Sentinel-2 NDSI

    Quasi Global (83 degrees N, 56 degrees S latitude)
Spatial Resolution
Data Source(s)
    Copernicus Programme
Science Partner


The Sentinel program is composed of two satellites (Sentinel 2A, 2B) and was developed to monitor land surface changes in vegetation, the environment, and land cover. Sentinel-2 spatial, spectral, and temporal resolutions allow for imagery to be processed at sufficient spatial and temporal detail that is optimal for field scale land surface monitoring, management, and research. Sentinel-2 datasets are available for Sentinel 2A, 2B, and composite of 2A/2B using top of atmosphere or at surface reflectance data.

Normalized Difference Snow Index (NDSI) provides information on snow cover. It typically ranges from -1 to1 with values from 0.5 to 1 representing snow coverage. Information on snow coverage and depth is an important resource in water resource management, planning, and forecasting. Monitoring snow extent using satellite imagery is useful for understanding snow depletion and recession rates, evaluating snow extent relative to long term average conditions, and is a useful drought metric.

NDSI is calculated as the normalized difference between a visible band (VIS) and short wave infrared band 1 (SWIR1) following the formula NDSI = (VIS-SWIR1)/(VIS+SWIR1) (Crane and Anderson, 1984; Dozier, 1984). Here the VIS wavelength is the green band. This method is effective at separating clouds and snow since snow is highly reflective in VIS wavelengths and absorptive in the SWIR1 wavelength while clouds are highly reflective in both (Hall et al., 2001).

Bands Used:
Green: 0.560 μm (S2A) / 0.559 μm (S2B)
Short Wave Infrared 1 (SWIR1): 1.6137 μm (S2A) / 1.6104 μm (S2B)

Technical Information

Quasi Global (83 degrees N, 56 degrees S latitude)
Period of Record
2015-present for 2A; 2017-present for 2B
Spatial Resolution
Temporal Resolution
2-3 days (at mid latitudes), 5-10 days (at equator)
Data Summaries
max, min, mean, median, anomalies, trend and statistical significance, spatial and temporal aggregations, time series
Data Source(s)
European Space Agency (ESA)
Data Formats
raster (geotiff), raster tile (tile ID), time series (.csv, .xls, .json, .geojson)

Crane, R. G., and Anderson, M. R., 1984, Satellite discrimination of snow/cloud surfaces. International Journal of Remote Sensing, 5(1), 213 ­223.

Dozier, J., 1984, Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment, 28.9-22.

Hall, D. K., Riggs, G. A., Salomonson, V. V., Barton, J. S., Casey, K., Chien, J. Y. L., … & Tait, A. B. (2001). Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms. Nasa Gsfc, 45.

Level-2A Algorithm Overview (Accessed on 7/30/2020)

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