Next Article in Journal
Correction: Hammer, J., et al. Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index. Remote Sens. 2015, 7, 9070–9090
Previous Article in Journal
Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(10), 13807-13841; doi:10.3390/rs71013807

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

1
Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
2
Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan
3
Remote Sensing Application Department, Space Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 10000, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Senay, Magaly Koch and Prasad S. Thenkabail
Received: 15 August 2015 / Revised: 11 October 2015 / Accepted: 13 October 2015 / Published: 21 October 2015

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. The 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. View Full-Text
Keywords: water cover; random walker algorithm; MODIS; Superfine Water Index (SWI); HSV color model; global mapping water cover; random walker algorithm; MODIS; Superfine Water Index (SWI); HSV color model; global mapping
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Sharma, R.C.; Tateishi, R.; Hara, K.; Nguyen, L.V. Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data. Remote Sens. 2015, 7, 13807-13841.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top