Deep-Learning-Based Zero-Sample Gradient Guidance Spatial Resolution Enhancement for Microwave Radiometer in Fengyun-3D

For satellite brightness temperature images, researchers are constantly pursuing higher resolutions to obtain more detailed meteorological information. In this article, a novel deep-learning-based modeling approach, named zero-sample gradient guidance spatial resolution enhancement (ZSGRE), is developed explicitly for microwave radiometers. The detailed model, including mathematical derivation and key parameters, is presented. Subsequently, the proposed approach is applied in four scenarios: synthetic scene, simulated geographical brightness temperature, practical measurement of microwave radiometer in Fengyun-3D (FY-3D), and a cyclone analysis on the Atlantic. Compared with other methods, the proposed ZSGRE method improves 2.51% of structural similarity (SSIM), enhances 2.3 dB of peak signal-to-noise ratio (PSNR), and decreases 15.8% of instantaneous field of view (IFOV). Such applications demonstrate ZSGRE’s significant performance: zero-sample preparation and spatial resolution enhancement.