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Abstract

Sea ice thickness (SIT) retrieval using Global Navi- gation Satellite System-Reflectometry (GNSS-R) is a challenging problem in sea ice remote sensing, especially for SITs over 1 m, which is still in the blank stage. In this article, a seamless stacking-based retrieval method for SIT is proposed, which reduces the root mean square error (RMSE) for thicknesses below 1 m while ensuring the accuracy of SIT retrieval for thicknesses above 1 m. Principal component analysis (PCA) was used to extract delayed Doppler map (DDM) features, while the scattering coefficient and incidence angle were calculated using TechDemoSat-1 (TDS-1) data. Sea ice salinity and temperature were derived from Soil Moisture and Ocean Salinity (SMOS) data and used as inputs to the model along with other features. The performance of four machine learning (ML) algorithms— decision tree (DT), K-nearest neighbors (KNNs), support vector regression (SVR), and random forest (RF)—was compared, and the stacking model was constructed using these four algorithms as the base learner to improve performance. Validation using SMOS data for thicknesses up to 1 m showed that the stacking algorithm significantly improved retrieval accuracy, reducing the RMSE from 7 to 0.4 cm and improving the correlation coefficient (r) from 0.94 to 0.99. For thicknesses greater than 1 m, validation using Cryosat-2 data also showed strong performance. In addition, the effect of sea ice parameters on retrieval accuracy and sources of error was analyzed