Abstract

In this article, an effective schematic is developed for estimating sea ice thickness (SIT) from the reflectivity ( $\Gamma$ ) produced with TechDemoSat-1 (TDS-1) Global Navigation Satellite System-Reflectometry data. Here, $\Gamma$ 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 $\Gamma$ is neglected when only considering signals of coherent reflection. In practice, $\Gamma$ at the specular point is first generated using TDS-1 data. Afterwards, SIT is calculated from TDS-1 $\Gamma$ 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 1 m. 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.

Highlights

  • A GOOD knowledge of sea ice parameters helps improve the understanding of global climate change and facilitates human activities in ice-covered regions

  • Sea ice remote sensing has been a topic of interest in Global Navigation Satellite System-Reflectometry (GNSS-R) circles due to public access to the data provided by the TechDemoSat-1 (TDS-1) satellite

  • It is worth mentioning that a spreading of the DDM has been observed in some cases over sea ice, suggesting that the scattering might contain a relevant incoherent component superimposed to the coherent one [2]

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Summary

INTRODUCTION

A GOOD knowledge of sea ice parameters helps improve the understanding of global climate change and facilitates human activities in ice-covered regions. Large-scale SIT data can be derived from 1) sea ice elevation (freeboard) measurement by satellite altimeters, e.g., European Remote Sensing [14], ENVISAT [15], and CryoSat-2 [16], based on the conversion between SIT and freeboard; or 2) microwave radiometry, e.g., Soil Moisture Ocean Salinity (SMOS) [17] and Soil Moisture Active Passive (SMAP) [18], according to the model of brightness temperature measurement and SIT [19]. Mayers and Ruf [20] confirmed the possibility of measuring SIT with GNSS-R based on simulated data. This method, cannot be implemented currently due to lack of raw data.

TDS-1 Remote Sensing Data
Reference Data
Derivation of Reflectivity
Relationship Between SIT and Γ
Dielectric Models
RESULTS
Γ: CYGNSS and TDS-1
SIT Estimate
Case Study
Error Source Analysis
CONCLUSION
Full Text
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