Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Abstract

Abstract:Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. OPEN ACCESSRemote Sens. 2015, 7 13808The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

Production of Global Land Cover Data – GLCNMO2013

“…The water map in 2013 was produced using Superfine Water Index (SWI) developed by Sharma, Tateishi, Hara, & Nguyen (2015). The SWI were calculated from MODIS data as follows:…”

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

“…The use of two degrees of confidence levels (95% and 90%) is justified, which indicates that 95% and 90% of the intervals obtained from such classification will contain the true category. One-side lower/upper confidence limits are separately performed for the water and non-water normal distributions, which can be calculated as Equation (4). In particular, 1.96 and 2.235 are respectively the 0.975 and 0.95 one-side quantiles of the normal distribution.…”

“…Remote sensing provides repetitive mapping in time-and cost-saving modes. Various remote sensing data have been utilised in water surface mapping, including Synthetic Aperture Radar (SAR) satellites [1,2], LiDAR data [3], and various spatial resolution optical satellite images ranging from low-resolution [4,5] to very high-resolution imagery [6,7].…”

MODIS-based multi-parametric platform for mapping of flood affected areas. Case study: 2006 Danube extreme flood in Romania

“…• The Superfine Water Index (SWI) is derived by replacing the "Green" in the NDWI with the 'Saturation (Sat)' obtained from the HSV (Hue-Saturation-Value) transformation of the RGB composite made up of red (R), green (G), and blue (B) bands of the MODIS data (Sharma et al, 2015). The SWI provides very high contrast between the surface water and nonwater cover types including the snow and vegetation.…”