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A physics-constrained machine learning method for mapping gapless land surface temperature
Jun Ma, Huanfeng Shen, Menghui Jiang +5
More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-dr…
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…
Spatially Continuous and High-resolution Land Surface Temperature: A Review of Reconstruction and Spatiotemporal Fusion Techniques
Penghai Wu, Zhixiang Yin, Chao Zeng +6
Remotely sensed, spatially continuous and high spatiotemporal resolution (hereafter referred to as high resolution) land surface temperature (LST) is a key parameter for studying t…
Validation approaches for satellite-based PM2.5 estimation: Assessment and a new approach
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +1
Satellite-derived aerosol optical depth (AOD) has been increasingly employed for the estimation of ground-level PM2.5, which is often achieved by modeling the relationship between…
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…
Real-time and Seamless Monitoring of Ground-level PM2.5 Using Satellite Remote Sensing
Tongwen Li, Chengyue Zhang, Huanfeng Shen +2
Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is stil…
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…