## 2 Materials and Methods  
### Detection Accuracy of the Marginal Zone of Glaciers--Landsat Case Study  
In our research, the main criterion for selecting the remote sensing data for analysis was the ability to determine the ice border by identifying the sharp boundary between ice and land or the clear border between ice and the dead ice zone. The need to include in the image the entire studied region in a given year was also assumed. Fieldwork conducted by the authors (1999-2001, 2007) allowed for the collection of this type of data in the studied region and gain experience in interpreting aerial and satellite images. The preliminary analysis of the use of LANDSAT and ASTER images showed a limited possibility of obtaining this data, mainly due to cloudiness (Table 1). Additionally, not all the images with cloudiness below 10% met the quality requirements (no full coverage, clouds in the area of research). The high resolution of the Planet and Sentinel images should not raise any doubts when detecting ice borders for such small glacial systems as found in the South Shetland Islands. However, in the case  
Figure 2: The average air temperature, precipitation, and averages for the periods analysed in this paper. The trends are from the Arctowski (A, 1977–1999), Bellingshausen (B, 1968–2014), Jubany (J, 1986–2014), and Ferraz (F, 1986–2013) stations. All plots from the Arctowski Station are based on data from the Department of Antarctic Biology of Polish Academy of Sciences (Warsaw, Poland), the plots from the Bellingshausen Station are based on data from the Arctic and Antarctic Research Institute (Saint Petersburg, Russia), the plots from the Jubany and Ferraz stations from the READER (Reference Antarctic Data for Environmental Research)—a project of the Scientific Committee on Antarctic Research, [[http://www.scar.org](http://www.scar.org)]([http://www.scar.org](http://www.scar.org)) [2]; the annual Positive Degree-Days (PDD) sums plots from Jubany (J) and Ferraz (F) stations from [PERSON] et al. [30]. of Landsat data, one may be afraid that images recorded in individual bands with different resolutions are not detailed enough for the precise detection of the ice border. In order to answer this question and decide to include this data for further analysis, a preliminary test was carried out. It consisted of comparing the course of the ice border determined on the basis of the most detailed data Planet data with the map being a processed image of Landsat-8. Both types of data were obtained for a similar period (P: 2018/03/10; L: 2018/03/09). The proposed methodology consists of the following steps:  
* **NDSI index map. ** The image after geometric and radiometric correction completed in the ENVI software (c) 2018 Harris Geospatial Solutions, Inc., Colorado, CO, USA) was classified to the map of Normalised Difference Snow Index (NDSI) using the following algorithm [31] (Figure 3A,B): \[\mathrm{NDSI}=\frac{\mathrm{Green}-\mathrm{SWIR1}}{\mathrm{Green}+\mathrm{ SWIR1}}\] where Green: (0.52-0.60 \(\mathrm{\SIUnitSymbolMicro m}\)) and SWIR1: (shortwave infrared) band (1.55-1.75 \(\mathrm{\SIUnitSymbolMicro m}\)). The NDSI index is based on the spectral characteristics of ice and snow. In general, in the entire visible spectrum, the reflection is very high: about 90% for snow and 65% for ice of glaciers. In contrast, in the shortwave infrared spectral reflection can be even lower than 10% [32]. * **panNDSI index map. ** A Panchromatic layer of Landsat (15 m resolution) was normalized to the range of 0-1 and multiplied by the NDSI index (pixel by pixel). As a result, a spatially and radiometrically enhanced NDSI map was generated in the resolution of 15 m. This layer was named the panNDSI index map (Figure 3C). * **Sensitivity analysis of the panNDSI index map to the detection of the glacier border. ** From the glacier border determined on the basis of the Planet image, a buffer zone was created at a distance equal to the panNDSI map resolution (2 \(\times\) 15 m; see Figure 3D,E).