The Normalized Difference Water Index (NDWI) is a calculation used in remote sensing to monitor and measure the amount of moisture in vegetation and surface water bodies.
What is Normalized Difference Water Index (NDWI)?
Similar to normalized difference vegetation index (NDVI), NDWI is based on the concept that different surfaces on Earth reflect sunlight in unique ways. Water bodies and vegetation, for example, have distinctive reflection and absorption patterns in the visible and near-infrared spectra of light. By capturing and analyzing these patterns through satellite imagery, NDWI quantifies the presence and concentration of water in liquid and vapor forms both within plants, as well as water on the surface of the Earth.
What are applications of NDWI?
There are many benefits for calculating NDWI. Some examples of the application of NDWI are:
- Water Resource Management: NDWI is used to monitor water levels in rivers, lakes, and reservoirs, providing data essential for managing these resources sustainably. NDWI helps in tracking changes in water bodies’ extents over time, crucial for regions facing water scarcity or floods.
- Agriculture: Farmers and agricultural managers use NDWI to assess crop health and irrigation needs. Since healthy plants contain more water, NDWI can indicate stressed vegetation areas, guiding irrigation scheduling and improving water use efficiency.
- Ecology and Environmental Management: NDWI helps in monitoring wetlands and their health, detecting changes in ecosystems that could signal environmental issues such as drought or habitat degradation.
- Climate Change Studies: By tracking changes in surface water and vegetation moisture over time, NDWI provides valuable data for understanding the impacts of climate change on natural water cycles and ecosystems.
How is NDWI calculated for surface water?
Depending on whether NDWI is being calculated to measure water moisture in vegetation or water present on the surface of the Earth, there are two different calculations. Calculating water content in leaves of plants and trees uses near-infrared (NIR) and short-wave infrared (SWIR) satellite bands.
For determining water on the surface of the Earth, green and near-infrared (NIR) bands in satellite data are used. Therefore the formula for calculating surface water NDWI is:
In this equation, ‘Green’ refers to the reflectance in the green light band, and ‘NIR’ refers to the reflectance in the near-infrared band. The result is a value that ranges between -1 and 1, where higher values indicate higher moisture content or water presence. This calculation created values for each cell in a raster layer that then can be used to create a map that highlights areas of water and moisture, distinguishing them from other land cover types like vegetation or soil.
Using NDWI to map flooding
Not only is using NDWI a method by which to quantify changes in water on the surface of the Earth, when visualized, NDWI values can be used to map out the extent of flooding. Detecting areas of flooding on satellite imagery can be difficult to see in its entirety due to the flooded and non-flooded areas appearing muddy or dark green on the image.
As an example, compare the two images below showing flooding that happened during April of 2020 around the Red River that runs through Marshall County, Minnesota, and Walsh County, North Dakota.
The Red River is susceptible to overflow and flooding, especially after particularly wet autumn and winter seasons, when the spring thaw combines with the river’s geographical traits to significantly elevate water levels. The Red River flows north, towards Canada’s Lake Winnipeg before emptying into the Hudson Bay. Ice jams along the river’s route in the north form blockades that impede the flow of the river, causing waters to rise further south.
NASA researchers used NDWI to calculate the intensity of the flooding of the Red River from Landsat 8 satellite imagery. By classifying areas where water was present (blue) compared to farmland (green), the bottom image makes visually detecting both the extend and amount of flooded areas easier.
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3), 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., … & Qin, Y. (2017). Open surface water mapping algorithms: A comparison of water-related spectral indices and sensors. Water, 9(4), 256. https://doi.org/10.3390/w9040256