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

Abstract

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.

Keywords:

• Snow cover
• Snow accumulation and ablation
• Landsat MODIS data fusion
• Algorithm development
• Time-lapse photography
• Time series

Acknowledgements

Funding for this research was generously provided by MITACS, and St’át’imc Government Services (SGS) facilitated this project through many collaborative efforts, which included providing equipment, feedback, and assistance in the field (for more information, visit http://statimc.ca/programs/sgs-environment/). Darwyn John (SGS) and Denise Antione (SGS) have particularly helped in this project through feedback and field work. Dr. Sue Senger offered much support throughout the project with guidance, opportunities to network, and help with logistics. Scott Taylor is the Lillooet Base Manager and pilot for Black Comb Helicopters (blackcombhelicopters.com), whose assistance has made this project possible by accessing remote locations for validation sites. Dr. Cole Burton (UBC) has also been an advisor and supporter of this project, offering guidance with future projects in mind. We are also grateful to the two anonymous reviewers for their comments on an earlier version of the manuscript, which greatly helped to improve the clarity and impact of this article. For more information on provincial manual and ASP data please contact [email protected]. For information on federal climate data, contact [email protected]. Finally, we would like to thank every individual who has allowed for some of the cameras to be installed within their private properties: Linda and Tom Hancock, Eckhard and Deanne Zeidler, and Jacquie and Verne Rasmussen.