Singh Manmit Kumar, Anilkumar Ritu, Bharti Rishikesh
Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
Department of Space, North Eastern Space Applications Centre, Ri Bhoi, Meghalaya, India.
Environ Monit Assess. 2024 Dec 10;197(1):45. doi: 10.1007/s10661-024-13476-3.
Snow is considered contaminated when any foreign materials are deposited/mixed with it, which can accelerate melting and significantly impact the snow cover's radiative balance. Such an enhanced melting rate results in a reduction in freshwater sources at the catchment level. In optical remote sensing, snow contamination is widely studied using a normalizing difference index called the snow contamination index. This is based on the finding that the impact of snow contamination diminishes with wavelength and is most noticeable in the visible spectrum (0.3-0.7 μm). However, the study of snow contamination using optical remote sensing is hindered in the Himalayan terrain due to enduring cloud cover in the region. Synthetic Aperture Radar (SAR) data such as Sentinel-1 can be used to ensure all-weather monitoring of such areas. This study focuses on the SAR backscattering behavior at the C-band of clear and contaminated snow for March 2022 in a part of the Eastern Himalayas of Arunachal Pradesh, India. An attempt has been made to utilize Landsat-9 and Sentinel-1 to study the snow contamination. The SAR backscattering for snow conditions (clear/contaminated) is studied using thresholds obtained from the Landsat-9 snow cover map. The SCI and SAR backscattering statistical analysis shows a negative correlation (R > 0.6) at a 95% confidence level. It is observed that in the microwave region of the C-band, contaminated snow has a comparatively higher backscattering value than clear snow. However, in the visible wavelength, the contaminated snow has a lower reflectance value than clean snow. Such behavior of the snowpack in the microwave region of the C-band is explained using the physical properties of the snowpack and the dominant scattering mechanism over the surface. The key findings of this study suggest that SAR backscattering is affected by snow contamination due to changes in the local incidence angle, snow wetness, and surface roughness. This research provides critical insight into snow contamination using microwave remote sensing, which can be the first step toward developing an index for radar observations.
当有任何外来物质沉积/混入雪中时，雪就被视为受到污染，这会加速融化并显著影响积雪的辐射平衡。这种加快的融化速度会导致集水区层面淡水资源的减少。在光学遥感中，人们广泛使用一种名为雪污染指数的归一化差异指数来研究雪污染。这是基于这样一个发现：雪污染的影响会随着波长减小，并且在可见光谱（0.3 - 0.7微米）中最为明显。然而，由于该地区长期有云层覆盖，在喜马拉雅地区利用光学遥感研究雪污染受到了阻碍。诸如哨兵 - 1号这样的合成孔径雷达（SAR）数据可用于确保对这些地区进行全天候监测。本研究聚焦于2022年3月印度阿鲁纳恰尔邦东喜马拉雅部分地区清洁雪和受污染雪在C波段的SAR后向散射行为。已尝试利用陆地卫星9号和哨兵 - 1号来研究雪污染。利用从陆地卫星9号积雪图获得的阈值，研究了雪况（清洁/受污染）的SAR后向散射。雪污染指数（SCI）和SAR后向散射统计分析表明，在95%置信水平下存在负相关（R > 0.6）。据观察，在C波段的微波区域，受污染的雪比清洁的雪具有相对更高的后向散射值。然而，在可见波长下，受污染的雪比干净的雪具有更低的反射率值。利用积雪的物理特性和表面上占主导地位的散射机制，对C波段微波区域积雪的这种行为进行了解释。本研究的主要发现表明，由于局部入射角、雪湿度和表面粗糙度的变化，SAR后向散射会受到雪污染的影响。这项研究为利用微波遥感研究雪污染提供了关键见解，这可能是朝着开发雷达观测指数迈出的第一步。
