3.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Liangpei Zhang3 profiles44 · h 130
- Gui-Song Xia2 profiles22 · h 43
- Huanfeng Shen19 · h 76
- Qiangqiang Yuan15 · h 59
- Bo Du2 profiles11 · h 82
- Chen Wu3 profiles11 · h 29
- Guisong Xia2 profiles11 · h 39
- Chenxi Duan2 profiles9 · h 19
- L. Zhang9 · h 59
- Rui Li2 profiles8 · h 15
- Ce Zhang2 profiles7 · h 28
- Jie Li7 · h 26
- Wuhan UniversityCN150 papers
- Chinese Academy of SciencesCN13 papers
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE9 papers
- Huazhong University of Science and TechnologyCN8 papers
- Technical University of MunichDE8 papers
- China University of GeosciencesCN7 papers
- Lancaster UniversityGB7 papers
- UK Centre for Ecology & HydrologyGB7 papers
- The University of TokyoJP6 papers
- Sun Yat-sen UniversityCN5 papers
- University of SouthamptonGB5 papers
- Institute of Geographic Sciences and Natural Resources ResearchCN4 papers
8 papers · 1 filter
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
Tian Xie, Menghui Jiang, Huanfeng Shen +5
Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inve…
Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data
Huanfeng Shen, Yun Jiang, Tongwen Li +3
Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep…
Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces
Yuan Wang, Qiangqiang Yuan, Tongwen Li +3
In this study, we evaluated the performance of the MODIS C6.1 AOD products and compared them with the C6 products over regions in China with multifarious underlying surfaces during…
Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +1
The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 provides a promising approach for obtaining spatial PM2.5 data. Several spatiotemporal m…
Estimating regional ground-level PM2.5 directly from satellite top-of-atmosphere reflectance using deep learning
Huanfeng Shen, Tongwen Li, Qiangqiang Yuan +1
Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectan…
The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations
Qianqian Yang, Qiangqiang Yuan, Tongwen Li +2
The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM…
Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +2
Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates ge…
Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: methods and assessment
Tongwen Li, Huanfeng Shen, Chao Zeng +2
Fine particulate matter (PM2.5) is associated with adverse human health effects, and China is currently suffering from serious PM2.5 pollution. To obtain spatially continuous groun…