Object-based automatic terrain shadow removal from SNPP/VIIRS flood maps

Abstract

Terrain shadow is a big challenge to land products such as flood extent and snow cover from moderate-resolution optical satellite data. Because terrain shadows share similar spectral features with floodwaters, they can be easily detected as floodwaters by flood detection algorithms based on spectral features in visible, near-infrared, and short-wave infrared channels, which decreases the accuracy of flood detection substantially. However, because terrain shadows appear in mountainous areas with large surface roughness while floodwaters accumulate in low-lying areas with small surface roughness, analysis on surface roughness between terrain shadows and floodwaters can be very effective to distinguish one from the other. Root-mean-square height, internal height difference, and external height difference are used as principal quantitative surface roughness parameters in this study and calculated upon water objects that are clustered from a group of adjacent water pixels. This object-based method is applied in terrain shadow removal from SNPP/VIIRS (Suomi National Polar Orbit Partnership/Visible Infrared Imager Radiometer Suite) near-real-time flood maps and shows promising results according to the tests with 10,000+ VIIRS granules across global areas. Quantitative evaluation in the northwest of the USA also indicates that more than 99% terrain shadow pixels could be removed from VIIRS flood maps by this method, which significantly improves the accuracy of near-real-time flood detection from SNPP/VIIRS imagery.