CoAuthors

Pierre Thibaut (CLS, France); Duc Hoang (CLS, France); François Boy (CNES, France); Amandine Guillot (CNES, France); Nicolas Picot (CNES, France)

Since the launch of the SARAL/AltiKa mission on February 25th, 2013, altimeter measurements of excellent quality are acquired all over the globe for the first time in Ka-band. One of the main benefits of the Ka-band is to havea very low penetration length in the ice (unlike the Ku-band historically used by previous altimetry missions), which allows to significantly reduce measurements uncertainties of the sea ice topography. Flying on the Envisat orbit and providing measurements at 40 Hz, the exploitation of AltiKa waveforms on sea ice is of great interest.
Sea ice covered regions are characterized by a large number of different surfaces with a multitude of backscattering properties rapidly evolving with time. The backscattering properties from each of these surfaces (first year ice, multiyear ice, fast ice, leads, polynyas, etc.) result in rapid changes of the returned echo shape. In the framework of the PEACHI project (Prototype for Expertise on AltiKa, for Coastal, Hydrology and Ice funded by CNES) which aims at analyzing and improving AltiKa measurements, a neural network waveform processing is developed in order to classify the waveforms according to their geometric shapes. The analysis of the observed classes combined to their geographic distributions and their contributions to a complete sea ice processing is detailed in this study.