Augmented Normalized Difference Water Index for improved surface water monitoring. (June 2021)

• Record Type: Journal Article
• Authors: Rad, Arash Modaresi; Kreitler, Jason; Sadegh, Mojtaba
• Is Part Of: Environmental modelling & software. Volume 140(2021)
• Publication Date: 2021-06
• Subjects: Satellite water mapping -- Spectral index -- MNDWI -- AWEI -- WI -- Landsat; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105030
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Graphical abstract: Image 1 Highlights: A novel water mapping index is presented that improves classification accuracy. The index performs successfully under various climatic and land-surface conditions. Cloud-filtering algorithm is presented that increases the number of useable imagery. GEE App creates a 16-day time series of water surface areas at user-selected region.