An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales

Abstract

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.

Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System

“…The spectral mixture analysis method was adopted in MODIS as the MODIS Snow Covered Area and Grain size (MODSCAG) algorithm (Painter et al, 2009), as well as for Landsat 8 and VIIRS (Rittger et al, 2021). All previous validation shows the higher accuracy of spectral mixture analysis compared to NDSI (Aalstad et al, 2020;Masson et al, 2018;Rittger et al, 2013;Stillinger et al, 2023), encouraging continued development (Bair et al, 2020;Keuris et al, 2023). Challenges remained, however, such as cloud gaps, distorted viewing geometry from the satellite view angle (Dozier et al, 2008;Pahlevan et al, 2017), and reduction of viewable snow cover in forests (Raleigh et al, 2013;Rittger et al, 2020).…”

Remote sensing of mountain snow from space: status and recommendations

“…Similarly, spectral unmixing methods developed for MODIS are being adapted to Landsat 8/9 and Sentinel-2 (Aalstad et al, 2020;Bair et al, 2020;Stillinger et al, 2023). Keuris et al (2023) proposed a method for estimation fractional snow extent exploiting the full spectral capabilities of moderns satellite sensors such as Sentinel-2, Landsat-7/8/9 and Sentinel-3 SLSTR and OLCI, applying multispectral unmixing with local adaptive endmember selection and accounting for the high variable solar illumination in mountainous areas. These approaches take advantage of all available spectral information to retrieve fractional snow cover and other properties such as snow albedo, grain size or the presence of light absorbing particles (Nolin et al, 1993;Painter et al, 2009).…”