期刊
ADVANCES IN SPACE RESEARCH
卷 68, 期 2, 页码 732-745出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2020.02.001
关键词
Sea ice freeboard; Remote sensing; Arctic; Delay-Doppler Altimetry; SAMOSA; TFMRA; Hamming; Zero-padding
类别
资金
- ESA Cryo-SeaNice Project
The study analyzes the main steps of radar freeboard computation as part of the Cryo-SeaNice Project, aiming to quantify the impacts of each processing method and identify optimal strategies to improve freeboard estimations from SAR altimetry measurements.
Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+. (C) 2020 COSPAR. Published by Elsevier Ltd.
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