A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer

• Published in: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
• Main Authors: Picchiani M., Del Frate F., Sist M.
• Other Authors: Picchiani, M, Del Frate, F, Sist, M
• Format: Conference Object
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2018
• Subjects: Cloud detection; Neural Network; Sentinel-3; Settore ING-INF/02 - CAMPI ELETTROMAGNETICI; Sea ice
• Online Access: http://hdl.handle.net/2108/216365; https://doi.org/10.1109/IGARSS.2018.8517857; https://ieeexplore.ieee.org/document/8517857

A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene.