Abstract 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. (DSRZ (WSRZ 20152023. 20152023 2015 2023. 2023 2015-2023 Sentinel1 Sentinel 1 Sentinel- CryoSat2 CryoSat 2 CryoSat- ERA5 ERA σ⁰ σ (σ⁰ 6.5 65 6 5 -6. occurrence 11 -1 C precipitation degreedays. degreedays degree days. days degree-days However variables 20192020 2019 2020 2019/202 20202021 2021 2020/202 20182018 2018 2018/201 region environments largescale large scale loss 2015202 201 202 2015-202 (σ 6. -6 - 2019202 2019/20 2020202 2020/20 2018201 2018/20 201520 20 2015-20 201920 2019/2 202020 2020/2 201820 2018/2 20152 2015-2 20192 2019/ 20202 2020/ 20182 2018/ 2015-

Abstract Several studies have utilized passive microwave imagery for monitoring snowmelt in Antarctica. However, due to the low spatial resolution of these images (25 km), the quantification of snowmelt is not precise. To enhance the accuracy of these estimations, this study proposed a subpixel analysis approach based on a Spectral Linear Mixing Model. This approach was applied to images obtained from channels 18/19 GHz and 37 GHz, both horizontally and vertically polarized, acquired from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSM/IS) instruments, spanning the period 1978-2018. The spatiotemporal analysis of the estimated snowmelt fraction images indicated that the most persistent and intensive melt was observed on the Antarctic Peninsula, particularly on the Larsen, Wilkins, George VI, and Wordie ice shelves. The melting period in the Antarctic Peninsula began in late October, with a peak in early January, and ended in late March. Other regions with persistent and intensive snowmelt were Mary Bird Land and Wilkes Land, followed by Dronning Maud Land, Amery Ice Shelf, Filchner-Ronne Ice Shelf, and Ross Ice Shelf. These snowmelt data are valuable for modeling the impacts of snowmelt on glacial systems, local coastal environments, and sea-level rise. Antarctica However 25 (2 km, km , km) precise estimations Model 1819 18 19 18/1 3 polarized SMMR, SMMR (SMMR) SSM/I, SSMI SSM/I SSM I (SSM/I) ImagerSounder Sounder SSM/IS SSMIS IS (SSM/IS instruments 19782018. 19782018 1978 2018. 2018 1978-2018 Larsen Wilkins VI shelves October January March Shelf FilchnerRonne Filchner Ronne systems environments sealevel sea level rise 2 ( 181 1 18/ (SMMR (SSM/I 1978201 197 201 1978-201 197820 20 1978-20 19782 1978-2 1978-

Abstract 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.