CGMFN: Conditional Generative Model Fusion Network for Land Surface Temperature Generation

Land surface temperature (LST) is an important parameter representing surface energy, which is of great significance for monitoring urban heat islands, agricultural drought, and global climate. The high-resolution LST observations will address new applications in hydrology. The spatiotemporal fusion...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 13
Hlavní autoři:	Chen, Yuncheng, Yang, Yingbao, Pan, Xin, Hu, Penghua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Agricultural drought Autoencoders BatchFormer Cloud cover conditional generative adversative networks (CGAN) conditional variational autoencoder (CVAE) Data models Deep learning Drought Global climate Hydrology Land surface Land surface temperature land surface temperature (LST) Missing data River basins Spatial discrimination Spatial resolution Spatiotemporal data spatiotemporal fusion Spatiotemporal phenomena Spectroradiometers Surface energy Surface properties Surface temperature Temperature distribution Temperature sensors Training Urban agriculture Urban heat islands
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
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