Sea ice thickness from SMOS measurements. The retrieval is based in a hybrid approach, combining the Burke emission model (Burke et al., 1979) with a machine learning algorithm. The emission model is
The SMOS Level 3 Sea Ice Thickness product, in NetCDF format, provides daily estimations of SMOS-retrieved sea ice thickness (and its uncertainty) at the edge of the Arctic Ocean during the October-Ap
The SMOS-CryoSat merged Sea Ice Thickness Level 4 product, in NetCDF format, is based on estimates from both the MIRAS and the SIRAL instruments, with a significant reduction in the relative uncertain
Sea ice thickness from SMOS measurements. The retrieval is based in a hybrid approach, combining the Burke emission model (Burke et al., 1979) with a machine learning algorithm. The emission model is
The SMOS Level 3 Sea Ice Thickness product, in NetCDF format, provides daily estimations of SMOS-retrieved sea ice thickness (and its uncertainty) at the edge of the Arctic Ocean during the October-Ap
