Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index

Abstract

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolutions on a global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrains using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectance after masking cloud and snow-free areas. The NDSI–FSC function is calibrated using Pléiades very high-resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5 × tanh(a × NDSI + b) + 0.5, where a = 2.65 and b = −1.42, yielding a root mean square error (RMSE) of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.

Snow Property Inversion From Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With Examples From MODIS and Landsat 8 OLI

“…For MODIS, a regression-based approach was developed to convert NDSI to fractional (i.e., subpixel) snow-covered area ( f sca ) [8], but this technique showed high root mean square error (RMSE) values [9] so the MODIS data system no longer provides a fractional snow product. A nonlinear (sigmoid) regression of f SCA derived from the high resolution (0.5-2 m) Pléiades constellation versus NDSI calculated from the Sentinel-2 satellites showed RMSE values of 0.25-0.38 in the Pyrenees, with the least certain results in regions of rugged topography [10].…”

Retrieval of Snow Albedo and Total Ozone Column from Single-View MSI/S-2 Spectral Reflectance Measurements over Antarctica

“…The climatic effects of snow surfaces depend both on snow fraction and snow albedo. The retrieval of snow fraction using MSI/S-2 measurements are performed in [34,35] at 20 m resolution in open terrain. In this paper a simple algorithm to retrieve the snow albedo and total ozone column using the high spatial resolution single-view MSI/S-2 measurements over Antarctica is proposed.…”

A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

“…To evaluate the effect of the corrections on each dataset, four metrics were selected for evaluation using both the uncorrected and corrected datasets, following the approach of recent similar studies that compare snow cover retrievals from lower and higher resolution datasets [17,30,31]. We have first calculated the mean error, which indicates the bias of the dataset being evaluated.…”

“…A systematic study of the impact of such shadow for all the remote sensing datasets is needed to quantify this problem. Other recent studies that have investigated methods to retrieve fractional snow cover from Sentinel-2 have modelled the NDSI-snow cover fraction relation as a nonlinear sigmoid-shaped function [31], which may present a better approach for calibrating the MODIS data against Sentinel-2 in future. However, since this study was based on a test site in alpine mountainous terrain, the calibration coefficient may not necessarily be universal and may need to be revised for polar regions such as Svalbard and is the main reason this approach was not adopted in this study.…”

“…The RMSE of 8.6% in the uncorrected dataset is somewhat smaller than the RMSE obtained in their study, which was of the order of 14-15%, but here we have used Sentinel-2 data rather than very high-resolution optical images as a reference. In several earlier studies, the RMSE of the dataset being evaluated is often reduced when the reference data are aggregated to coarser resolution [17,31] as was done with the Sentinel-2 data in this study. Moreover, at the resolution of the MODIS data, the correction using the spline relationship resulted in just 1% reduction in RMSE and no change in correlation, while the mean error was reduced to 0%.…”