Estimation of Antarctic sea ice properties using surface and space borne data.

摘要

Sea ice is a fundamental component of the Earth's systems that cannot be ignored in the large scale environmental predictions of future climate conditions. Sea ice is a complex material and has major influences on global climate with its large maximum extent and seasonal change. In this research, remote sensing validation based on comparisons with surface based data has been done for quantitative monitoring of the ice properties. Various satellite products consisting of passive microwave, active microwave, and high resolution visible imagery were used and compared with in-situ measurements collected during scientific Antarctic cruises, conducted during International Polar Year (IPY) 2007--2008. This data used to provide a quantifiable method for observing sea ice, from all regions of the Antarctic sea ice zone to develop relationships that test existing remote sensing algorithms, evaluate alternative algorithms and provide error estimates on sea ice thickness derived from existing algorithms.Chapter 2 presents the comparison of ice extent/ice edge data from the NIC and the AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) passive microwave products using the Antarctic Sea Ice Process and Climate (ASPeCt) ship observations from the Oden expedition in December 2006 as ground truth to verify the two products during Antarctic summer. Ice edge location comparison has also been made between the two data sets, ship ice observations and NIC daily ice edge products. NIC analyses rely more heavily on high resolution satellite imagery such as active radar and visible imagery when visibility (clouds) allows. From these comparisons, a quantitative estimate of the differences in summer ice extent between the two remotely obtained products, AMSR-E and NIC ice edge, over the larger West Antarctic sea ice zone, has been obtained.Chapter 3 evaluates the comparison of ASPeCt ship based observations (conducted during Sea Ice Mass Balance in the Antarctic (SIMBA) 2007 Antarctic cruise) with coincident satellite active and passive microwave data. We combined visual ship-based observations of sea-ice and snow properties during SIMBA with coincident active and passive microwave satellite data with the aims to (a) derive typical radar backscatter ranges for observed sea-ice types and ice type mixtures, (b) improve our knowledge about the radar backscatter of different ice types in the Bellingshausen Sea at early-middle spring, (c) interpret AMSR-E snow depth over these ice types, and (d) identify the potential of the investigated active microwave signatures for a synergy with AMSR-E data to eventually improve the snow depth retrieval.Chapter 4 presents the validation of remote sensing measurements of ice extent and concentration with ASPeCt ship-based ice observations, conducted during the SIMBA and the Sea Ice Physics and Ecosystem eXperiment (SIPEX) International Polar Year (IPY) cruises (Sept--Oct 2007). First, the total sea ice cover around the entire continent was determined for 2007--2008 from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) passive microwave and National Ice Center (NIC) charts. Second, Antarctic Sea Ice Processes and Climate (ASPeCt) ship observations from the SIMBA and SIPEX expeditions in the austral end of winter--beginning of spring 2007 are used as ground truth to verify the AMSR-E sea ice concentration product provided by both the Enhanced NASA Team Algorithm (NT2) and Bootstrap Basic Algorithm (BBA).Chapter 5 presents supplemental analysis related to the baseline thickness of Antarctic sea ice on a circumpolar basis from field measurements. In this part, our objectives were (1) Develop statistical relationships between surface elevation (snow freeboard), ice elevation (ice freeboard) and mean sea ice thickness using previous and newly obtained Antarctic sea ice profiles and examine these relationships for any consistent regional trends, (2) Derive sea ice thickness from profile elevations, using buoyancy equation, to determine error estimates compared to measured thickness compare error estimates between the thicknesses derived using statistical relationships (Objective 1) and buoyancy theory where the additional term for the density of the slush layer is needed, when surfaces are flooded from snow loading. (Abstract shortened by UMI.)