A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products
Abstract
:1. Introduction
- To address the non-linearity of inter-sensor LST relationships with incorporation of a neural network,
- To capture temporal change in LST in multiple scales, and
- To generate high-quality fine resolution LST images in urban areas to support studies of intra-city temperature variations.
2. Materials and Methods
2.1. Study Area
2.2. LST Data
2.3. Sensor-to-Sensor Biases
2.4. Framework Description
2.4.1. Workflow
2.4.2. Linear Stretching across Time (LSAT)
2.4.3. Neural Network
2.4.4. Enrichment of Fine-Resolution Variations
2.4.5. Sharpening an Arbitrary MODIS Image
3. Results
3.1. Training and Validation Loss
3.2. Accuracy Assessment
3.3. Evaluation of Sharpened LST
3.4. Comparison with Bilateral Filtering
4. Discussion
4.1. Error Analysis
4.2. Comparison with Air Temperature
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Yang, Y.; Lee, X. A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products. Remote Sens. 2022, 14, 983. https://doi.org/10.3390/rs14040983
Yang Y, Lee X. A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products. Remote Sensing. 2022; 14(4):983. https://doi.org/10.3390/rs14040983
Chicago/Turabian StyleYang, Yichen, and Xuhui Lee. 2022. "A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products" Remote Sensing 14, no. 4: 983. https://doi.org/10.3390/rs14040983