High-resolution snowmelt detection in mountainous areas based on remote sensing retrieved snow surface grain size variation

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 and 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. As snow starts to melt, the liquid water in snow tend to increase the optical equivalent grain size retrieved from optical remote sensing remarkably. When the wet snow refreezes, the optical equivalent grain size remains considerably larger than dry snow. This provides the theoretical basis for snowmelt onset detection from optical remote sensing. The snow surface optical equivalent grain size is retrieved by applying snow reflectance models, with bidirectional reflectance, sun zenith angle, sensor viewing angle, and relative azimuth angle as inputs. Pure snow pixels are selected for snow optical equivalent grain size retrieval and snowmelt detection. In this study, snowmelt detection results of the Altay Mountain are presented and analyzed. Snowmelt onset detection based on optical remote sensing is also compared with SAR method, and the advantages and shortcomings of the two methods are analyzed. The main advantages of the proposed new method in this study are as follows The new method based on optical remote sensing is more sensitive in detecting the occurrence of snowmelt and provides richer information about the snow melting process. The choice of snow reflectance model can introduce differences in the retrieved snow grain size due to variations in modeling snow particle shape and light scattering and absorption. Additionally, the selection of threshold values for distinguishing between wet and dry snow grain sizes can affect the results. Therefore, the use of different snow reflectance models can lead to certain differences in the results of snowmelt detection. The snowmelt onset dates retrieved using the optical method show overall similarity to those retrieved from the SAR method, exhibiting similar dependencies on elevation and aspect. However, some differences between the two methods, which can be attributed to variations in detection principles and data sources, are observed. Compared with the SAR method, the optical method is less affected by speckle noise, mixed pixels, and vegetation cover. Particularly in low-elevation and vegetated areas, the proposed method demonstrates superior capability in detecting snowmelt events compared to the SAR method. The snowmelt onset date retrieved using Sentinel-2 data is similar to those retrieved from the SAR method using Sentinel-1 data, and they show similar dependencies on elevation and aspect. The new method based on Sentinel-2 data also shows advantages over the SAR method, e.g., the optical method is less affected by speckle noise, mixed pixels, and vegetation cover, and it provides more spatial details about the snowmelt onset data. The proposed snowmelt detection method based on optical data suffers from cloud cover, but it offers an alternative way to detect wet snow with high spatial resolution other than SAR. The snowmelt detection based on SAR and Sentinel-2 data can be complementary to each other, and the snowmelt detection in mountainous area can be improved by combining both methods. © 2024 Science Press. All rights reserved.