Name
Abstract | ||
|---|---|---|
Snow cover on sea ice plays an important role in the climate of the Polar Regions. Snow on sea ice reduces the heat exchange between the ocean and the atmosphere. The high albedo of snow increases the solar energy that is reflected back into the atmosphere, and the low thermal conductivity better isolates the interaction between the sea ice and the atmosphere. As sea ice and snow cover retreat, more energy will be absorbed into the ocean, and the warmer water will melt more sea ice (1). Better data on the extent and thickness of snow cover are therefore needed to understand the condition and future behavior of sea ice. The data under consideration in this work is what was collected in the phase I of the Arctic 2006 mission in the Alaskan coastal region. The radar altimetry data comes from the Radar Altimeter developed by Applied Physics Laboratory at JHU (2) that uses the delay-Doppler phase-monopulse concept and exploits coherent signal processing and differential phase measurements to achieve better performance with a smaller instrument than is possible from a conventional radar altimeter. The laser altimetry data comes from the Atmospheric Topographic Mapper 4 (ATM4) which is part of the Greenland ice sheet project. The two altimeters discussed above have a resolution of about 40 cm each. However, the snow thickness which is of interest is typically of the order of 3-10 cm. Measurement of snow thickness is still possible. The key is to use the Radar and Laser Altimetry data intelligently conjunctive. The fact that is exploited is the strongest signal for the radar altimeter is from the snow-ice interface while the laser altimeter gets its strongest signal from the upper layer of snow. In a broad sense, the difference would yield the snow thickness. The basic step in the estimation of the surface topography at a given point involves alignment of a particular number of waveforms collected to a common reference in time and summation in Fourier domain. In this work, the number of waveforms to be integrated has been chosen after careful consideration so that both signal-to-noise ratio and the resolution do not suffer. The GPS and INS data corresponding to the radar data have been used to correct for the aircraft height and attitude variations respectively. The correction has been verified to be accurate by running simulations on the ocean data which resulted in an almost flat/smooth topography as is expected. A phase correction has also been done for each waveform to allow beam steering for better focusing at any given point on the surface. The radar data has been calibrated with respect to the in situ data collected over a runway at the Wallops Flight Facility. Future work involves coming up with a simulator for the radar to verify the functionality of the Radar Altimeter and consequently, the usefulness of the data. This would involve simulation of the actual field conditions and the electromagnetic signal propagation through varying amounts of sea ice and snow (3). Most importantly, the laser and radar altimetry data will be used in conjunction and the data will be interpreted based on the simulator results to measure the snow thickness. Differences between the radar and laser height estimates will be compared with in situ measurements of snow thickness to verify and adjust the algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IGARSS.2008.4779920 | Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International |
Keywords | Field | DocType |
Global Positioning System,height measurement,inertial navigation,oceanographic techniques,radar signal processing,remote sensing by laser beam,remote sensing by radar,sea ice,snow,Beufort line,Chukchi line,Global Positioning system,Inertial Navigation System,conjunctive radar,data processing,laser altimetry,ocean-ice-atmosphere interactions,sea ice,snow thickness,Binary search algorithm,Delay/Doppler altimeter,Laser altimeter,Radar altimeter | Inertial navigation system,Radar,Altimeter,Computer science,Remote sensing,Laser,Lidar,Radar altimeter,Global Positioning System,Geodesy,Snow | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-2808-3 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Puthalapat, D. | 1 | 0 | 0.34 |
| Carlton J. Leuschen | 2 | 33 | 10.31 |
| T. Markus | 3 | 188 | 45.80 |
| D. J. Cavalieri | 4 | 120 | 30.95 |