Augmented Normalized Difference Water Index for Improved Surface Water Monitoring

Authors

Arash Modaresi Rad, Boise State University
Jason Kreitler, U.S. Geological Survey
Mojtaba Sadegh, Boise State UniversityFollow

Document Type

Article

Publication Date

6-2021

Abstract

We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR_1-2, to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI's performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe.

Publication Information

Rad, Arash Modaresi; Kreitler, Jason; and Sadegh, Mojtaba. (2021). "Augmented Normalized Difference Water Index for Improved Surface Water Monitoring". Environmental Modelling & Software, 140, 105030. https://doi.org/10.1016/j.envsoft.2021.105030