Lake Level Estimation Based on CryoSat-2 SAR Altimetry and Multi-Looked Waveform Classification

In this study, reliable water levels for four lakes are estimated based on an innovative processing strategy using a semi-automatic CryoSat-2 Synthetic Aperture Radar (SAR) multi-looked waveform classification. The selection of valid water returns is an essential step in inland altimetry applications. In order to identify reliable observations allowing for an accurate retracking, an unsupervised classification method for CryoSat-2 SAR multi-looked waveforms has been developed based on the k-mean algorithm. With this approach, changes in the water surface extent or surrounding inundation areas can be taken into account. In addition, a modified version of the Improved Threshold Retracker is developed in order to obtain optimal results for the lake heights. The used method is based on the identification of the optimal sub-waveform by employing height thresholds. The validation of the derived CryoSat-2 SAR time series with in-situ gauging data yields root mean square (RMS) differences between 3 and 90 cm for the different lakes. Compared to modeled CryoSat-2 water heights derived according to the approach used in the AltWater database our water level time series are slightly improved in terms of RMS accuracy but they contain more gaps due to the lack of reliable observations. In comparison with classical radar altimeter missions such as Envisat or Jason-2, the SAR-based time series show smaller RMS differences for the small lakes but larger RMS differences for the large lakes covered by multiple repeat missions. The presented innovative processing strategy can be easily adopted to other satellite altimetry SAR data such as from the new Sentinel-3 mission.