Theo dơi
Tongwen Li
Tongwen Li
Associate Professor, Sun Yat-Sen University
Email được xác minh tại - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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