Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm
Land surface temperature (LST) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather...
Saved in:
| Main Authors: | Qin Su, Yuan Yao, Cheng Chen, Bo Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/23/7424 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A novel ensemble learning algorithm integrating WRF-CMAQ and downscaling models for hourly estimation of regional air pollution along with vegetation exposure risk detection
by: Peng Zhou, et al.
Published: (2025-08-01) -
Applying Energy Dissipation Rate GNSS Accelerometry to a Non‐Circular Orbiting Satellite
by: D. J. Fitzpatrick, et al.
Published: (2025-04-01) -
Retrieval of Total Precipitable Water Under All‐Weather Conditions From Himawari‐8/AHI Observations Using the Generative Diffusion Model
by: Haixia Xiao, et al.
Published: (2025-08-01) -
Land use change impact on urban land surface temperatures: A GIS-supported satellite-based case study
by: Caroline Walder, et al.
Published: (2022-12-01) -
Machine learning-based multimodal data fusion for the Swiss land use statistics
by: Adrian Meyer, et al.
Published: (2025-12-01)