Saline ice thickness retrieval under diurnal thermal cycling conditions

An inverse scattering algorithm is presented that reconstructs ice growth under thermal cycling conditions by using time-series active microwave measurements. The algorithm uses a direct scattering model consisting of a physically based electromagnetic model that accounts for thermal and electromagnetic properties of ice and combined volume and surface scattering effects as well as a one-dimensional (1D) thermodynamic model of saline ice growth that includes thermal interactions with the atmosphere. The combined thermodynamic-electromagnetic scattering model is applied to interpret the United States Army Cold Regions Research and Engineering Laboratory, Hanover, NH, 1994 experimental observations (CRRELEX'94) on both the ice growth and the diurnal cycles in C-band polarimetric backscatter. The crucial part of the inversion algorithm is the use of sequentially measured radar data together with the direct scattering model to retrieve the sea ice parameters. The algorithm was applied to CRRELEX'94 data and successfully reconstructed the evolution of ice growth under a thermal cycling environment. This work shows that the inversion algorithm using time-series data offers a distinct advantage over algorithms using individual microwave data set.

[1]  Wilford F. Weeks,et al.  Equations for Determining the Gas and Brine Volumes in Sea-Ice Samples , 1982, Journal of Glaciology.

[2]  Son V. Nghiem,et al.  Thin saline ice thickness retrieval using time-series C-band polarimetric radar measurements , 1998, IEEE Trans. Geosci. Remote. Sens..

[3]  Frank D. Carsey,et al.  Microwave Remote Sensing of Sea Ice , 1992 .

[4]  Stephen F. Ackley,et al.  The Growth, Structure, and Properties of Sea Ice , 1982 .

[5]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6]  Eric Rignot,et al.  Identification of sea ice types in spaceborne synthetic aperture radar data , 1992 .

[7]  C. Swift,et al.  An improved model for the dielectric constant of sea water at microwave frequencies , 1977, IEEE Journal of Oceanic Engineering.

[8]  Simon Yueh,et al.  Evolution in polarimetric signatures of thin saline ice under constant growth , 1997 .

[9]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[10]  J. Kong,et al.  Theory of microwave remote sensing , 1985 .

[11]  Gary A. Maykut,et al.  Large‐scale heat exchange and ice production in the central Arctic , 1982 .

[12]  A. Stogryn,et al.  The dielectric properties of brine in sea ice at microwave frequencies , 1985 .

[13]  Simon Yueh,et al.  Diurnal thermal cycling effects on microwave signatures of thin sea ice , 1998, IEEE Trans. Geosci. Remote. Sens..

[14]  Richard K. Moore,et al.  Microwave Remote Sensing , 1999 .

[15]  Simon Yueh,et al.  Retrieval of thin ice thickness from multifrequency polarimetric SAR data , 1995 .

[16]  A. Jordan,et al.  Electromagnetic remote sensing of sea ice , 1994 .

[17]  Gary A. Maykut,et al.  The Surface Heat and Mass Balance , 1986 .

[18]  Son V. Nghiem,et al.  Polarimetric signatures of sea ice: 2. Experimental observations , 1995 .

[19]  Ian Joughin,et al.  On the response of polarimetric synthetic aperture radar signatures at 24-cm wavelength to sea ice thickness in Arctic leads , 1995 .

[20]  G. Maykut Energy exchange over young sea ice in the central Arctic , 1978 .

[21]  Hong Tat Ewe,et al.  Inversion algorithms for remote sensing of sea ice , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Shoichiro Fukusako,et al.  Thermophysical properties of ice, snow, and sea ice , 1990 .

[23]  W. F. Weeks,et al.  Numerical simulations of the profile properties of undeformed first‐year sea ice during the growth season , 1988 .