Snow climatology in Lithuania based on the cloud‐free moderate resolution imaging spectroradiometer snow cover product

Snow cover plays an important role in environmental, hydrological and climate systems. To monitor the inter‐annual and seasonal variation of snow in Lithuania, daily moderate resolution imaging spectroradiometer (MODIS) snow cover products MOD10A1 and MYD10A1 were employed during the period from 2002 to 2018. The main disadvantage of the MODIS sensor is that it is unable to determine the surface conditions under cloud cover. In this study, a four‐step procedure was implemented to remove clouds from the MODIS snow products and estimated the annual and monthly number of snow cover days (SCDs). The steps included a combination of MODIS data from Aqua and Terra satellites, spatial and temporal filtering of cloud covered pixels. Additional daily minimum temperature control filter helped to reduce the misclassification snow errors. The final daily cloud‐free MODIS snow maps showed an overall accuracy of 89% when compared with manual ground observations. The lowest validation scores were determined on the Baltic Sea coast, where snow cover is ephemeral, and the highest scores were in the eastern part of the country where the climate is continental. The MODIS‐based SCD had larger relative errors in autumn, smaller errors in spring and very good agreement with the in situ observations in the middle of winter. Although the generated cloud‐free MODIS snow cover product tended to overestimate the annual SCD on average by 8.5 days, we consider it adequate to describe the inter‐annual and monthly variation of snow cover in Lithuania. Monitoring of snow cover using MODIS instrument is limited due to the high cloudiness in winter in mid‐latitudes. In this study, we implemented the four‐step procedure to remove clouds from the MODIS snow product and estimated the annual and monthly number of snow cover days (SCDs). The final daily MODIS snow maps showed an overall accuracy of 89%, with larger relative errors in autumn months, smaller errors in spring and very good agreement in the middle of winter.