Surface water classification from GF-4 images using a time series water index

Abstract

ABSTRACTAlthough satellite remote sensing imagery is suitable for mapping and monitoring the surface water, the application of surface water classification techniques in the literatures is still constrained by low accuracy in various situations. As the GF-4 satellite is one of the few moderate spatial resolution geostationary orbit remote sensing satellites, the purpose of this study is to introduce a method that consistently improves the accuracy of surface water classification by utilizing the temporal characteristics of the dense time series of GF-4 images. A new time series water index called the spectrum and solar altitude angle water index (SSWI) is proposed based on the characteristics of the variation in the top of atmosphere (TOA) radiance with the change in solar altitude angle in the near-infrared (NIR) band of GF-4 images. The thresholding technique is applied to the SSWI map to automatically extract surface water. The performance of the SSWI method for surface water classification is validated and compared with the results of two widely used water classification methods, the Normalized Difference Water Index (NDWI) and maximum likelihood (ML) classifier, at four representative sites in different parts of China, including Beijing, Hubei, Tibet and Guangdong. The results show that at all four test sites, the classification accuracy of SSWI is significantly higher than that of NDWI and ML. Averaged over the four test sites, the total error of the SSWI for water bodies is only approximately 36.9% of that of the NDWI method and 23.5% of that of the ML classifier. In particular, the SSWI method shows outstanding performance in terms of distinguishing surface water from backgrounds that are usually sources of surface water classification errors, e.g. mountain shadows and low-albedo built-up areas. Therefore, the SSWI method can be used to classify surface water from GF-4 images and holds the potential to be a useful surface water classification technology for water resource studies and applications.