The snow cover distribution is crucial to Earth's climate and hydrological systems. Snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely utilized in climate research and water-source management. Three MODIS-derived fractional snow cover (FSC) products, namely, MOD10A1 version 6, MODSCAG, and MODAGE, are assessed to evaluate the accuracy of FSC data over the Tibetan Plateau from May 2013 to April 2015. A Landsat-8/ Operational Land Imager (OLI) snow map that is based on linear spectral mixture analysis is regarded as the “reference dataset.” A total of 149 OLI images that span the Tibetan Plateau are used. The performance of these three datasets is compared to the “reference dataset” by using binary and fractional metrics over forest, grass, and bare soil, and in the Himalaya region. The binary classification assessment reveals that larger local illumination angles and vegetation coverage at the snow boundaries resulted in omission errors in MOD10A1, and snow patchiness caused this model to miss snow. MODSCAG correctly identified snow pixels, but overestimated snow at the snow boundaries and at local illumination angles that exceeded 30°. MODAGE precisely recognized snow-free areas, but underestimated snow coverage because of snow patchiness. MODSCAG and MODAGE produced smaller root mean square errors (RMSE) than MOD10A1, likely because spectral mixture analysis is superior to the normalized difference snow index-based empirical method. The behaviors of these products differed at various spatial scales. The RMSE significantly declined with decreasing spatial resolution from 500 m to 2 km. However, when aggregated to 5 km, the RMSE increased because fewer pixels were involved in the calculation.

Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R² values ranging from 0.46 to 0.80.

The moderate resolution imaging spectroradiometer (MODIS) snow algorithm has been used to generate global fractional snow cover (FSC) at a pixel size of 500 m using a linear regression relationship (called “FRA6T”) between FSC and the normalized difference snow index (NDSI). However, the linear relationship is problematic because of the considerable NDSI variation in nonsnow conditions. In this letter, we propose a universal ratio snow index (URSI), which is the ratio of the visible reflectance and the sum of the near infrared and shortwave infrared reflectances. It is called “universal” because it has weak sensitivity under snow-free ground conditions and, therefore, can improve the stability of the linear snow index methodology. A comparison between NDSI and URSI with regard to estimate FSC using the linear snow index methodology is carried out for the Tibetan Plateau. The scatter plots of MODIS NDSI/URSI and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) FSC indicate that a linear relationship can be assumed for both NDSI and URSI for barren land conditions and is more appropriate for URSI than it is for NDSI in forested areas. Validation efforts show that the linear relationship using URSI (designated “FracURSI”) achieves fewer errors in FSC estimation compared with the developed NDSI method (“FracNDSI”), particularly for forested areas and for moderate FSC values. Averaged over all comparisons, the root-mean-square error (RMSE) of FSC estimates for FRA6T is 0.13, and for FracNDSI is 0.12, whereas FracURSI RMSE is 0.11.

The normalized difference snow index (NDSI) plays an important role in mapping snow cover with spaceborne visible and shortwave-infrared imagery. The NDSI variation depends on illuminating-viewing geometry and snow physical properties, including equivalent grain size (EGS), snow depth (SD) and impurity concentration, as well as fractional snow cover (FSC) within a mixed pixel; however, it is still not fully understood. To quantifiably characterize the pattern of snow NDSI variation, we use a light scattering model of snow to calculate bidirectional reflectance and consequent NDSI values for a wide range of illuminating-viewing geometries, SD, and EGS values. In the model designated bicontinuous snow model using Geometric Optics theory and Radiative Transfer Equation (bicontinuous-GO/RTE), snowpack is represented by a bicontinuous microstructure, and bidirectional reflectance is simulated based on geometric optics and vector radiative transfer equation. The discrete ordinates radiative transfer (DISORT) algorithm is used to simulate the soot concentration effect on snow NDSI. A soil spectral reflectance model (SOILSPECT) is utilized with the assumption of the linear spectral mixture of snow and soil to quantify the effect of FSC on NDSI. As for discontinuous forests, an analytical hybrid geometric-optical and radiative transfer (GORT) model in conjunction with the bicontinuous-GO/RTE model and the PROpriétésSPECTrales (PROSPECT) model is used to examine the effect of canopy cover, which is related to the maximal FSC viewable to satellites. Modeling results indicate that: 1) snow NDSI is comparably low at off-nadir viewing angles, and this effect is exacerbated by the decline in solar elevation but limited by large EGS; 2) snow NDSI increases with EGS yet becomes saturated at EGS of 500 $\mu \text{m}$ ; 3) the effect of SD that is as low as 1 cm on NDSI is rarely noticeable; 4) the concentration of internally mixed soot up to 1 ppm has little reducing effect on snow NDSI (<0.05); 5) the angular effects on the NDSI thresholding method to identify binary snow cover for soil background are nonsignificantly pronounced in the cross plane (CP); 6) NDSI increases linearly with FSC for soil background at nadir as well as in the CP; 7) the SNOw-MAPping algorithm (SNOMAP) method could omit snow cover at large zenith angles for solar zenith angles $> 20^{\circ }$ in forests; and 8) forests complicate the nonlinear relationship between NDSI and canopy cover with fully snow-covered ground beneath canopies. These findings imply important uncertainty sources of binary and FSC mapping with NDSI.