAerosol Retrieval. Environmental Science & Technology, 58 (32), 14260-14270.

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.