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Measuring surface water using remote sensing technology is an essential research topic in many research areas, including flood-related studies and water resource management. Recent advances in satellite remote sensing have provided more efficient ways of monitoring surface water from space. Several sensors, such as the Visible Infrared Imaging Radiometer Suite on board Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) and Operational Land Imager (OLI) on board Landsat 8, have begun to monitor earth surface in recent years. They have been continuously providing enormous remotely sensed images. Nevertheless, it has to be noted that tradeoffs between spatial and temporal resolutions of these images still exist. Medium- to high-resolution images, such as Landsat OLI, are typically available fortnightly or less often, which limits their applications for intensively and continuously monitoring flood inundation dynamics. Whereas coarse-resolution sensors, such as NPP-VIIRS, scan the earth's surface once or several times a day, but their coarse spatial resolution hampers the correct mapping of flooded areas. This study, therefore, aims to blend NPP-VIIRS and Landsat OLI images in order to gain high spatial resolution from Landsat OLI and high temporal resolution from NPP-VIIRS simultaneously. Two classic fusion models, namely the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were tested and evaluated. Particularly, the fusion results of both models were compared with the actual Landsat images in order to evaluate the accuracy of these fusion results. It is hoped that this study will enlighten other studies that require remotely sensed data with both high spatial and temporal resolutions.

Blending NPP-VIIRS and Landsat OLI images for flood inundation monitoring