利用极化GNSS-R综合反演海冰盐度、密度和厚度

IF 11.4 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2025-03-01 Epub Date: 2025-01-26 DOI:10.1016/j.rse.2025.114617
Joan Francesc Munoz-Martin, Nereida Rodriguez-Alvarez, Xavier Bosch-Lluis, Kamal Oudrhiri
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引用次数: 0

摘要

本文提出了一种利用偏振全球导航卫星系统-反射测量(GNSS-R)估算海冰厚度(SIT)的新方法。在先前证明GNSS-R测量薄海冰能力的工作基础上,本研究利用土壤湿度主被动(SMAP)任务的数据,将其应用扩展到更厚和多年的海冰。该研究使用了三个关键数据集:来自SMAP的极化GNSS-R数据,来自CryoSat-2和SMOS的海冰厚度数据,以及来自ERA5的冰温数据。建立了将GNSS-R反射率与SIT相关联并考虑海冰盐度影响的详细模型。结果表明,GNSS-R衍生参数与CryoSat-2/SMOS SIT数据具有较高的相关系数,表明该方法具有较强的鲁棒性。该研究得出结论,全极化GNSS-R可用于估算海冰盐度和密度,这对于改进SIT模型以用于GNSS-R、其他雷达和微波辐射测量仪器至关重要。
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Integrated retrieval of sea-ice salinity, density, and thickness using polarimetric GNSS-R
This study presents a novel methodology for estimating sea-ice thickness (SIT) using polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R). Building on previous work that demonstrated the capability of GNSS-R to measure thin sea ice, this research extends the application to thicker and multi-year sea ice using data from the Soil Moisture Active Passive (SMAP) mission. The study employs three key datasets: polarimetric GNSS-R data from SMAP, sea-ice thickness data from CryoSat-2 and SMOS, and ice temperature data from ERA5. A detailed model correlating the GNSS-R reflectivity to SIT and incorporating the impact of sea-ice salinity is developed. Results show high correlation coefficients between the GNSS-R derived parameters and the CryoSat-2/SMOS SIT data, indicating the method's robustness. The study concludes that full polarimetric GNSS-R can be useful to estimate sea ice salinity and density, critical to improve SIT models for its use in GNSS-R, other radar, and microwave radiometry instruments.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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