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

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

doi:10.3390/rs13214311

Remote Sensing (Oct 2021)

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

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

Affiliations

Jinsheng Tu: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Haohan Wei: College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Rui Zhang: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Lei Yang: College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Jichao Lv: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Xiaoming Li: College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
Shihai Nie: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Peng Li: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Yanxia Wang: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Nan Li: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China

DOI: https://doi.org/10.3390/rs13214311
Journal volume & issue: Vol. 13, no. 21
p. 4311

Abstract

Read online

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.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

About the journal

Abstract

Keywords