Examining Thresholding and Factors Impacting Snow Cover Detection Using Nighttime Images

Abstract

Nighttime remote sensing data from the Visible Infrared Imaging Radiometer suite day/night band (VIIRS DNB) enable snow cover detection from full moonlight reflection. Using nighttime data is particularly relevant in areas with limited daytime hours due to high latitudes. Previous studies demonstrated the potential of using thresholding methods in detecting snow, but more research studies are needed to understand the factors that influence their accuracy. This study explored seven thresholding algorithms in four case study areas with different characteristics and compared the classified snow results to the MODIS MOD10A1 snow cover product. The results found that Li thresholding delivers higher accuracies for most case studies, with an overall accuracy between 65% and 81%, while mean thresholding performed best in mountainous regions (70%) but struggled in other areas. Most false negatives are caused by forests, especially closed and evergreen forests. The analysis of NDVI data matches these findings, with the NDVI of false negatives being significantly higher than true positives. False positives appear to be primarily located in or around built-up areas. This study provides insights into where nighttime VIIRS DNB data can be used to increase snow cover data temporal and spatial coverage.