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Abstract:
The Global Navigation Satellite System
Reflectometry (GNSS-R) technology has shown significant
potential in retrieving snow depth using Signal-to-Noise Ratio
(SNR) data. However, compared to traditional in-situ snow depth
measurement techniques, we have observed that the accuracy
and performance of GNSS-R can be significantly impacted under
certain conditions, particularly when the elevation angle
increases. This is due to the attenuation of the multipath effect,
which is particularly evident during snow-free periods and under
low snow conditions where snow depths are below 50 cm. To
address these limitations, we propose a snow depth inversion
method that integrates SNR signals with the Support Vector
Regression (SVR) algorithm, utilizing SNR sequences as feature
inputs. We conducted studies at stations P351 and P030, covering
elevation angles ranging from 5°to 20°, 5°to 25°, and 5°to 30°.
The experimental results show that the Root Mean Square Error
(RMSE) at both stations decreased by 50% or more compared to
traditional methods, demonstrating an improvement in inversion
accuracy across different elevation angles. More importantly, the
inversion accuracy of our method does not significantly lag
behind that at lower elevation angles, indicating its excellent
performance under challenging conditions. These findings
highlight the contribution of our method in enhancing the
accuracy of snow depth retrieval and its potential to drive further
advancements in the field of GNSS-R snow depth inversion.
