12 citations · 17 across the 4 of their papers we have counts for
4 papers
Uncertainty-Aware Hourly Air Temperature Mapping at 2 km Resolution via Physics-Guided Deep Learning
Shengjie Kris Liu, Siqin Wang, Lu Zhang
Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage,…
Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction
Shengjie Liu, Lu Zhang, Siqin Wang
Central to Earth observation is the trade-off between spatial and temporal resolution. For temperature, this is especially critical because real-world applications require high spa…
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification
Shengjie Liu, Siqin Wang, Lu Zhang
Many real-world applications rely on land surface temperature (LST) data at high spatiotemporal resolution. In complex urban areas, LST exhibits significant variations, fluctuating…
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction
Shengjie Liu, Lu Zhang
Remote sensing data provide a low-cost solution for large-scale monitoring of air pollution via the retrieval of aerosol optical depth (AOD), but is often limited by cloud contamin…