| 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 | 956 | 2020 |
| 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 | 181 | 2017 |
| 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 | 121 | 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 |
| Investigation of the spatially varying relationships of PM2. 5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression Q Yang, Q Yuan, L Yue, T Li Environmental pollution 262, 114257, 2020 | 37 | 2020 |
| Mapping PM2. 5 concentration at a sub-km level resolution: A dual-scale retrieval approach Q Yang, Q Yuan, L Yue, T Li, H Shen, L Zhang ISPRS Journal of Photogrammetry and Remote Sensing 165, 140-151, 2020 | 33 | 2020 |
| Mapping PM2. 5 concentration at high resolution using a cascade random forest based downscaling model: Evaluation and application Q Yang, Q Yuan, T Li, L Yue Journal of cleaner production 277, 123887, 2020 | 26 | 2020 |
| Ultrahigh-resolution PM2. 5 estimation from top-of-atmosphere reflectance with machine learning: Theories, methods, and applications Q Yang, Q Yuan, T Li Environmental Pollution 306, 119347, 2022 | 19 | 2022 |
| Global air quality change during COVID-19: a synthetic analysis of satellite, reanalysis and ground station data Q Yang, B Wang, Y Wang, Q Yuan, C Jin, J Wang, S Li, M Li, T Li, S Liu, ... Environmental Research Letters 16 (7), 074052, 2021 | 13 | 2021 |
| Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model M Li, Q Yang, Q Yuan, L Zhu Chemosphere 301, 134817, 2022 | 12 | 2022 |
| A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning Q Yang, Q Yuan, M Gao, T Li Science of The Total Environment 857, 159542, 2023 | 11 | 2023 |
| Joint estimation of PM2. 5 and O3 over China using a knowledge-informed neural network T Li, Q Yang, Y Wang, J Wu Geoscience Frontiers 14 (2), 101499, 2023 | 10 | 2023 |
| Research progress and challenges of data-driven quantitative remote sensing Q Yang, C Jin, T Li, Q Yuan, H Shen, L Zhang Natl. Remote Sens. Bull 26 (2), 268-285, 2022 | 9 | 2022 |
| A synchronized estimation of hourly surface concentrations of six criteria air pollutants with GEMS data Q Yang, J Kim, Y Cho, WJ Lee, DW Lee, Q Yuan, F Wang, C Zhou, ... NPJ climate and atmospheric science 6 (1), 94, 2023 | 3 | 2023 |
| 数据驱动的多源遥感信息融合研究进展 张良培， 何江， 杨倩倩， 肖屹， 袁强强 测绘学报, 2022 | 3 | 2022 |
| 数据驱动的定量遥感研究进展与挑战. 杨倩倩， 靳才溢， 李同文， 袁强强， 沈焕锋， 张良培 Journal of Remote Sensing 26 (2), 2022 | 3 | 2022 |
| How well can satellite AOD indicate the ground-level PM2.5 variations in China in recent 5 years? Q Yuan, Q Yang, L Yue, T Li, H Shen, L Zhang, H Zhang AGU Fall Meeting Abstracts 2018, A21G-2728, 2018 | | 2018 |
