# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 1 Introduction  
Antarctic is an important driver of global climate change and the last unexplored continent. Due to bad weather conditions, it is difficult to investigate Antarctica using field observation methods. The development of satellite remote sensing technology has greatly reduced the impact of adverse natural environmental conditions, making monitoring Antarctica rapid and convenient. A satellite remote sensing imagery mosaic map of Antarctica can be used to study the changes in large-scale environmental characteristics, such as glacier mass balance monitoring, ice shelf calving, and Antarctic ecology, to help people better understand the impact of polar changes on global climate change. Relative radiometric normalization (RRN) can be defined as the procedure of adjusting radiometric (or color) distortions from gray levels of a multispectral subject image based on a multi-spectral reference image [1]. Since multiple images in a mosaic dataset may be captured by multiple sensors, under different illumination conditions or from different seasons, the color statistics inside a single image or between images are inconsistent, which restricts their usage [2]. Therefore, it is necessary to perform RRN processing before image mosaicking. Since the first Chinese ocean satellite was launched in 2002, China has worked to establish a global operational ocean satellite observation system. Currently, the observation system consists of 10 satellites, which include three series: ocean color series satellites (HY-1), ocean dynamic environment series satellites (HY-2), and ocean surveillance and monitoring series satellites (HY-3) [3]. In this study, Antarctic images obtained from the HY-1C and HY-1D satellites are used to study a color-matching method during the mosaic process. The HY-1C and HY-1D satellites (Table 1) were launched in 2018 and 2020, respectively. They are an operational satellite constellation that belongs to Chinese ocean color satellites. The Coastal Zone Imager (CZI) instrument on board the ocean color seriessatellites is an optical remote sensing payload. The band includes blue, green, red, and near-infrared channels, with a spatial resolution of 50 m and a swath of 900 km. It can be used to monitor the coastal zone environment, sea ice, red tide, green tide, and marine and polar environments. The large coverage characteristics and good imaging performance of the CZI provide an effective observation method for the Antarctic region. Mapping with satellite images is important basic work for investigation and research in the Antarctic region. At present, some research institutions have carried out mosaic mapping of Antarctica with satellite images and released data products, and some applications based on these data products have been studied. For example, in 1997, the Byrd Polar and Climate Research Center of the Ohio State University and the Canadian Space Agency (CSA) obtained the Radarsat-1 Antarctic mosaic map with a resolution of 25 m for the first time (the data imaging time was from September to October in 1997) [4]. This work was the first stage of the Radarsat-1 Antarctic Mapping Project (RAMP), and the second stage of the RAMP data was collected from September to November in 2000 [5]. [PERSON] used RAMP data to discuss changes in several ice shelves and ice sheet margins around Antarctica [6]. In 2007, the United States Geological Survey (USGS), the National Science Foundation, the National Aeronautics and Space Administration (NASA), and the British Antarctic Survey (BAS) jointly produced the Landsat Antarctic Image Mosaic (LIMA) from 1065 Landsat images covering Antarctica between 1999 and 2003 [7; 8]. The National Snow and Ice Data Center of the United States developed an Antarctica mosaic image map with a resolution of 250 m based on 260 MODIS images acquired between 2003 and 2004. The Antarctic coastline, ground line, mainly ice shelf coverage, and blue ice distribution were developed based on this MODIS mosaic product [9]. [PERSON] et al.