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+## Challenges with deriving sea ice roughness from SAR imagery using deep learning 
+### *Anton Korosov*
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+This study presents a novel approach for retrieving sea ice roughness using Synthetic Aperture Radar (SAR) imagery, enhanced by convolutional neural networks (CNNs). 
+By training the CNN with altimetry-derived roughness as the target, the method accurately captures surface features crucial for sea ice monitoring. 
+Our model leverages the spatial resolution of SAR and the physical measurements from altimetry to improve roughness estimation across diverse ice conditions. 
+Results demonstrate abiility to distinguishing between smooth and rough ice. 
+This technique is expected to provide inputs for sea ice model with surface drag parametrisation and improve sea ice dynamics simulations.
+
+[back to the Workshop page](https://nansencenter.github.io/superice-nersc/workshop/)