Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow maps, derived from MODIS imagery, provide managers and researchers with vital snow cover information, but only at spatial scales of 500 m or more. Finer resolution time series maps, however, retain large temporal gaps, particularly during recurrent cloud cover. This paper’s authors have developed the novel algorithm MODSAT-NDSI to harness the strengths of both coarse and finer spatial resolution imagery by fusing MODIS and Landsat normalized difference snow index (NDSI) data. Daily 30 m snow cover maps were thus generated for 2000 – 2017 with an overall accuracy of 90%, using 33 validation sites distributed throughout south-central British Columbia. Snow cover trends were analyzed across stratified elevation bands and land cover types, revealing that snow cover persists under lower elevation forests for an average of 23.5 d longer than in adjacent open areas during spring. We conclude that the MODSAT-NDSI approach captures temporal and spatial advantages of freely available snow cover datasets and can be modified to suit a variety of novel investigations relating to snow cover or other spectral indices.