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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Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 13
Main Authors: Chen, Yuncheng, Yang, Yingbao, Pan, Xin, Hu, Penghua
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
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Summary: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 method can generate LST with high temporal and spatial resolution. However, the missing data due to cloud cover becomes a main limitation to improve the accuracy of spatiotemporal fusion models. The purpose of the fusion of LST is image prediction and generation, and deep learning generative models provide an effective idea to solve this problem. Therefore, we proposed a conditional generative model fusion network (CGMFN) for LST generation in this article. First, based on the generated model, we construct an unsupervised generation network (GAN) that simultaneously learns and iterates, which can generate fine-spatiotemporal-resolution LST data from reference images with missing values. Then, the spectral normalization (SN) was applied to generators and discriminators to stabilize the training process. The pretraining mechanism was adopted to improve the iteration efficiency of the model. We tested and evaluated the model in the Heihe River Basin (HRB) using FY-4A LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets. Compared with different methods, CGMFN produces a lower RMSE (average < 0.4 K), LPIPS (average < 0.11), and higher SSIM (average > 0.97). In practical applications, CGMFN can reduce the influence of reference image missing values on fusion results and generate LST products with reliable accuracy.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3574080