413 citations · 970 across the 8 of their papers we have counts for
8 papers
A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval
Tian Xie, Menghui Jiang, Chao Zeng +4
Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate processes. LST derived from medium- to high-r…
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…
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…
An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine
Yudie Wang, Zhiwei Li, Chao Zeng +2
Urban water is important for the urban ecosystem. Accurate and efficient detection of urban water with remote sensing data is of great significance for urban management and plannin…
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…
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Qiang Zhang, Qiangqiang Yuan, Chao Zeng +2
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information…
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…