Abstract
Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for snow sensing. However, most previous studies mainly used GPS L1C/A and L2C Signal-to-Noise Ratio (SNR) data to retrieve snow depth. In this paper, snow depth variations are retrieved from new weak GPS L2P SNR data at three stations in Alaska and evaluated by comparing with in situ measurements. The correlation coefficients for the three stations are 0.79, 0.88 and 0.98, respectively. The GPS-estimated snow depths from the L2P SNR data are further compared with L1C/A results at three stations, showing a high correlation of 0.94, 0.93 and 0.95, respectively. These results indicate that geodetic GPS observations with SNR L2P data can well estimate snow depths. The samplings of 15 s or 30 s have no obvious effect on snow depth estimation using GPS SNR L2P measurements, while the range of 5°–35°elevation angles has effects on results with a decreasing correlation of 0.96 and RMSE of 0.04 m when compared to the range of 5°–30° with correlation of 0.98 and RMSE of 0.03 m. GPS SNR data below 30° elevation angle are better to estimate snow depth.
Highlights
Snow is one of the most important components in the hydrological system, which impacts the water cycle and atmospheric circulation [1,2,3]
These results indicate that geodetic GPS observations with Signal-to-Noise Ratio (SNR) L2P data can well estimate snow depths
This paper aims to overcome the above limitations with the new L2P SNR data, which are used to retrieve snow depth
Summary
Snow is one of the most important components in the hydrological system, which impacts the water cycle and atmospheric circulation [1,2,3]. It is difficult to monitor snow variations because of high spatial and temporal variability. Manual measurements, including snow depth and density observations, have high precision, but low temporal resolution. The automated techniques, e.g., sonic depth measurements, snow pillows and gamma radiation measurements, have a higher temporal resolution, but miss the information of the spatial variations on-site [5]. One of the benefits is that GPS-R has a high temporal and spatial resolution, and GPS observations from IGS network are permanent, continuous and freely available. Concerning the Remote Sens. 2016, 8, 63; doi:10.3390/rs8010063 www.mdpi.com/journal/remotesensing
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