What SAR Labels Hide about a Changing Arctic

Abstract Synthetic aperture radar (SAR) has been the backbone of Arctic sea ice monitoring since the 1990s, yet nearly every operational product that assigns an ice class or geophysical state (melt onset dates, freeze‐up timing, ice‐type classifications) is derived by compressing continuous radar backscatter into discrete categorical labels. This Commentary argues that such compression is not merely a simplification but a systematic loss of physical information that increasingly compromises trend attribution and cross‐sensor synthesis as the Arctic transitions from a perennial to a seasonal ice regime. A semi‐empirical forward‐model demonstration shows that physically distinct ice states can produce indistinguishable C‐band backscatter yet separate cleanly at L‐band, confirming that single‐frequency categorical detection is fundamentally non‐unique. The multi‐frequency SAR constellation now in orbit (Sentinel‐1 and RCM at C‐band, NISAR at L‐ and S‐band) provides the measurement basis to move beyond labels toward physically based retrieval. A Bayesian inversion framework that outputs continuous state estimates with formal uncertainty bounds could preserve backward‐compatible categorical products for operational ice services while adding the process‐level traceability that climate science demands. Realizing this architecture requires coordinated investment in multi‐frequency forward models, designated Arctic supersites, and in situ campaigns targeting the snow microstructure variables that remain the largest source of retrieval ambiguity.