从事基于人工智能的遥感数据智能分析与陆地生态系统监测研究，系列成果提供了有效的长时序陆面动态监测方法与数据集，肯定了我国在应对气候变化与人类活动中相关政策与行动的效益，为制定未来的城市管理政策、实现联合国可持续发展目标提供了重要参考。相关成果被国家自然科学基金委、中国科学技术协会、新华网、央视网、Nature News等上百家媒体报道，荣获“2024年度中国生态环境十大科技进展”、广东省青年拔尖人才等奖项。

一、期刊论文
Ao Zurui#, Hu Xiaomei#, Tao Shengli#*, et al. (2024). A national-scale assessment of land subsidence in China’s major cities. Science, 384, 301-306.
Wang Lijun#, Zurui Ao#, Tao Shengli*, Hu Xie*, et al. (2026). Global view of urban land subsidence and its changing trajectories. Nature Cities. https://doi.org/10.1038/s44284-026-00527-8
Tao Shengli*, Ao Zurui*, Wigneron, J. P.et al. (2023). A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT. Earth System Science Data, 15(4), 1577-1596.
Ao Zurui, Sun Ying, Xiaoyu Pan, et al. (2022). Deep learning-based spatiotemporal data fusion using a patch-to-pixel mapping strategy and model comparisons. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-18.
Ao Zurui, Wu Fangfang, Hu Saihan, et al. (2022). Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks. The Crop Journal, 10(5), 1239-1250.
Ao Zurui, Sun Ying, Xin Qinchuan*. (2020). Constructing 10-m NDVI time series from Landsat 8 and Sentinel 2 images using convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 18(8), 1461-1465.

二、学术著作
敖祖锐, 常占强, 张晶.星载SAR影像模拟与正射校正[M]. 中国环境出版社, 2017

三、国际会议
Jiang kai, Lin yiming, Ao zurui*. Local Spacing-Aware Hungarian Matching for Stable Point-Supervised Crowd Counting. ECCV 2026.
Ao Zurui et al. Spatiotemporal Fusion of Landsat and MODIS Images using Deep Convolutional Neural Networks. IEEE IGARSS 2020 (Oral presentation).
Ao Zurui et al. Quantifying the Effect of Registration Error on Deep Learning-based Spatio-Temporal Fusion Methods. CPGIS 2021 (Oral presentation).