Spatio-Temporal Coherence Based Technique for Near-Real Time Sea-Ice Identification from Scatterometer Data

Abstract

The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5% (during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data.

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

“…One is based on the microwave backscattering characteristics of sea ice, including the backscattering powers of the horizontal and vertical polarization. Sea ice and sea water recognition was first carried out using C-band data [12,18,19], and then the Ku-band was demonstrated to be applicable for recognizing sea ice and sea water [20,21]. Recognition accuracies achieved 90% [22].…”

Advance in Sea Ice Studies in India

“…Discrimination between sea-ice and ocean is ambiguous in the Scaterrometer observations under the high wind and/ or thin/scattered ice conditions. Oza et al (2011b) developed an algorithm to distinguish ocean winds from the sea ice in both the hemispheres using spatiotemporal coherence techniques, in addition to backscatter coefficient and the Active Polarization Ratio (API). These authors found that the threshold Antarctic sea ice thickness has also been retrieved by using Ice Cloud and land Elevation satellite (ICEsat) elevation measurements to retrieve sea-ice (with snow cover) freeboard in the Weddell Sea Antarctica (Kern and Spreen, 2015).…”

“…Discrimination between sea-ice and ocean is ambiguous in the Scaterrometer observations under the high wind and/ or thin/scattered ice conditions. Oza et al (2011b) developed an algorithm to distinguish ocean winds from the sea ice in both the hemispheres using spatiotemporal coherence techniques, in addition to backscatter coefficient and the Active Polarization Ratio (API). These authors found that the threshold…”

Exploring the Antarctic

“…Automatic identification of sea-ice edge and its validation using enhanced resolution QuikSCAT data has been carried out byHaarpaintner et al (2004). As mentioned earlier, Scatterometer data has shown its potential in delineating the sea ice extent(Oza et al, 2010d;Rivas and Stoffelen, 2011;Rivas et al, 2018;Haarpaintner et al, 2004) Li et al (2016). have demonstrated the potential of Chinese HY-2A Scatterometer for sea ice monitoring Lindell and Long (2016).…”

“…An attempt was also made to estimate the sea ice concentration using OSCAT (Ku-band) and ASCAT (C-band) Scatterometer data(Singh et al 2014c). In the study carried out byOza et al (2010d), various values of Active Polarisation Ratio (APR) ranging from (-0.01) to (-0.04) at 0.005 interval were used to arrive at the optimum APR threshold for sea ice detection in the Arctic and the Antarctic regions. The recent work carried out by SAC using SCATSAT-1 Scatterometer is shown in Figure3.4 Oza et al (2009a;2009c, 2010c.…”