Permanent Water Bodies Mapping in the Mekong River Delta Using Seasonal Time Series C-band SAR Data - Duy, Nguyễn Bá; Giang, Trần Thị Hương (VNU Journal of Science: Earth and Environmental Sciences, 2015)
Microwave remote sensing or SAR (Synthetic Aperture Radar) data has been employed extensively to map open water bodies and to monitor flood extents, where cloud cover often prohibits the use of satellite sensors operating at other wavelengths. Where total inundation occurs, a low backscatter return is expected due to the specular reflection of SAR signal on the water surface. However, low local incidence angle and wind induced waves can cause a roughening of the water surface which result in a high return signal. It is also mean that the temporal variability (TV) of the backscatter from water bodies is higher than other land surfaces. The Mekong River Delta is a region with very long wet season (starting in May and lasting until October-November), resulting in almost crop fields also has low backscatter returns. Where such conditions occur adjacent to open water, this can make the separation of water and land problematic using SAR data. In this paper, we use seasonal time series C-band SAR data (dry season), we also examine how the variability in radar backscatter with incidence angle may be used to differentiate water from land overcoming. We carry out regression over multiple sets of seasonal time series data, determined by a moving window encompassing consecutively-acquired ENVISAT ASAR Wide Swath Mode data, to derive three backscatter model parameters: the slope β of a linear model ﬁtting backscatter against local incidence angle; the backscatter normalized at 50° using the linear model coefficients s o (50 o ), and the minimum backscatter (MiB) from time series data after normalized. A comparison of the three parameters (β, TV and MiB) shows that MiB in combination with TV provides the most robust means to segregate water from land by a simple thresholding algorithm. Keywords: Water bodies mapping, SAR, time series analysis.