Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping

• Main Authors: Chang Huang, Yun Chen, Shiqiang Zhang, Linyi Li, Kaifang Shi, Rui Liu
• Format: Article
• Language: English
• Published: MDPI AG 2017-10-01
• Series: Water
• Subjects: linear spectral unmixing; subpixel mapping; surface water dynamics; lake shoreline mapping; Poyang Lake
• Online Access: https://www.mdpi.com/2073-4441/9/11/834

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.