Abstract
Compared with previous snow depth monitoring methods, global navigation satellite system-interferometric reflectometry (GNSS-IR) technology has the advantage of obtaining continuous daily observation data, and has great application potential. However, since GNSS satellites are in motion, their position in the sky is constantly varying, and the Fresnel reflection areas about the receiver in different periods alter accordingly. As a result, the retrieving results obtained from different GNSS satellites, and data sets collected in different periods, fluctuate considerably, making the traditional single-satellite-based GNSS-IR retrieving method have limitations in accuracy and reliability. Therefore, this paper proposed a novel GNSS-IR signal-to-noise ratio (SNR) retrieving snow depth method for fusing the available GNSS-IR observations to obtain an accurate and reliable result. We established the retrieval model based on the backpropagation algorithm, which makes full use of the back propagation (BP) neural network’s self-learning and self-adaptive capability to exploit the degree of contribution of different satellites to the final results. Then, the SNR observations of the global positioning system (GPS) L1 carrier from the Plate Boundary Observation (PBO) site P351 were collected to experiment for validation purposes. For all available GPS L1 carrier data, the snow depth values retrieved for each satellite were first obtained by the existing single-satellite-based GNSS-IR retrieval method. Then, four groups of comparison results were acquired, based on the multiple linear regression model, random forest model, mean fusion model, and the proposed BP neural network model, respectively. Taking the snow depth in-situ data provided by snow telemetry (SNOTEL) as a reference, the root mean squared error (RMSE) and mean absolute error (MAE) of the proposed solution are 0.0297 m and 0.0219 m, respectively. Furthermore, the retrieving results are highly consistent with the measured data, and the correlation coefficient is 0.9407.
Highlights
Zhang et al [14] further improved the accuracy of snow depth retrieval by establishing a grid model for retrieval of snow depth to attenuate the influence of surrounding terrain, compared with the results provided by the Plate Boundary Observation (PBO) H2O team
For global positioning system (GPS) positioning measurement, the signal received by the receiver is a vector sum of the direct signal from the satellite and the reflected signal that enters the receiver through the reflection of the features around the station [24]
The strength of the synthesized signal can be expressed in terms of signal-to-noise ratio (SNR), which is mainly affected by various factors, such as the transmitting power of the satellite signal, antenna gain, and multipath effect [25]
Summary
As an essential component of the hydrological system, snow plays an important role in balancing freshwater resources, and in the process of climate regulation [1]. It is important to continuously and accurately monitor the snow depth. The traditional way of snow monitoring mainly relies on the manual measurement on the ground, or the deployment of snow depth detection machines (using ultrasound or laser) [2], though the labor cost and time-consumption are appreciable. With the development of the GNSS, the GNSS-IR remote sensing technique has gradually become a research hotspot, and has been 4.0/).
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