Thiebault, K; Young, S (2020). Snow cover change and its relationship with land surface temperature and vegetation in northeastern North America from 2000 to 2017. INTERNATIONAL JOURNAL OF REMOTE SENSING, 41(21), 8453-8474.

Snow cover has a major influence on the global energy balance through the reflection of shortwave solar radiation as well as influencing ecological processes and human activity. Numerous studies have found that snow cover extent (SCE) is decreasing in the Northern Hemisphere and this decline appears to be influencing temperatures and might be a major factor in the polar amplification. This research used satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data (MOD10C2), Land Surface Temperature (LST) data (MOD11C3), and Normalized Difference Vegetation Index (NDVI) data (MOD13C2) to detect changes in SCE and its potential relationship to changes in land surface temperature and vegetation growth from 2000 to 2017 over northeastern North America. There is a lack of detailed research concerning these variables for northeastern North America. The data were composited into seasonal and annual (snow-year: September-June) groupings. Two different change analyses were undertaken: 1) significant change using the Mann-Kendall statistical analysis and 2) univariate differencing using three different time periods (3 years, 5 years, 8 years). A regression and correlation analysis was undertaken between SCE and LST and NDVI to determine the relationship between changing SCE and changes in LST and NDVI. Based on the Mann-Kendall statistical change analysis (p-value = 0.05) for the 16-day data (32-day data), the area of declining SCE was more than 12 times the area of increasing SCE (more than 5 times for 32-day data) with declines occurring in all seasons, most notably in fall, June and the entire snow-year. Based on the univariate differencing analysis, SCE declined more than increased 96% of the time. Based on the regression/correlation analysis, SCE explains variability in LST (NDVI) for the snow-year: 43% (51%), spring: 31% (22%), June 34% (no significant relationship), fall: 40% (no significant relationship), and winter with no significant relationship (30%). It was determined that there is a weak to moderate inverse relationship between SCE and LST and a similar, but less prominent relationship between SCE and NDVI. A multiple regression/correlation with SCE and LST (independent) and NDVI (dependent), LST was a better predictor of NDVI than SCE. This relationship indicates that there is a potential positive feedback mechanism warming the region and increasing the region's NDVI.