MODIS NDSI

Overview
Extent
    Global
Spatial Resolution
    500m
Data Source(s)
    National Aeronautics and Space Administration (NASA)
Science Partner

Description

The MODIS sensors aboard the twin satellites Aqua and Terra provide global coverage of earth’s surface every 1 to 2 days. MODIS’s spatial, spectral, and temporal resolution allow for imagery to be utilized for regional monitoring of vegetation, land surface temperature, and the environment. MODIS data is available for Terra (daily, 8 day, or 16 day composites), Aqua (daily, 8 day, or 16 day composites), and composite of Terra/Aqua 16 day using 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:
Short wave infrared 1 (SWIR1): 1.628-1.652 μm
Green: 0.545-0.565 μm

Technical Information

Feature
Detail
Extent
Global
Period of Record
2000-present
Spatial Resolution
500-1000m
Temporal Resolution
Daily
Data Summaries
max, min, mean, median, anomalies, trend and statistical significance, spatial and temporal aggregations, time series
Data Source(s)
National Aeronautics and Space Administration (NASA)
Data Formats
raster (geotiff), raster tile (tile ID), time series (.csv, .xls, .json, .geojson)
Sources

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.

End User License Agreement

View the End User License Agreement conditions

Your license is subject to your prior acceptance of either this Licensed Application End User License Agreement (“Standard EULA”), or a custom end user license agreement between you and the Application Provider (“Custom EULA”), if one is provided.

Interested in learning more?

Contact us to start the conversation.

Loading...