季节性积雪的演变对于山区水文循环具有决定性影响，同时也是调控陆地生态系统的关键要素。精确监测融雪动态对于气象、水文以及全球气候变化的研究至关重要，同时也对灾害预测、预警发挥着不可或缺的作用。然而，传统的基于时间序列合成孔径雷达（SAR）的融雪监测技术常受到植被、地形及积雪特性等因素的影响，并且在重访周期较长的区域监测效果有限。本研究利用高时空分辨率的Sentinel-2卫星光学遥感数据，基于雪表粒径的时间序列变化信息，提出了一种新颖的融雪监测方法。以阿勒泰地区为例，参照站点雪水当量和气温数据，深入分析了新方法的融雪监测性能。本文对比了新融雪监测方法与常规的时间序列SAR方法，探讨了两者在山区融雪监测方面的优势与局限。结果表明该新方法能够较为准确地识别融雪起始时间，并且在植被和混合像元等干扰因素的影响下，展现出了比时间序列SAR方法更出色的监测能力。但是，新方法易受云雨气象条件的影响，未来可与时间序列SAR方法互为补充，共同提升山区融雪监测的时效性和准确性。

Snow seasonal evolution is one of the key factors influencing hydrological dynamics in mountainous areas and controlling terrestrial ecology. Accurate information on snowmelt is essential for meteorological, hydrological and global climate change studies as well as for disaster prediction and early warning. The traditional snowmelt detection approach based on time series SAR suffers from the influence of vegetation cover, rugged terrain and long revisit time in some regions. In this study, we propose a new snowmelt detection method based on high resolution Sentinel-2 optical remote sensing data. The time series snow surface grain size variation is used to detect snowmelt events. The physical principle and methodology are explained in this paper, and snowmelt detection results of Altay Mountain is presented and analyzed. Snowmelt detection based on optical remote sensing is also compared with SAR method, the advantages and shortcomings of the two methods are analyzed. Validated by station measured snow water equivalent and air temperature data, The proposed optical data based snowmelt detection method suffers from cloud cover, but it offers an alternative way to detect wet snow with high spatial resolution other than SAR. Moreover, the optical data based method has advantages comparing with SAR based method, such as areas with high tree cover fraction.