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Water 2017, 9(11), 834;

Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
CSIRO Land and Water, Canberra ACT 2601, Australia
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing 100048, China
This is an extended work of the following conference paper: Huang, C.; Chen, Y.; Zhang, S. Mapping Lakewater area at sub-pixel scale using Suomi NPP-VIIRS imagery. In Proceedings of the 1st International Electronic Conference on Water Sciences, 15–29 November 2016; Sciforum Electronic Conference Series, Volume 1, 2016, f001; doi:10.3390/ecws-1-f001.
Author to whom correspondence should be addressed.
Received: 29 July 2017 / Revised: 26 October 2017 / Accepted: 27 October 2017 / Published: 30 October 2017
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Capturing the dynamics of a lake-water area using remotely sensed images has always been an essential task. Most of the fine spatial resolution data are unsuitable for this purpose because of their low temporal resolution and limited scene coverage. A Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP–VIIRS) is a newly-available and appropriate sensor for monitoring large lakes due to its frequent revisits and wide swath (more than 3000 km). However, it provides visible and infrared images at relatively coarse spatial resolutions, which would sometimes hamper the accurate mapping of lake shorelines. This study, therefore, proposes a two-step downscaling method that combines spectral unmixing and subpixel mapping to produce a finer resolution lake map from NPP–VIIRS imagery, which is then applied to delineate the shorelines of five plateau lakes in Yunnan Province, as well as the shoreline dynamics of Poyang Lake at three separate times. A newly published global water dynamic dataset is employed in this study to improve the downscaling method. Results suggest that the proposed method can generate a finer resolution lake map that exhibits more details of the shoreline than hard classification. The downscaling results of the Suomi NPP–VIIRS generally achieve higher than 75% accuracy, while the downscaling results of a Landsat-simulated fraction map could have accuracy higher than 85%. This reveals that errors and uncertainties exist in both procedures, but mainly come from the spectral unmixing procedure which retrieves water fractions from NPP–VIIRS data. View Full-Text
Keywords: linear spectral unmixing; subpixel mapping; surface water dynamics; lake shoreline mapping; Poyang Lake linear spectral unmixing; subpixel mapping; surface water dynamics; lake shoreline mapping; Poyang Lake

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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).

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Huang, C.; Chen, Y.; Zhang, S.; Li, L.; Shi, K.; Liu, R. Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping. Water 2017, 9, 834.

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