Support-Aware Dual-SWIR Downscaling from Sentinel-2 and Submetre Jilin-1 Imagery on Non-Nested Grids

Fine-scale shortwave-infrared (SWIR) imagery can support detailed land-surface mapping, but submetre satellites generally acquire only blue, green, red and near-infrared (RGBN) bands. Combining these data with Sentinel-2 SWIR requires an observation model that accounts for non-nested pixel supports. We developed a support-aware inverse-allocation framework using an RGBN-conditioned prior, exact polygon-overlap weights and a continuous correction field. The framework was evaluated in four regions of Xinjiang, China, using retained 20 m B11/B12 observations as reference. In a strictly matched control, replacing centre ownership with exact area overlap reduced mean joint root-mean-square error (RMSE) by 0.00174–0.00217 in reflectance units. The reduction occurred in all 15 non-nested phases and all 16 spatial blocks of each region. Continuous correction provided a further reduction of 0.000066–0.000166. Under a matched single-observation budget, the method achieved lower mean joint RMSE than adapted area-to-point regression kriging and DSen2 baselines in all four regions. Exact area overlap retained its advantage under controlled blur, ±10 m guide-image offsets and scale ratios of 2, 3 and 6, although the advantage narrowed at the coarsest ratio. Full-scene application to 0.75 m Jilin-1 RGBN and 20 m Sentinel-2 SWIR produced observation-consistent conditional SWIR allocations, with accuracy evaluated at the controlled 20 m reference resolution.