Assessment of Machine Learning-Driven Retrievals of Arctic Sea Ice Thickness from L-Band Radiometry Remote Sensing

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite. Alongside the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation with in situ data from the BGEP moorings and the IRO2/ESA SMOSice campaign shows that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing model complexity and suggest that simpler, adaptable methods such as RF offer a promising basis for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for the development of upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR).