Search (over 200 pages above & below)

Crop Agriculture

Vegetation & Crop Water Content Mapping 

Related Page: About Crop Water Content Mapping

Free Landsat imagery can be used along with free GIS software (or other GIS Software, such as ArcGIS) to map the relative amount of water in many different types of vegetation (including crops or natural vegetation) by calculating the Normalized Difference Water Index (NDWI). The index values can also be used as the basis to map the quantity of water in corn or soybeans based on the work of Jackson et al. (2004). The slideshow below illustrates corn and soybean water content mapping (see the link above and information below for more details).

Images transition every 4 seconds (or click arrows or numbers); description appears below graphic  


The NDWI index was applied to Landsat by Jackson et al. (2004) and was written as Equation 2 as follows:

Normalized Difference Water Equation (NDWI)

For Jackson et al.'s (2004) study vegetation water content (VWC) data was taken from a previous Iowa study where biomass was removed then weighed wet and dry to determine water. It was found that NDWI was a better than NDVI as sensing water content. The above sliceshow example is based on apparent reflectance. The following equations were developed by Jackson et al. (2004) (based on reflectance derived from atmospheric correction as described in Jackson et al. [2004] where apparent reflectance was first calculated then was further refined) and are to be applied to estimated vegetation water content with Landsat reflectance-based NDWI:

Vegatation Water Content equation based on NDWI


Testing results from Jackson et al. (2004) of the NDWI equation compared to NDVI are as follows:

Results of the NDWI equation compared to NDVI for predicting vegetation water content



Jackson, T.J., Chen, D., Cosha, M., Lia, F., Anderson, M., Walthalla, C., Doriaswamya, P., and E.R. Hunt. 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment 92: pp. 475-482.