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Open AccessArticle

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

1
College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
2
Commonwealth Scientific and Industrial Research Organization (CSIRO) Land and Water, Canberra, ACT 2600, Australia
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
4
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China
5
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
Remote Sens. 2016, 8(8), 631; https://doi.org/10.3390/rs8080631
Received: 21 May 2016 / Revised: 6 July 2016 / Accepted: 27 July 2016 / Published: 29 July 2016
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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MDPI and ACS Style

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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