GNSS-R Snow Depth Inversion Study Based on SNR-SVR

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 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 at both the 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.

Keywords

• Global navigation satellite system reflectometry (GNSS-R)
• signal-to-noise ratio (SNR)
• snow depth
• support vector regression (SVR)