Wavelet‐transform based edge detection approach to derivation of snowmelt onset, end and duration from satellite passive microwave measurements

Abstract

We developed a new method for deriving the onset date, end date, duration and spatial extent of snowmelt using satellite passive microwave measurements. Our method exploits the fact that apparent edges are present on the brightness temperature (Tb ) time series curve corresponding to sharp and abrupt melt‐induced transitions of brightness temperature. Through a wavelet transform of daily Tb observations, our method identifies and tracks significant upward and downward edges on the Tb curves. Through variance analysis and bi‐modal Gaussian curve fitting, an optimal edge strength threshold is statistically determined to differentiate real snowmelt edges from weak edges caused by noisy perturbations and other non‐melt processes. Based on the principle of spatial autocorrelation, a neighbourhood operator is designed to detect and correct possible errors in the melt computations that are purely based on temporal analysis of individual Tb curves. We have implemented the method using C++ programming language and successfully applied it to Special Sensor Microwave/Imager (SSM/I) data collected in 2001–2002 over the Antarctic ice sheet. The computation results were evaluated through visual interpretation of brightness temperature time series and examination of historical near‐surface air temperature records.