Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI

Karl Rittger,Jeff Dozier,Kat J Bormann,Thomas H Painter,Edward H Bair

doi:10.3389/frsen.2021.647154

Abstract

We present the first application of the Snow Covered Area and Grain size model (SCAG) to the Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals with finer‐resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI). Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visible wavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluated using data from earlier Landsat sensors. Use of the fractional snow cover maps from Landsat 8 OLI shows a negative bias of −0.5% for MODSCAG and −1.3% for VIIRSCAG, whereas previous MODSCAG evaluations found a bias of −7.6% in the Himalaya. We find similar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS, respectively. The Recall statistic (probability of detection) for cells with more than 15% snow cover in this challenging steep topography was found to be 0.90 for both MODSCAG and VIIRSCAG, significantly higher than previous evaluations based on Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition, daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope, and aspect. Different native resolutions of the gridded products at 1 km and 500 m for VIIRS and MODIS, respectively, result in snow cover maps showing a slightly different distribution of values with VIIRS having more mixed pixels and MODIS having 7% more pure snow pixels. Despite the resolution differences, the snow maps from both sensors produce similar total snow-covered areas and snow-line elevations in this region, with R2 values of 0.98 and 0.88, respectively. We find that the SCAG algorithm performs consistently across various spatial resolutions and that fractional snow cover maps from the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be a suitable replacement as MODIS sensors reach their ends of life.

Highlights

Earth’s cryosphere is rapidly diminishing in extent and volume (Bormann et al, 2018)
When we expand the evaluation to the full set of coincident scenes for the period 2012–2015 for the two tiles shown in Figure 2 and limit to clear sky dates (
The reason for the improvement in MODSCAG performance revealed in this analysis is due to the improved Landsat 8 Operational Land Imager (OLI) validation data, improved sensor technology that avoids saturation in the visible bands and systematic overestimation bias in the validation maps from the assumption of 100% snow cover when bands 1, 2, and 3 were saturated in Landsat Thematic Mapper (TM) and ETM+

Summary

Introduction

Earth’s cryosphere is rapidly diminishing in extent and volume (Bormann et al, 2018). Global climate models show that more countries will experience water stress in the future, those that rely on melt of snow and ice for their water resources (Arnell, 1999). Observational datasets of glaciers show mass loss on a global scale (Zemp et al, 2015). In the mid-latitudes especially, glaciers ablate faster when the winter snow cover disappears earlier (Painter et al, 2013). Interannual variability and trends in snow cover result from variability in snowfall and the rate of snowmelt, which is driven by the energy balance at the snow surface and is especially sensitive to snow albedo. A comprehensive view of changes and variability in snow extent and albedo can be observed directly only with satellite remote sensing

Results

Discussion

Conclusion