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USGS · 70220117

Augmented normalized difference water index for improved monitoring of surface water

Arash Modaresi Rad·Jason R. Kreitler·Mojitaba Sadegh
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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.

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Arash Modaresi Rad, Jason R. Kreitler, Mojitaba Sadegh. 2021. Augmented normalized difference water index for improved monitoring of surface water. https://doi.org/10.1016/j.envsoft.2021.105030

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