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Using Generative Art to Convey Past and Future Climate Transitions
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Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network.

Jiaqi Zheng1, Xi Wu1, Xiaojie Li1

  • 1Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved conditional generative adversarial network (CGAN) to estimate near-surface air temperature using Fengyun-4A satellite data, effectively filling gaps from sparse ground stations.

Keywords:
conditional generative adversarial networkdeep learningmulti-scalenear-surface air temperatureremote sensingself-attention mechanism

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Area of Science:

  • Meteorology
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Ground-based meteorological stations have uneven spatial distribution, leading to missing near-surface air temperature data.
  • Satellite remote sensing offers comprehensive spatial coverage, making it valuable for environmental monitoring.

Purpose of the Study:

  • To develop an advanced method for estimating near-surface air temperature by integrating satellite data with deep learning.
  • To overcome limitations of sparse ground-based observations for accurate air temperature mapping.

Main Methods:

  • An improved conditional generative adversarial network (CGAN) framework was developed for air temperature estimation.
  • Fengyun-4A (FY-4A) satellite remote sensing data were used as conditional input for the CGAN.
  • The generator incorporated a self-attention mechanism and residual blocks with a U-Net backbone, while the discriminator featured multi-level spatial feature fusion.

Main Results:

  • The proposed CGAN-based method demonstrated significant improvements in accuracy.
  • Compared to Attention U-Net and Pix2pix, the method achieved a 68.75% reduction in root mean square error (RMSE).
  • Pearson's correlation coefficient (CC) improved by 10.53%, indicating enhanced estimation performance.

Conclusions:

  • The developed method effectively estimates near-surface air temperature using satellite data, addressing data sparsity issues.
  • The integration of advanced deep learning techniques, including attention mechanisms and multi-scale feature fusion, enhances estimation accuracy.
  • This approach offers a promising solution for high-resolution air temperature monitoring.