An Inverse Model for Sea Ice Thickness Retrieval Using Active Microwave Remote Sensing

Sea ice encompasses a large area within the polar region and greatly influences the earth’s climate system. One particular sea ice parameter of interest in understanding the dynamics of the sea ice cover and the heat exchange between the ocean and the atmosphere is its thickness. Due to this, there has been an increase in interest towards research in the polar region. Yet the harsh environment proves a great challenge to scientists doing research in those regions. The use of microwave remote sensing to retrieve physical data of the polar region, in particular sea ice thickness serves as a practical solution to the problem. In this paper, an RT-DMPACT Inverse Model to retrieve sea ice thickness from active microwave remote sensing data is presented. The inverse model is a combination of the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT-DMPACT) forward model and the Levenberg-Marquardt Optimization algorithm. The RT-DMPACT forward model is an improved forward model and is applied to generate the radar backscatter data, where the DMPACT is included to account for the close spacing effect among the scatterers within the medium. The Levenberg-Marquardt Optimization algorithm is then applied to improve on the set of input parameters until the sea ice thickness can be estimated. Data from ground truth measurements carried out in Ross Island, Antarctica, such as sea ice surface roughness and temperature, together with radar backscatter data extracted from purchased satellite images, are used as inputs to estimate the sea ice thickness in an area. The estimated sea ice thickness is then compared with the ground truth measurement data to verify its accuracy. The results from the simulation show promise towards the use of the RT-DMPACT inverse model to retrieve sea ice thickness from actual conditions in the polar region.