Enhancing Passive Microwave Brightness Temperature Using Dual-Attention SRGAN

This study presents a Dual-Attention SuperResolution Generative Adversarial Network (DA-SRGAN) framework to enhance the spatial resolution of passive microwave (PMW) brightness temperature (TB) data from 12.5/25 km to 3.125/6.25 km. By integrating channel and spatial attention mechanisms into the SRGAN architecture, the proposed model prioritizes critical low- and mid-frequency features inherent to PMW measurements, effectively suppressing noise while preserving radiometric accuracy and geophysical structures such as atmospheric fronts, precipitation bands, and coastlines. Quantitative evaluations demonstrate DA-SRGAN's superiority over baseline models (SRCNN, SRGAN) across metrics including MAE, RMSE, SSIM, and LPIPS, with cosine similarities exceeding 0.995 in histogram-based distribution comparisons. Cross-sensor validation with AMSR2 89 GHz data demonstrates the model's ability to maintain statistical relationship between sensors at enhanced spatial resolution despite hardware and frequency differences. Spatio-temporal consistency analysis across diverse environmental conditions reveals robust performance with RMSE values consistently below 1K across all surface types and seasons, demonstrating temporal stability and regional consistency. Exploratory assessment at finer resolution (3.125/6.25 km) confirms effective spatial detail enhancement while preserving statistical distribution integrity. These advancements position DA-SRGAN as a transformative tool for bridging resolution gaps in PMW satellite remote sensing, offering improved observational fidelity for environmental applications.