Sentinel-2 NDWI(SWIR1/Green)

    Quasi Global (83 degrees N, 56 degrees S latitude)
Spatial Resolution
Data Source(s)
    European Space Agency (ESA)
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 Water Index (NDWI) (SWIR1/Green) is a water index which provides information on the extent of small turbid ponds (Lacaux 2007) or larger water bodies. This product typically ranges from -0.25 to 1 where water is typically below 0 (Özelkan 2020).

NDWI (SWIR1/Green) is the normalized difference between the short wave near infrared 1 (SWIR1) and green bands following the equation (SWIR1-Green)/(SWIR1+Green) (Lacaux 2007; Allen et al., 2014). Water reflects Green and absorbs SWIR1 electromagnetic radiation while vegetation and soil absorbs Green and reflects SWIR1 electromagnetic radiation (Xu 2006). The result is a low NDWI over water and a high NDWI over land with high contrast.

Bands Used:
Green: 0.560 μm (S2A) / 0.559 μm (S2B)
Short Wave Infrared 2 (SWIR2): 2.2024 μm (S2A) / 2.1857 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)

Allen, R., Trezza, R., Tasumi, M., & Kjaersgaard, J. (2014). METRIC: Mapping Evapotranspiration at High Resolution Using Internalized Calibration Applications Manual for Landsat Satellite Imagery. V, 3.

Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., & Lafaye, M. (2007). Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74.

Özelkan, E. (2020). Water body detection analysis using NDWI indices derived from Landsat-8 OLI. Polish Journal of Environmental Studies, 29(2), 1759-1769.

Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 27(14), 3025-3033.

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