Sea ice has an intense impact on the polar environment, ocean circulation, weather and regional climate. Unexpected melting of sea ice, which is considered as one of the climate change effects, has become a potential threat to the Earth’s climate. The regular monitoring of sea ice and its extent has become very important towards understanding of sea ice temporal dynamics. In this study, we present an operational technique of generation of sea ice images and sea ice area (derived from the images) using level-4 data from Indian Scatterometer SCATSAT-1. Using hierarchical classification rules, the threshold-based technique has been developed and applied to generate super-resolution (2.25 km) daily sea ice images over the Antarctic for the years 2017 and 2018. The technique uses four SCATSAT-1 data products, i.e. Gamma0 [Horizontal (H) and Vertical (V)] and Brightness Temperature (H and V) to classify sea ice, open water and other classes. Classification accuracy has been assessed by comparing SCATSAT-1 sea ice images with those obtained from AMSR2 sea ice concentration data. The comparison shows that there is around 96.1% matching of sea ice classification between SCATSAT-1 and AMSR-2 SIC derived sea ice images. Hence, it indicates that the super-resolution data of SCATSAT-1 is well capable of distinguishing sea ice from water.

海冰对极地环境、海洋环流、天气和区域气候产生强烈影响。海冰的意外融化被认为是气候变化影响之一，已成为对地球气候的潜在威胁。定期监测海冰及其范围对于了解海冰时间动态变得非常重要。在这项研究中，我们提出了一种使用来自印度散射计 SCATSAT-1 的 4 级数据生成海冰图像和海冰面积（源自图像）的操作技术。使用分层分类规则，基于阈值的技术已被开发并应用于生成 2017 年和 2018 年南极上空的超分辨率（2.25 公里）每日海冰图像。该技术使用四种 SCATSAT-1 数据产品，即 Gamma0 [水平的 （H ) 和垂直 ( V )] 和亮度温度（H和V）对海冰、开阔水域和其他类别进行分类。通过将 SCATSAT-1 海冰图像与从 AMSR2 海冰浓度数据获得的图像进行比较，评估了分类精度。比较表明，SCATSAT-1 和 AMSR-2 SIC 派生的海冰图像之间的海冰分类匹配率约为 96.1%。由此可见，SCATSAT-1的超分辨率数据能够很好地区分海冰和水。