GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data

Jinsheng Tu,Haohan Wei,Jichao Lv,Peng Li,Rui Zhang,Yanxia Wang,Lei Yang,Nan Li,Shihai Nie,Xiaoming Li

doi:10.3390/rs13214311

Abstract

Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

Highlights

Introduction conditions of the Creative CommonsSnow is an important part of the land hydrological cycle and global climate system.Accurate real-time snow depth data are an important reference indicator for water resource management and climate disaster warning [1,2]
The results showed that this method can be effectively used for snow depth detection [20]
It can be seen that the technical route of the article can be divided into three parts: (1) global navigation satellite system interferometric reflectometry (GNSS-IR) data preprocessing is carried out, where the signal-tonoise ratio (SNR), pseudo-random noise (PRN), satellite elevation angle, azimuth angle, and other data parameters are extracted from the observation (OBS) file and navigation (NAV) file collected by GNSS receivers; (2) the Lomb–Scargle periodogram (LSP) method is used to analyze both the reflector height of snow-free and snow surfaces and the difference between them in order to retrieve the snow depth; (3) the multi-GNSS and multi-frequency GNSS-IR snow depth retrieval results and Plate Boundary Observatory (PBO) snow depth data are compared, and the mean fusion analysis of the multi-GNSS and multi-frequency GNSS-IR snow depth retrieval results is carried out

Summary

Introduction

Introduction conditions of the Creative CommonsSnow is an important part of the land hydrological cycle and global climate system.Accurate real-time snow depth data are an important reference indicator for water resource management and climate disaster warning [1,2]. GNSS-IR is a kind of satellite remote sensing technology that uses GNSS signals as the transmitting source to realize the retrieval of the physical parameters of surface targets by receiving and processing the interference effect of GNSS signals formed by direct and surface reflection [4,5,6]. At present, this technology is mainly employed to retrieve the soil moisture content (SMC), snow depth, and vegetation parameters [7,8,9,10,11,12]

Objectives

Methods

Results

Conclusion