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 (T_b ) time series curve corresponding to sharp and abrupt melt‐induced transitions of brightness temperature. Through a wavelet transform of daily T_b observations, our method identifies and tracks significant upward and downward edges on the T_b 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 T_b 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.

Acknowledgement

This work was supported by the NASA grant NAG5‐10112 and the NSF grant No. 0126149. The authors wish to thank the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado for providing the SSM/I EASE‐Grid brightness temperature data for this research project.