# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 5 Discussion
The difference in average annual ice production between the two studies was more than 100 km\({}^{3}\). In addition, using different data sources or different definitions of polynya areas in the PMW method may also result in differences in estimates. For example, based on [19], the ice productions from AMSR-E/2, with finer resolution, were smaller than that from SSM/I, with coarser resolution. The ice production in the same year from [39] that used ice concentration to determine the polynya area, and from [19] that applied ice thickness to determine the polynya area were different. The annual average areas of RISP and MSP in 2017 and 2018 estimated from the PMW are consistent with previous studies. However, these numbers are much different from what is mapped from Sentinel-1 in this paper (Table 2). For example, in the RISP, the polynya areas from AMSR2 are much larger than those from our method, but in the MSP, the polynya areas from AMSR2 is only half of the value from our method in these two years. This is mostly due to different definitions of the polynya area between the PMW method and our method. However, due to the limited image data in 2017 and the short time period (only 2 years of data) examined, it is still difficult to determine if this difference in the two years is significant or not. Our next step is to extend this study further to 2019, 2020, and beyond. Although SAR can be used to accurately detect polynya because of its high spatial resolution, some uncertainties in ice production estimation in this study remain to be further examined. The polynya areas in the current study are derived by manually digitizing the outline/trace of the polynya-covered ice area, based on visual observations and experiences. This is time-consuming and labor-intensive. Due to the manually digitizing, the data sets are not easy to be reproduced or automatically extracted. Automated methods have been developed to identify the polynya area based on COSMO-SkyMed X-SAR and ERS C-SAR data [40; 41; 53; 54], and the results showed high agreement with manual results. However, these methods have not yet been used to derive time series of polynya areas, and are not applied in the condition that the polynya extent/outline in the gap days obtained, totally based on visual observation and experiences, from the images after the gap days. In future work, the automatic method is hoped to be developed based on the Sentinel-1 SAR, to estimate time series of polynya areas, using machine learning/deep learning methods. The ice thickness algorithm was developed for the thin ice area (ice thickness less than 20 cm), and the estimation accuracy would decrease when the ice thickness is larger than 20 cm. In this study, we see the ice thickness could be larger than 20 cm in the polynya area and we assume such thickness to be 30 cm, based on the average value of the observed ice thickness of more than 20 cm from the PIPERS 2017 cruise (ASPeCt ice observations) over the RISP,in May/June 2017. This assumption would cause some uncertainties. Satellite-based altimetry is a promising method to derive accurate sea ice thickness [46; 47]. The launch of ICESat-2 provides a great opportunity to estimate freeboard/ice thickness starting in September 2018; therefore, it is our next step to use ICESat-2 to derive ice thickness for ice production estimation.