Retrieval of River Ice Thickness From C-Band PolSAR Data

Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images

In this paper we introduce a parameter for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric (CP) synthetic aperture radar (SAR) images. The parameter is denoted as the “CP ratio”. In model simulations we investigated the sensitivity of the CP ratio to the dielectric constant, ice thickness, ice surface roughness, and radar inci...

Retrieval of sea-ice thickness distribution in the Sea of Okhotsk from ALOS/PALSAR backscatter data

Although satellite data are useful for obtaining ice-thickness distribution for perennial sea ice or in stable thin-sea-ice areas, they are still largely an unresolved issue for the seasonal ice zone (SIZ). We address this problem using L-band synthetic aperture radar (SAR). In the SIZ, ice-thickness growth is closely related to deformation, so surface roughness is expected to correlate with ic...

Speckle Filtering of NASA/JPL POLSAR C- and L-band Data with Structural Feature and Scattering

On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, are key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave sensing, although the sea ...

Estimation of River Ice Thickness Using Artificial Neural Networks

The purpose of this study is to assess the ability of the artificial neural network (ANN) models in estimating river ice thickness using easy available climate data. A site specific ANN models were developed for two hydrometric stations at two rivers in Alberta (Canada). The ANN models were found to adequately estimate ice thickness. Ways to improve the performances of the ANN models are proposed.