Retrieving freeze/thaw-cycles using Machine Learning approach in Nunavik (Québec, Canada)

In the terrestrial cryosphere, freeze/thaw (FT) state transition plays an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes.

Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Many algorithms have been developed and evaluated over time in this scope. With the advancement of data science and artificial intelligence methods, the potential of better understanding the cryosphere is emerging.

This work is dedicated to exploring an effective approach to retrieve FT state based on microwave remote sensing data using machine learning methods, which is expected to fill in some hidden blind spots in the deterministic algorithms. Time series of remote sensing data will be created as training data. In the initial stage, the work aims to test the feasibility and establish the basic neural network based on fewer training factors. In the advanced stage, we will improve the model in terms of structure, such as adding more complex dense layers and testing optimizers, and in terms of discipline, such as introducing more influencing factors for training. Related parameters, for example, land cover types, will be included in the analysis to improve the method and understanding of FT-related processes.