| Deep learning in environmental remote sensing: Achievements and challenges Q Yuan, H Shen, T Li, Z Li, S Li, Y Jiang, H Xu, W Tan, Q Yang, J Wang, ... Remote sensing of Environment 241, 111716, 2020 | 1016 | 2020 |
| Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach T Li, H Shen, Q Yuan, X Zhang, L Zhang Geophysical Research Letters 44 (23), 11,985-11,993, 2017 | 380 | 2017 |
| Point-surface fusion of station measurements and satellite observations for mapping PM2. 5 distribution in China: Methods and assessment T Li, H Shen, C Zeng, Q Yuan, L Zhang Atmospheric Environment 152, 477-489, 2017 | 206 | 2017 |
| The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations Q Yang, Q Yuan, T Li, H Shen, L Zhang International journal of environmental research and public health 14 (12), 1510, 2017 | 187 | 2017 |
| Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks H Shen, T Li, Q Yuan, L Zhang Journal of Geophysical Research: Atmospheres 123 (24), 13,875-13,886, 2018 | 134 | 2018 |
| Effects of urban form on haze pollution in China: Spatial regression analysis based on PM2. 5 remote sensing data M Yuan, Y Huang, H Shen, T Li Applied geography 98, 215-223, 2018 | 130 | 2018 |
| The relationships between PM2. 5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations Q Yang, Q Yuan, L Yue, T Li, H Shen, L Zhang Environmental Pollution 248, 526-535, 2019 | 129 | 2019 |
| Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data H Shen, Y Jiang, T Li, Q Cheng, C Zeng, L Zhang Remote Sensing of Environment 240, 111692, 2020 | 118 | 2020 |
| Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces Y Wang, Q Yuan, T Li, H Shen, L Zheng, L Zhang Atmospheric Environment 200, 280-301, 2019 | 84 | 2019 |
| Exploring the association between the built environment and remotely sensed PM2. 5 concentrations in urban areas M Yuan, Y Song, Y Huang, H Shen, T Li Journal of Cleaner Production 220, 1014-1023, 2019 | 82 | 2019 |
| The influence of urban planning factors on PM2. 5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data L Guo, J Luo, M Yuan, Y Huang, H Shen, T Li Science of The Total Environment 659, 1585-1596, 2019 | 81 | 2019 |
| Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental US Q Yuan, H Xu, T Li, H Shen, L Zhang Journal of Hydrology 580, 124351, 2020 | 77 | 2020 |
| Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5 T Li, H Shen, Q Yuan, L Zhang arXiv preprint arXiv:1809.09860, 2018 | 75 | 2018 |
| Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP Y Wang, Q Yuan, T Li, L Zhu, L Zhang ISPRS Journal of Photogrammetry and Remote Sensing 175, 311-325, 2021 | 64 | 2021 |
| Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation Y Wang, Q Yuan, T Li, H Shen, L Zheng, L Zhang ISPRS Journal of Photogrammetry and Remote Sensing 157, 1-12, 2019 | 58 | 2019 |
| A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T Li, H Shen, C Zeng, Q Yuan IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2020 | 54 | 2020 |
| Temporal and Spatial Features of the Correlation between PM2.5 and O3 Concentrations in China J Chen, H Shen, T Li, X Peng, H Cheng, C Ma International Journal of Environmental Research and Public Health 16 (23), 4824, 2019 | 49 | 2019 |
| Estimate hourly PM2. 5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network B Wang, Q Yuan, Q Yang, L Zhu, T Li, L Zhang Environmental Pollution 271, 116327, 2021 | 46 | 2021 |
| Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach J Wang, Q Yuan, H Shen, T Liu, T Li, L Yue, X Shi, L Zhang Journal of Hydrology 585, 124828, 2020 | 44 | 2020 |
| Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach T Li, X Cheng International Journal of Applied Earth Observation and Geoinformation 101 …, 2021 | 43 | 2021 |
Huanfeng ShenProfessor, School of Resource and Environmental Science, Wuhan University在 whu.edu.cn 的电子邮件经过验证
