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Lake Urmia is located in the northwest of Iran and shared between the provinces of West Azarbaijan and East Azarbaijan. In the last two decades, there has been a considerable decline in the lake’s water level. Satellite altimetry (SA) together with the advanced precise orbital positioning system has reached a high accuracy in the measurement of the water level height, but increasing the accuracy of waveform retracking (WR) is a challenging issue. In this study, wavelet decomposition and convolutional neural network were used for the WR with 50%, 55%, and 60% training scenarios and the threshold method was used for the 1992–2019 period. The training of 55% has the best result with a ± 0.027 m root mean square error. The water level has decreased by approximately 7 m from 1994 to 2018 and its overall trend is downward. The proposed method has been able to increase the WR accuracy by up to 30%. The gravity recovery and climate experiment and the annual monitoring of the water level station have also been used for the SA verification, which have a significant correlation of 0.66 and 0.96 with SA, respectively.

Wavelet decomposition and deep learning of altimetry waveform retracking for Lake Urmia water level survey