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代表性论著 可解释性深度学习模型研发与应用：
1.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.
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.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.
4.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.
5.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
细模态气溶胶遥感反演研究：
6.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.
7.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.
8.Yan, X., Zang, Z., Li, Z.*, Luo, N., Zuo, C., Jiang, Y., Li, D., Guo, Y., Zhao, W., Shi, W., and Cribb, M.: A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches, Earth System Science Data, 2022, 14(3): 1193-1213.
9.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.
10.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.
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