Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients

Abstract

Abstract. Lake ice, serving as a sensitive indicator of climate change, is an important regulator of regional hydroclimate and lake ecosystems. For ice-covered lakes, traditional satellite altimetry-based water level estimation is often subject to winter anomalies that are closely related to the thickening of lake ice. Despite recent efforts made to exploit altimetry data to resolve the two interrelated variables, i.e., lake ice thickness (LIT) and the water level of ice-covered lakes, several important issues remain unsolved, including the inability to estimate LIT with altimetric backscattering coefficients in ungauged lakes due to the dependence on in situ LIT data. It is still unclear what role lake surface snow plays in the retrieval of LIT and water levels in ice-covered lakes with altimetry data. Here we developed a novel method to estimate lake ice thickness by combining altimetric waveforms and backscattering coefficients without using in situ LIT data. To overcome complicated initial LIT conditions and better represent thick ice conditions, a logarithmic regression model was developed to transform backscattering coefficients into LIT. We investigated differential impact of lake surface snow on estimating water levels for ice-covered lakes when different threshold retracking methods are used. The developed LIT estimation method, validated against in situ data and cross-validated against modeled LIT, shows an accuracy of ∼ 0.2 m and is effective at detecting thin ice that cannot be retrieved by altimetric waveforms. We also improved the estimation of water levels for ice-covered lakes with a strategy of merging lake water levels derived from different threshold methods. This study facilitates a better interpretation of satellite altimetry signals from ice-covered lakes and provides opportunities for a wider application of altimetry data to the cryosphere.

Lake Ice Thickness Retrieval Method with ICESat-2-Assisted CyroSat-2 Echo Peak Selection

“…In this section, the proposed method is compared with two other methods. One method utilizes the backscattering coefficient for LIT retrieval [15], whereas the other uses a fixedrange bin for LIT retrieval [16]. By comparing the two methods, we evaluate the performance of the proposed method for LIT retrieval.…”

“…Therefore, it is possible to establish a regression relationship between the radar backscattering and the ice thickness, thereby enabling an accurate ice thickness retrieval [3]. Li et al [15] combined radar altimetry waveforms and backscattering coefficients to estimate the LIT, which utilized a logarithmic regression model to establish the relationship between the backscattering coefficient and ice thickness; the accuracy of estimating the LIT was approximately 0.2 m. However, recent studies have shown that the backscattering coefficient is mainly affected by the roughness of the lake ice surface, and an increase in the LIT does not have a significant effect on the backscattering coefficient of lake ice [8]; therefore, the use of the backscattering coefficient in LIT retrieval needs to be verified for the reasonableness of the model. The thickness retrieval by the altimetry radar waveforms is a rigorous physical process; the use of altimetry radar echo bimodal characteristics for ice thickness retrieval was first proposed by Beckers et al [16].…”

Improving the Estimation of Lake Ice Thickness with High Resolution Radar Altimetry Data

“…Other recent studies [9][10][11][12] have exploited radar waveforms for LIT estimation from different missions (both Low Resolution Mode or LRM, and SAR mode), mainly focusing on the estimation of the bias introduced by the presence of ice in winter on lake water level (LWL) measurements, more than on obtaining an accurate LIT estimation. Additionally, the studies mentioned above, make use of empirical methods and/or retrackers that cannot correctly account for the specific waveform shape over ice covered lakes and also depend on threshold settings that are not accurate enough to follow the LIT evolution, in particular at the seasonal transitions when the LIT signature is less discernible in the radar waveforms, leading to biased and sub-optimal LIT estimation.…”

A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms

“…However, the backscatter extracted from SAR images changes complexly during the evolution of lake ice, and its change mechanism is still controversial. The current altimetric satellites used for lake ice monitoring are the Jason series, CryoSat-2, and Sentinl-3A [ 28 , 43 , 44 , 45 ]. Compared to other methods, radar altimeters can not only estimate water levels but also be used to detect lake ice.…”