Investigating the Effect of Lake Ice Properties on Multifrequency Backscatter Using the Snow Microwave Radiative Transfer Model

Abstract

Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.

Forward Modelling of SAR Backscatter during Lake Ice Melt Conditions using the Snow Microwave Radiative Transfer (SMRT) Model

“…SMRT provides a framework where different electromagnetic, microstructure, and interface modules can be used, allowing for more faithful modelling of real conditions. Results of a recent sensitivity analysis using SMRT were found to be consistent with the results of other lake ice modelling and satellite observations, supporting the crucial role of the ice-water interface (Murfitt et al, 2022(Murfitt et al, , 2023.…”

“…For example, models have not factored in the presence of snow, ignored roughness of different interfaces, or considered the ice column to be a single homogenous layer. Recently the snow microwave radiative transfer (SMRT) model was used to conduct sensitivity analysis for lake ice under dry conditions (Murfitt et al, 2022(Murfitt et al, , 2023. SMRT provides a framework where different electromagnetic, microstructure, and interface modules can be used, allowing for more faithful modelling of real conditions.…”

“…Previously mentioned lake ice studies using SMRT only evaluated changes in backscatter under dry conditions (Murfitt et al, 2022(Murfitt et al, , 2023. Therefore, experiments during wet conditions are an important next step in the application of this model for lake ice cover.…”

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

“…Sampling positions of GSL, Baker Lake, and Peipus Lake are listed in Table 2. It should be noted that in situ ice thick-ness data are often measured near the shore, where the lake water freezes earlier, and the ice thickness could be larger at the beginning of ice seasons (Murfitt et al, 2022;Mangilli et al, 2022). Data records for GSL and Baker Lake have been updated to 2016 and 2020, respectively.…”

“…Satellite altimeters were initially designed for monitoring ocean topography. Nevertheless, numerous studies have explored the potential of satellite altimetry in monitoring inland waters, such as river water levels and discharge, lake water levels and storage changes, glacier elevation changes and mass balance, and recently in LIT (Huang et al, 2019;Zakharova et al, 2021;Murfitt et al, 2022;Beckers et al, 2017;Zhang et al, 2021;Zhao et al, 2022;Li et al, 2022a;Huang et al, 2018;Li et al, 2019). Altimetry-based LIT can be derived from backscattering coefficients or radar waveforms.…”