CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II

Abstract

Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems. To enhance the spatial resolution of these observations toward this scale, we propose the Channel-Decoupling and Geographic-Prior Fusion Network (CDGP-Net), an unsupervised hyperspectral–multispectral fusion framework that reconstructs 4 km high-spatial-resolution hyperspectral radiances by fusing the FengYun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) data with co-platform Medium Resolution Spectral Imager II (MERSI-II) imagery while preserving the original spectral sampling. To adapt hyperspectral–multispectral fusion to infrared sounder data, CDGP-Net incorporates two components: a self-reconstruction and spectral-degradation channel-decoupling (SDCD) design, which allows physically related non-overlapping MERSI-II infrared information to be used as an auxiliary input while keeping the spectral degradation physically consistent; and a reconstruction-domain geographic-prior regularization (RGPR) scheme, which constrains the reconstructed radiances in both geographic space and spectral shape. Because true high-resolution observations are unavailable, we further introduce a radiative-transfer-anchored evaluation (RTAE) scheme that uses the line-by-line radiative transfer model (LBLRTM) simulations driven by reanalysis and forecast atmospheric fields as independent physical references. For the selected FY-3D overpass cases, the evaluation using Ref-HR as the high-resolution physical reference shows that CDGP-Net improves the peak signal-to-noise ratio (PSNR) by 3.8 dB and reduces the spectral angle mapper (SAM) and erreur relative globale adimensionnelle de synthèse (ERGAS) by 64.1% and 54.6%, respectively, compared with the unmixing baseline. Under the same evaluation conditions, relative to geographic interpolation, it improves the structural similarity index measure (SSIM) by 16.4% and reduces ERGAS by 8.8%, with the clearest advantages in partial-cloud and coastal transition scenes.