Daily snow cover mapping based on Dynamic Wavelength Warping using geostationary satellite data

Snow cover mapping is a form of precipitation that detects snow-covered pixels by observing snow accumulated on the ground. Snow is the largest single component of the cryosphere and has high reflectance compared to other index, so it plays an important role in maintaining heat balance between the Earth’s surface and the atmosphere, or in maintaining the balance of the Earth’s energy balance in terms of global or regional aspects. In case of snow cover mapping using satellite data, a wide range of data can be easily obtained and time series observations can be made periodically for the same area. Although the characteristics of snow appear in satellite data show differences in reflectance compared to snow-free, the reflectance change pattern depending on wavelength also has a unique pattern. We focused to the unique reflectance change pattern according to the wavelength of the snow, and used the Dynamic Wavelength Warping (DWW) method to perform the snow cover mapping using the unique pattern. The DWW is a method that determines the similarity of change patterns by using reflectance change pattern according to wavelength. in this study, daily composite snow cover mapping was calculated using snow cover data calculated using DWW method. In order to evaluate the accuracy of the synthetic snow cover data calculated from this study, we used the Global Multisensor Automated Snow/Ice Map (GMASI) data from the National Oceanic and Atmospheric Administration (NOAA) and conducted quantitative and qualitative evaluations. As a result, Probability of Detection (POD) was 97.14 % and False Alarm Ratio (FAR) was 1.96 %.