# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 1 Introduction
Since 2002, AMSR-E on Aqua greatly improved the spatial resolution with the footprints of K and Ka bands of \(16\times 27\) km\({}^{2}\) and \(8.8\times 14.4\) km\({}^{2}\), respectively, enabling the identification of some smaller polynyas that could not be detected by SSM/I (s) [19]. Optical or thermal remote sensing are good choices to monitor polynyas based on the difference in albedo or temperature between ice surface and water surface [29; 30; 31; 32], but cloud cover and/or polar nights limit their applications. Aiming to conquer the influence of the cloud cover and polar nights, some studies used spatial feature reconstruction methods to derive cloud-covered sea-ice area, such as temporal interpolation based on the surrounding days [31; 33], fuzzy cloud artifact filter [34], and combining PMW and optical remote sensing to yield a merged sea-ice concentration for identification of polynya [35]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temporal** & **Mean Daily Area (km\({}^{2}\))** & \
\begin{tabular}{c} **Mean Annual Cumulative** \\ **Volume (km\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Data** \\ **Sources** \\ \end{tabular} & **References** \\ \hline
1987 & RISP + MSP = 25,000 & & SMMR & [PERSON] et al. (1985) [36] \\ \hline
1992–2002 &
\begin{tabular}{c} RISP = 25,000 \(\pm\) 5000 \\ MSP = 2000 \(\pm\) 600 \\ \end{tabular} & RISP = 500 \(\pm\) 160 & SSM/[PERSON] et al. (2007) [25] \\ \hline
1992–2001 & & RISP + MSP = 390 \(\pm\) 59 & SSM/[PERSON] et al. (2008) [8] \\ \hline
1997–2002 & RISP + MSP = 20,000 & & SSM/[PERSON] and [PERSON] (2003) [37] \\ \hline
1992–2008 & RISP + MSP + TNBP = 930,000 &
\begin{tabular}{c} RISP = 602 \\ MSP = 47 \\ \end{tabular} & SSM/[PERSON] et al. (2011) [24] \\ \hline
1992–2013 & RISP + MSP = 22,000 \(\pm\) 3000 & RISP + MSP = 382 \(\pm\) 63 & SSM/[PERSON] & [PERSON] et al. (2016) [21] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of polynya ice production in the Ross Sea presented in previous studies. Synthetic aperture radar (SAR) has been used to identify sea ice from water [40; 41], and to provide the most accurate polynya area/extent [42; 43]. Satellite-borne SAR data has not been used for routine monitoring activities, because its longer revisiting time cannot capture every wind-driven polynya event. Since its spatial resolution (less than 100 m) is much finer than PMW (from 6 km to 25 km), it has been used to provide reference or validation for PMW estimation of ice production and mapping polynyas [15; 18; 23]. Since 2014/2016, however, Sentinel-1 SAR images with high temporal and spatial resolution have been freely available, starting a new era in satellite SAR observations for many applications. The Sentinel-1 data thus provides an unprecedented opportunity to study the yearly occurrence frequency and area/extent of coastal polynyas in polar regions. This study presents the first attempt at such an examination. It is generally accepted that ice thickness is best estimated through ice freeboard or total freeboard measurements from radar/lidar altimetry under the hydrostatic equilibrium assumption [44; 45; 46; 47; 48]. During the period of this study (2017-2018), Cryosat 2 radar altimetry was the only one available for estimating ice freeboard in the Ross Sea. Converting ice freeboard into ice thickness, however, still requires other parameters (such as snow depth, snow, water, and ice densities) that would bring additional uncertainties to the final sea ice thickness estimates. In any case, there is no such sea ice thickness product yet available for the period of this study for the Ross Sea. Although the coarse spatial resolution of PMW limited its application in small polynyas, especially before 2002 when AMSR-E was launched, PMW is currently the only effective dataset for obtaining thin ice thickness at the daily temporal resolution and has been routinely used to obtain long-term series of ice thickness [8; 19; 38].