Sea Ice Thickness Measurement Using Spaceborne GNSS-R: First Results With TechDemoSat-1 Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 577 - 587
• Main Authors: Yan, Qingyun, Huang, Weimin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Chemical analysis; Correlation coefficient; Correlation coefficients; Data; Estimation; Global navigation satellite system; Global navigation satellite system-reflectometry (GNSS-R); Ice cover; Ice thickness; Navigation; Navigational satellites; Ocean temperature; Reflectance; Reflection; Reflectometry; Remote sensing; Sea ice; sea ice thickness (SIT); Sea measurements; Sea surface; Seawater; Signal reflection; Soil; Soil moisture; soil moisture active passive (SMAP); soil moisture ocean salinity (SMOS); Soils; Specular reflection; Surface roughness; Surface roughness effects; TechDemoSat-1 (TDS-1); Thickness measurement; Water analysis
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2966880

In this article, an effective schematic is developed for estimating sea ice thickness (SIT) from the reflectivity (Γ) produced with TechDemoSat-1 (TDS-1) Global Navigation Satellite System-Reflectometry data. Here, Γ is formulated as the product of the propagation loss due to SIT and the reflection coefficient of underlying seawater. The effect of surface roughness on Γ is neglected when only considering signals of coherent reflection. In practice, Γ at the specular point is first generated using TDS-1 data. Afterwards, SIT is calculated from TDS-1 Γ based on the proposed reflectivity model, and verified with two sets of reference SIT data; one is obtained by the Soil Moisture Ocean Salinity (SMOS) satellite, and the other is the combined SMOS/Soil Moisture Active Passive (SMAP) measurements. This analysis is performed on the data with SIT less than 1m. Through comparison, good consistency between the derived TDS-1 SIT and the reference SIT is obtained, with a correlation coefficient (r) of 0.84 and a root-mean-square difference (RMSD) of 9.39 cm with SMOS, and an r of 0.67 and an RMSD of 9.49 cm with SMOS/SMAP, which demonstrates the applicability of the developed model and the utility of TDS-1 data for SIT estimation. In addition, this method is proved to be useful for improving existing sea ice detection accuracy.