XFuse: Multi-sensor CNN transformer fusion with cross attention and quality aware curriculum learning for high resolution fractional snow cover mapping

Accurate fractional snow cover (FSC) mapping requires combining the complementary strengths of optical imagery (spectral discrimination) and Synthetic Aperture Radar (all-weather capability). Yet existing fusion approaches employ simplistic concatenation strategies that fail to capture complex inter-modal relationships, while taking into account the resolution mismatch between high-resolution inputs and coarse reference labels. This paper introduces XFuse , a hybrid CNN-Transformer architecture that addresses these challenges through three key contributions: (1) bidirectional cross-attention fusion that explicitly models SAR-optical interdependencies via query-key–value transformations, enabling each modality to selectively attend to complementary information from the other; (2) a multi-resolution training strategy ( 10 m → 60 m → 10 m ) that fuses Sentinel-1/2 features at native resolution, aggregates to 60 m for supervision against Gap-Filled Fractional Snow Cover (GFSC) labels, and disaggregates through guided upsampling to restore fine spatial structure at 10 m ; and (3) quality-aware curriculum learning that incorporates multi-source metadata (cloud masks, atmospheric parameters, quality control flags) through adaptive loss weighting, enabling robust training from heterogeneous satellite data while providing calibrated uncertainty estimates. Evaluation on 29 Sentinel-1/2 tiles from Northern Finland (18,117 test patches from 4 geographically unseen tiles) demonstrates state-of-the-art performance: 13.77% MAE and 94.66% classification accuracy, representing 38.7% MAE reduction over the best baseline. Ablation studies confirm cross-attention as the largest individual contributor (5.16 percentage point accuracy gain), while uncertainty quantification achieves excellent calibration (ECE=0.025). The methodology generalizes to earth observation applications requiring fusion of heterogeneous multi-resolution satellite data