Augmented normalized difference water index for improved monitoring of surface water
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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 SWIR1-2, 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 type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Augmented normalized difference water index for improved monitoring of surface water |
| Series title | Environmental Modeling and Software |
| DOI | 10.1016/j.envsoft.2021.105030 |
| Volume | 140 |
| Year Published | 2021 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Western Geographic Science Center |
| Description | 105030, 15 p. |
| Google Analytic Metrics | Metrics page |