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

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 (R2 = 0.76, RMSE = 19.58 %). Further validation against 46 in-situ snow depth stations indicated a high binary classification accuracy of 0.91. As a robust and practical method for large-scale FSC mapping, TAFF shows promise for integrating additional data sources, such as geostationary and microwave sensors, to enhance the high-resolution monitoring of ephemeral snow.