Abstract

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

Highlights

  • Global climate change and increasing human activities have been causing large changes in the water bodies on the Earth’s surface [1,2]

  • Menarguez [43] put forward a new method by combining each of the three water indices (LSWI, modified normalized difference water index (mNDWI), and normalized difference water index (NDWI)) with enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI), and the results revealed that this integrated method was more sensitive to water bodies, especially the mixed water and vegetation pixels

  • The two lowest accuracies were from the the combinations of the tasseled cap wetness index (TCW)-based algorithm and Landsat 7 ETM+ data (TCW + Landsat 7 ETM+ for combinations of the TCW-based algorithm and Landsat 7 ETM+ data (TCW + Landsat 7 ETM+ for abbreviation) and the mNDWI + Landsat 7 ETM+; while the two highest accuracies were from the abbreviation) and the mNDWI + Landsat 7 ETM+; while the two highest accuracies were from the combinations of the NDWI + Landsat 8 Operational Land Imager (OLI) and the NDWI plus VI + Landsat 8 OLI

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Summary

Introduction

Global climate change and increasing human activities have been causing large changes in the water bodies on the Earth’s surface [1,2]. Those changes in surface water bodies have been substantially affecting agricultural and industrial production [3,4], as well as ecological and environmental security [5,6,7,8,9,10]. The information about the spatial distribution and area of water bodies is critical for regional economic development and environmental protection [11,12]. Pekel et al [24]

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