Abstract
High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.
Highlights
Published: 20 November 2021The snow depth (SD) in mountainous areas at the watershed scale is significant for regional energy balance, snowmelt runoff prediction, and disaster warning [1,2]
This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications
Each study area was small and contained meteorological stations, we found that not all areas within the study area significantly increased in brightness
Summary
The snow depth (SD) in mountainous areas at the watershed scale is significant for regional energy balance, snowmelt runoff prediction, and disaster warning [1,2]. SD measurements rely heavily on meteorological station observations [3], but most snow in the Northern Hemisphere is located in mountainous or inaccessible remote areas, making observations hard to achieve. As the meteorological stations in mountainous areas are less distributed and located in areas with complex terrain, the in-situ observation data from a single station cannot reflect the spatial variability characteristics of mountain snow [4,5]. The rapid development of remote sensing technology has made it a promising alternative method for SD measurement [6], providing accurate snow monitoring at a large scale. There exists a relationship between reflectance and SD for optical remote sensing, and many studies have obtained
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