Next Article in Journal
Time-Space Variability of Chlorophyll-a and Associated Physical Variables within the Region off Central-Southern Chile
Previous Article in Journal
Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model
Remote Sens. 2013, 5(11), 5530-5549; doi:10.3390/rs5115530
Article

A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

1
, 2,* , 3
, 4
, 1
, 1
, 1
 and 1
Received: 5 August 2013; in revised form: 15 October 2013 / Accepted: 16 October 2013 / Published: 28 October 2013
View Full-Text   |   Download PDF [3268 KB, uploaded 19 June 2014]   |   Browse Figures
Abstract: Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525–0.605 μm) and SWIR (Band 9: 1.550–1.750 μm) bands of the ALI sensor, namely NDWIA4,9, was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWIA4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy.
Keywords: remote sensing; image segmentation; land surface water mapping; Advanced Land Imager (ALI); normalized difference water index; Landsat remote sensing; image segmentation; land surface water mapping; Advanced Land Imager (ALI); normalized difference water index; Landsat
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Li, W.; Du, Z.; Ling, F.; Zhou, D.; Wang, H.; Gui, Y.; Sun, B.; Zhang, X. A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI. Remote Sens. 2013, 5, 5530-5549.

AMA Style

Li W, Du Z, Ling F, Zhou D, Wang H, Gui Y, Sun B, Zhang X. A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI. Remote Sensing. 2013; 5(11):5530-5549.

Chicago/Turabian Style

Li, Wenbo; Du, Zhiqiang; Ling, Feng; Zhou, Dongbo; Wang, Hailei; Gui, Yuanmiao; Sun, Bingyu; Zhang, Xiaoming. 2013. "A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI." Remote Sens. 5, no. 11: 5530-5549.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert