Daily seamless 30-m fractional snow cover mapping via an adaptive Time-Series approach

作者：Cheng Zhang, Lingmei Jiang, Jinmei Pan, Jianwei Yang, Jian Wang, Zongyi Jin · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2026 · DOI：10.1016/j.jag.2025.105068 · 研究领域：Cryospheric studies and observations、Remote Sensing in Agriculture、Synthetic Aperture Radar (SAR) Applications and Techniques

• A novel time-series technique (TAFF) generates seamless daily 30-m FSC maps. • Robust to cloudy inputs with high efficiency and reliable accuracy. • Captures rapid snow dynamics by leveraging time-series observations. • Suitable for automated, large-scale production and future data integration. Accurate daily mapping of 30-m fractional snow cover (FSC) is critical for hydrological modeling and disaster assessment. Frequent cloud cover and satellite revisit cycles create significant data gaps in high-resolution optical imagery (e.g., Landsat, Sentinel-2), hindering the continuous monitoring of rapid snow dynamics. To address these limitations, we propose the Time-series-based Adaptive snow-Fraction Fusion (TAFF) framework for generating seamless daily 30-m FSC over large scales. The core of TAFF is a dual-path fusion strategy that adapts to the physical state of the snowpack. First, a time-series-based snow stability assessment gauges the magnitude of temporal FSC change. This assessment then directs the fusion process: stable snow is processed using time-weighted fusion, while rapidly changing snow is handled by a pixel-level regression. Evaluated over the Qinghai-Tibet Plateau, TAFF demonstrates robust improvements over established spatiotemporal fusion algorithms, particularly under cloudy conditions. Independent validation against 215 Landsat 8 images yielded strong performance (R 2 = 0.76, RMSE = 19.58 %). Further validation against 46 in-situ snow depth stations indica...