Universidade Federal do Rio Grande do Sul, Instituto de Geociências, Centro Polar e Climático, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.
Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Sul, IFRS, Rodovia RS-239, Km 68, 3505, 95690-000 Rolante, RS, Brazil.
An Acad Bras Cienc. 2024 Jul 15;96(suppl 2):e20230704. doi: 10.1590/0001-3765202420230704. eCollection 2024.
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.
本研究调查了 2015 年至 2023 年间南极半岛北部干雪(DSRZ)和湿雪雷达区(WSRZ)的年际变化。通过将 CryoSat-2 数字高程模型和 ERA5 空气温度数据结合在 Google Earth Engine 上,创建了一个用于在 Sentinel-1 图像上检测雪区的特定代码。将后向散射系数(σ⁰)值超过-6.5 dB 的区域视为表面融化发生的范围,而干雪线被认为与年均气温的-11°C等温线重合。WSRZ 的年际变化与年均空气温度、总降水量和年度度日总和呈中度相关。然而,统计检验表明,DSRZ 行为与大气变量之间的决定系数低,趋势值不显著。与 2018/2018 年相比,2019/2020 年和 2020/2021 年 DSRZ 区域减少的结果表明,该 AP 地区的干区线呈上升趋势。该方法证明了其在数字处理环境中获取的数据的定量和定性分析中的有效性,允许对冰川质量损失的大尺度时空变化进行监测和理解。