Lake Ice Thickness Estimation Using Calibrated Enhanced-Resolution Passive Microwave Data

Lake ice thickness (LIT) significantly impacts water, climate, and socioeconomic activities. Due to lack of observations, estimating the spatiotemporal variations in LIT is still a challenging task. Brightness temperature (TB) measured by microwave radiometers correlates well with LIT at certain frequencies, which potentially enables global and daily estimations of LIT by spaceborne passive microwave (PMW) remote sensing over decades. In this study, a generalized empirical linear regression algorithm to estimate LIT using PMW is proposed using TB as the sole data source. The method is based on statistical analysis of TB over 18 lakes. The analysis revealed a large variability of linear coefficients relating TB to LIT; however, the coefficients could be tied to TB at the freeze-up end (FUE). When compared with LITs derived from in situ measurements, radar altimetry, and published LIT data, the root mean square errors (RMSEs) were 0.19, 0.16, and 0.15 m, respectively. This method was then used to estimate the LITs for 96 lakes in the Northern Hemisphere from 2002 to 2011, which was proven to be applicable for long-term LIT mapping and monitoring for large lakes, demonstrating improved generalizability.