Abstract
Abstract. Snow cover is one of the features that change rapidly on the surface, and remote sensing data with high spatial and temporal resolution is an important means to monitor the dynamic changes of snow. However, due to the limitation of satellite conditions, it is difficult to acquire remote sensing images with both high temporal and spatial resolution at the same time. The classical Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) breaks through the limitations of a single sensor and effectively brings into play the complementary advantages of different observation platforms. In this paper, we take the Babao River Basin within the Qinghai-Tibet Plateau as the study area to investigate the effect of fusion of Sentinel-2 and MODIS (Moderate-resolution Imaging Spectroradiometer) based on the ESTARFM and analyze the fusion-supplemented snow accumulation period data. The results show that the fusion effect of Sentinel-2 and MODIS is good in terms of visual and multiple quantitative indicators. The snow accumulation area of the fused image is also close to the real image. By fusing to supplement the missing Sentinel-2 data, a variation map of snow NDSI (Normalized Difference Snow Index) in the study area was obtained. It was found that the snowfall process in the watershed appeared first in the surrounding uplands and then in the flat areas, and the maximum rate of snow cover area change was 116.17 km2. Therefore, the spatiotemporal data fusion of Sentinel-2 and MODIS based on ESTARFM algorithm can provide reliable satellite remote sensing data with a higher spatiotemporal resolution for snow accumulation change monitoring.