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

Abstract

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.

A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

“…TAC has been used in many kinds of research to remove clouds [34][35][36][37]40]. As the Aqua and Terra satellites have different transit times but similar designs and performance, it is feasible to combine Terra (MOD10A1) and Aqua (MYD10A1) data to remove clouds.…”

“…Parajka and Blöschl [34] first presented a cloud removal method with three steps: Terra and Aqua image combination (TAC), spatial filtering using the nearest neighbors, and temporal filtering. This method has been improved by incorporating the snow line, air temperature, elevation information, and so on [35][36][37]. However, all these cloud removal methods use only spatial and temporal information step by step and ignore some important neighborhood information at small spatiotemporal distances.…”

HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model

“…Although these satellites have a high spatial resolution, they have a relatively coarse (10 or 16 d) temporal resolution, thereby rendering them insufficient to monitor the temporal variations in snow cover . Moderate Resolution Imaging Spectroradiometer (MODIS) has commenced producing snow products at 500 m resolution from 2000, which have been widely utilized as the primary datasets for monitoring snow cover (Muhammad and Thapa, 2020;Kilpys et al, 2020). The accuracy of MODIS snow products is greater than 85 % at the global scale under clear sky (e.g., Parajka et al, 2012;Yang et al, 2015).…”

“…Spatial methods estimate data-gap pixels based on gap-free pixels in the spatial neighborhood (Hou et al, 2019). Relevant environmental information (e.g., snow lines and topography) has been introduced into spatial methods Kilpys et al, 2020). Spatiotemporal methods have been integrated to fill as many data gaps as possible (Parajka and Blöschl, 2008;Kilpys et al, 2020).…”

“…Relevant environmental information (e.g., snow lines and topography) has been introduced into spatial methods Kilpys et al, 2020). Spatiotemporal methods have been integrated to fill as many data gaps as possible (Parajka and Blöschl, 2008;Kilpys et al, 2020). These studies integrated all information (i.e., spatiotemporal, and environmental information) according to heuristic rules instead of rigorous quantitative models.…”