## 6 Conclusions  
In this study, we present the geolocation method for BNU-1 images including two steps. For the first step, a rigorous geometric calibration model was applied to transform the image coordinates to the geographic coordinates for the BNU-1 images. The images geolocated by the geometric calibration model are the BNU-1 L1A product. For the second step, an automated geometric correction method was used to reduce the geolocation errors of the BNU-1 L1A product. The images corrected by the geometric correction method are the BNU-1 L1B product.  
The geometric correction method is commonly used for improving the geolocation errors of the visible image. However, the texture features of the ice and snow surfaces are rarely seen in polar images, which makes it difficult to find the CPs. The combination of the image division method and piecewise linear image enhancement method was applied to the BNU-1 L1A product and the corresponding MODIS images, and the results indicate that the CPs extracted increased by 30% to 182%, which can effectively improve the geometric accuracy of the BNU-1 images.  
The geolocation method was applied to 28 images of Antarctica and 15 images of Arctic regions. The average geolocation error was reduced from 10 km to \(\sim\)300 m. The coastlines in the image mosaics from the BNU-1 L1B product were consistent with the coastline dataset. These results suggest that the geolocation method has the ability to improve the geolocation errors of BNU-1 images and other satellite images in polar regions.  
Conceptualization, X.C.; methodology, Y.Z. and Z.C. ([PERSON]); software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z. and Z.C. ([PERSON]); writing--review and editing, Y.Z., Z.C. ([PERSON]), Z.C. ([PERSON]), F.H., T.L., B.Z. and X.L.; visualization, Y.Z.; supervision, Z.C. ([PERSON]), X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 41925027), National Key Research and Development Program of China (Grant No. 2019 YFC1509104 and 2018 YFC1406101), and Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021008).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We greatly thank NASA for providing the MODIS Level 1B calibrated radiances data (MOD02 QKM and MYD02 QKM) ([[https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)]([https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)), accessed on 11 September 2020), BAS for the high resolution vector polylines of the Antarctic coastline (7.4) ([[https://data.bas.ac.uk/items/e46](https://data.bas.ac.uk/items/e46) be5 bc-ef8e-4 fd5-967b-92863 fe2835/#item-details-data]([https://data.bas.ac.uk/items/e46](https://data.bas.ac.uk/items/e46) be5 bc-ef8e-4 fd5-967b-92863 fe2835/#item-details-data), accessed on 1 August 2021 ), and NSIDC for the MEASUREs MODIS Mosaic of Greenland (MOG) 2005, 2010, and 2015 Image Maps, Version 2 ([[https://nsidc.org/data/nsidc-0547](https://nsidc.org/data/nsidc-0547)]([https://nsidc.org/data/nsidc-0547](https://nsidc.org/data/nsidc-0547)), accessed on 20 October 2020 ). We greatly thank [PERSON] for her help with the preprocessing of MODIS images.  
The authors declare no conflict of interest.