Utilisation of CryoSat-2 SAR altimeter in operational ice charting

Abstract

Abstract. We present methods to utilise CryoSat-2 (CS-2) synthetic aperture radar (SAR) mode data in operational ice charting. We compare CS-2 data qualitatively to SAR mosaics over the Barents and Kara seas. Furthermore, we compare the CS-2 to archived operational ice charts. We present distributions of four CS-2 waveform parameters for different ice types as presented in the ice charts. We go on to present an automatic classification method for CS-2 data which, after training with operational ice charts, is capable of determining open ocean from ice with a hit rate of > 90 %. The training data are dynamically updated every 5 days using the most recent 15 days of CS-2 data and operative ice charts. This helps the adaption of the classifier to the evolving ice/snow conditions throughout winter. The classifier is also capable of detecting three different ice classes (thin and thick firstyear ice as well as old ice) with success rates good enough for the output to be usable to support operational ice charting. Finally, we present a near-real-time CS-2 product just plotting the waveform characteristics and conclude that even such a simple product is usable for some of the needs of ice charting.

Near-real-time Arctic sea ice thickness and volume from CryoSat-2

“…Although of coarser spatial resolution, our NRT thickness estimates complement the airborne data because of their wider spatial and temporal extent Chevallier and SalasMelia, 2012), and even though the data do not extend into the summer season, their use should nevertheless lead to improved model skill (Day et al, 2014;Sigmond et al, 2013). A previous study (Rinne and Similä, 2016) has highlighted the potential value of fast-delivery CryoSat-2 data for the classification of sea ice into discrete stages of its development -thin (< 70 cm) and thick (> 70 cm) FYI and MYIin the Kara Sea. We have extended this initial analysis of the mission potential to provide continuous measurements of sea ice thickness across the entire Northern Hemisphere.…”

Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

“…Spatio-temporal changes can have a multitude of sources, e.g., differences in snow depth and composition [30,31], changes in surface roughness in the order of the radar wavelength [24] or temperature variations [32]. To circumvent this problem, adaptive thresholding has been proposed for larger scale assessments of ice type variability from altimeter data [7]. For small-scale investigations, the relative spatial evolution of waveforms corresponds well to changes of sea ice surface types as determined from SAR imagery (see .…”

“…The altimeter measures the sea ice freeboard, i.e., the distance between the ocean and the sea ice surface, and ice thickness can then be calculated assuming hydrostatic equilibrium and auxiliary information about sea ice density, i.e., sea ice type, and snow load [4]. To reduce the dependence on auxiliary information, the direct retrieval of sea ice type from altimeter waveform data has been investigated and showed promising results, but also a large variability of waveform parameters [5][6][7][8]. The introduction of the delay/Doppler or SAR altimeter has significantly increased the along-track resolution of spaceborne altimetric measurements [9,10].…”

“…Altimetric data alone is, however, not sufficient to unambiguously determine the origin of waveform changes over sea ice, because of temporal and spatial variability of waveforms and the impact of off-nadir specular reflectors [6,7,12]. Near-coincidental SAR imagery has proven a valuable source for verification of lead detection algorithms in altimeter data processing, e.g., [13,14].…”

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

“…This consistency, however, cannot be assured by the current ice-charting process for two main reasons. Firstly, the SAR data scenes are not always acquired over the area of interest in time for the ice charting (Rinne and Similä, 2016). This results in the ice analysts' requirement to extract ice information from other available sources, typically consisting of optical sensors (e.g.…”