Spatiotemporal Fusion Network for Land Surface Temperature Based on a Conditional Variational Autoencoder

High spatiotemporal resolution land surface temperature (LST) data are essential for dynamic monitoring and prediction in climate change research. Due to the limitations of remote sensing instruments, the current platforms have difficulty in achieving a compromise between high spatial and temporal r...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 13
Hlavní autori:	Chen, Yuncheng, Yang, Yingbao, Pan, Xin, Meng, Xiangjin, Hu, Jia
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Biological system modeling Climate change Climate change research Climate prediction Coders Conditional variational autoencoder (CVAE) convolutional neural network (CNN) Data models Deep learning Heterogeneity Image reconstruction Instruments Land surface temperature land surface temperature (LST) Noise reduction Outliers (statistics) pretraining Process parameters Remote sensing River basins Root-mean-square errors Spatial resolution spatiotemporal fusion Spatiotemporal phenomena Surface temperature Temperature sensors Training Weighting Work platforms
ISSN:	0196-2892, 1558-0644
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