The use of synthetic aperture radar (SAR) imagery is generally considered to be an effective method for detecting surface water. Among various supervised/unsupervised classification methods, a SAR-intensity-based histogram thresholding method is widely used to distinguish waterbodies from
land. A SAR texturebased automatic thresholding method is presented in this article. The use of texture images substantially enhances the contrast between water and land in intensity images. It also makes the method less sensitive to incidence angles than intensity-based methods. A modified Otsu
thresholding algorithm is applied to selected sub-images to determine the optimal threshold value. The sub-images were selected using k-means results to ensure a sufficient number of pixels for both water and land classes. This is critical for the Otsu algorithm being able to detect an optimal
threshold for a SAR image. The method is completely unsupervised and is suitable for large SAR image scenes. Tests of this method on a Radasat-2 image mosaicked from 8 QuadPol scenes covering the Spritiwood valley in Manitoba, Canada, show a substantial increase in land-water classification accuracy
over the commonly used SAR intensity thresholding method (kappa indices are 0.89 vs. 0.79). The method is less computationally intensive and requires less user interaction. It is therefore well suited for detecting waterbodies and monitoring their dynamic changes from a large SAR image scene in a
nearreal time environment).