Deep-learning-based stage-aware classification of Arctic melt ponds and their microtopographic associations from high-resolution UAV imagery

Abstract. Melt ponds are a key component of the summer Arctic sea-ice surface because their formation and evolution strongly affect surface albedo, energy absorption, meltwater redistribution, and sea-ice mass balance. Most previous remote-sensing studies have treated melt ponds as a single surface class, limiting the characterization of heterogeneous pond states during late-summer melt and refreezing. In this study, we developed MP-Unet, a stage-aware semantic segmentation framework for identifying Open, Transitional, and Frozen Melt Ponds from high-resolution unmanned aerial vehicle imagery acquired during the 14th Chinese National Arctic Research Expedition. MP-Unet integrates residual blocks with channel attention, atrous spatial pyramid pooling, attention-gated skip connections, and an auxiliary binary segmentation head. The full model achieved an F1-score of 0.9440 and a mean intersection over union of 0.7466, with class-specific IoU values of 0.5897, 0.7544, and 0.6539 for Open, Transitional, and Frozen Melt Ponds, respectively. Stage-resolved mapping revealed marked spatial heterogeneity among the five observation sites, while Transitional Melt Ponds accounted for approximately 80.3 % of the total pond area in the pooled sample. Pond area–frequency distributions showed a general scale-dependent decline and a sparse large-area tail, although the strength of the fitted scaling relationship varied among sites. Object-level analysis further showed that Frozen Melt Ponds generally had more compact and regular shapes, whereas Open Melt Ponds exhibited broader circularity distributions extending toward lower values. DEM-assisted analysis indicated significant stage-dependent differences in local relative elevation: Transitional Melt Ponds occupied lower local topographic positions than Frozen Melt Ponds, despite the absence of significant differences in distance to the nearest ridge-like feature. These findings demonstrate that stage-aware classification provides information beyond conventional binary melt pond mapping by linking surface-state identification with pond morphology and local microtopographic position. The proposed framework offers a practical basis for fine-scale observations of Arctic sea-ice surface evolution and for the validation and improvement of satellite and numerical melt pond products.