Accurate delineation of glacier boundaries is essential for monitoring glacier changes. Debris-covered glaciers often resemble the textures of their surrounding terrain, making boundary extraction especially challenging. Interferometric coherence from Synthetic Aperture Radar (SAR) can help highlight these glaciers by reflecting surface changes. However, because optimal temporal baselines vary with glacier activity, this significantly limits the broader effectiveness of coherence for regional glacier boundary extraction. To address this, we introduce the Feature-based Amplitude Coherence Ratio (FACR), a new index that advances glacier mapping by framing temporal baseline selection as an optimizable feature. Unlike traditional approaches, FACR uses an F-test to automatically identify interferometric pairs that best separate glacier ice from stable terrain. We tested this method on glaciers in the Gyala Peri area of the southeastern Tibetan Plateau, using 14 ALOS-2 PALSAR images. The results show that FACR offers stronger robustness in capturing glacier surface information and achieves higher boundary accuracy (IoU: 0.68, Dice: 0.81) than other indices. FACR therefore provides a practical approach for regional glacier inventories and change detection.