Retrieval of sea ice thickness using FY-3E/GNOS-II data

• Published in: Satellite navigation Vol. 5; no. 1; pp. 17 - 13
• Main Authors: Xie, Yunjian, Yan, Qingyun
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Climate change; Datasets; Engineering; Fengyun-3E GNSS occultation sounder II (FY-3E/GNOS-II); Global navigation satellite system-reflectometry; Global positioning systems; GNSS Applications in Geosciences; GPS; Ice; Meteorological satellites; Original Article; Remote sensing; Salinity; Sea ice thickness; Sensors; Soil moisture ocean salinity; Weather; Arctic region; Sea ice thickness; Soil moisture ocean salinity; Global navigation satellite system-reflectometry; Fengyun-3E GNSS occultation sounder II (FY-3E/GNOS-II)
• DOI: 10.1186/s43020-024-00138-5
• ISSN: 2662-9291

Sea ice, a significant component in polar regions, plays a crucial role in climate change through its varying conditions. In Global Navigation Satellite System-Reflectometry (GNSS-R) studies, the observed surface reflectivity Γ serves as a tool to examine the physical characteristics of sea ice covers. This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model. However, it is important to note that when Sea Ice Thickness (SIT) becomes thicker, the accuracy of SIT retrieval via this two-layer model begins to decline. In this paper, we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E (FY-3E). Soil Moisture Ocean Salinity (SMOS) data are treated as the reference. The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval. The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m. By comparing the retrieved SITs against reference values, the three-layer model achieved a Root Mean Square Error (RMSE) of 0.149 m and Correlation Coefficient ( r ) of 0.830, while the two-layer model reported the RMSE of 0.162 m and r value of 0.789. A scheme incorporating both models yielded superior results than either individual model, with the RMSE of 0.137 m and r reaching up to 0.852. This study is the first application of FY-3E for GNSS-R SIT retrieval, combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m. This provides a good reference for the future studies on GNSS-R SIT retrieval.