Search

This work investigated the annual variations in dry snow (DSRZ) and wet snow radar zones (WSRZ) in the north of the Antarctic Peninsula between 2015-2023. A specific code for snow zone detection on Sentinel-1 images was created on Google Earth Engine by combining the CryoSat-2 digital elevation model and air temperature data from ERA5. Regions with backscatter coefficients (σ⁰) values exceeding -6.5 dB were considered the extent of surface melt occurrence, and the dry snow line was considered to coincide with the -11 °C isotherm of the average annual air temperature. The annual variation in WSRZ exhibited moderate correlations with annual average air temperature, total precipitation, and the sum of annual degree-days. However, statistical tests indicated low determination coefficients and no significant trend values in DSRZ behavior with atmospheric variables. The results of reducing DSRZ area for 2019/2020 and 2020/2021 compared to 2018/2018 indicated the upward in dry zone line in this AP region. The methodology demonstrated its efficacy for both quantitative and qualitative analyses of data obtained in digital processing environments, allowing for the large-scale spatial and temporal variations monitoring and for the understanding changes in glacier mass loss.

The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.

This article aims to analyze the dynamics of freezing and thawing of Antarctic lakes located in ice-free areas on Nelson Island and Fildes Peninsula, where response to changes in air temperature and precipitation rates occur rapidly, during the period from July 2016 to December 2018. In these places, which are difficult to access, remote sensing is an important alternative, especially considering the use of active remote sensors such as the Synthetic Aperture Radar (SAR), which has less restriction regarding the presence of clouds over the study area. Three backscatter thresholds were defined (σ) for the identification of the physical state of the water of the lakes of the study region, applied in Sentinel 1A SAR (S1A) images under Horizontal Horizontal (HH) polarization and Interferometric Wide (IW) imaging mode. These images, along with the air temperature data obtained by the Interim Re-Analysis (ERA-Interim) atmospheric reanalysis model, provided the evidence for the interpretation of the freezing and thawing periods of the lakes. The thresholds applied for the definition of the physical state of the lake water were greater than - 14 dB for frozen water, between - 14 and - 17 dB for the surface, with up to 60% of their frozen area, and less than - 17 dB for open water. The temporal analysis revealed that the lakes start to thaw in October, become completely thawed in February, and freeze again in March. Nevertheless, it can be said that the S1A satellite allows a satisfactory identification of the liquid and solid phases of the water in the lakes of the study region.