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Remote Sens. 2016, 8(8), 631;

Surface Water Mapping from Suomi NPP-VIIRS Imagery at 30 m Resolution via Blending with Landsat Data

College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Commonwealth Scientific and Industrial Research Organization (CSIRO) Land and Water, Canberra, ACT 2600, Australia
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China
Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing 100048, China
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 21 May 2016 / Revised: 6 July 2016 / Accepted: 27 July 2016 / Published: 29 July 2016
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Monitoring the dynamics of surface water using remotely sensed data generally requires both high spatial and high temporal resolutions. One effective and popular approach for achieving this is image fusion. This study adopts a widely accepted fusion model, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), for blending the newly available coarse-resolution Suomi NPP-VIIRS data with Landsat data in order to derive water maps at 30 m resolution. The Pan-sharpening technique was applied to preprocessing NPP-VIIRS data to achieve a higher-resolution before blending. The modified Normalized Difference Water Index (mNDWI) was employed for mapping surface water area. Two fusion alternatives, blend-then-index (BI) or index-then-blend (IB), were comparatively analyzed against a Landsat derived water map. A case study of mapping Poyang Lake in China, where water distribution pattern is complex and the water body changes frequently and drastically, was conducted. It has been revealed that the IB method derives more accurate results with less computation time than the BI method. The BI method generally underestimates water distribution, especially when the water area expands radically. The study has demonstrated the feasibility of blending NPP-VIIRS with Landsat for achieving surface water mapping at both high spatial and high temporal resolutions. It suggests that IB is superior to BI for water mapping in terms of efficiency and accuracy. The finding of this study also has important reference values for other blending works, such as image blending for vegetation cover monitoring. View Full-Text
Keywords: spatial and temporal fusion; mNDWI; OTSU thresholding; Pan-sharpening; ESTARFM spatial and temporal fusion; mNDWI; OTSU thresholding; Pan-sharpening; ESTARFM

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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. Surface Water Mapping from Suomi NPP-VIIRS Imagery at 30 m Resolution via Blending with Landsat Data. Remote Sens. 2016, 8, 631.

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