- Title
- CAWRES: a waveform retracking fuzzy expert system for optimizing coastal sea levels from Jason-1 and Jason-2 satellite altimetry data
- Creator
- Idris, Nurul Hazrina; Deng, Xiaoli; Din, Ami Hassan Md; Idris, Nurul Hawani
- Relation
- Remote Sensing Vol. 9, Issue 6, no. 603
- Publisher Link
- http://dx.doi.org/10.3390/rs9060603
- Publisher
- MDPIAG
- Resource Type
- journal article
- Date
- 2017
- Description
- This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR) products, because the former has a higher (≥0.77) temporal correlation and smaller (≤19 cm) root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions.
- Subject
- coastal altimetry; fuzzy expert system; neural network; waveform retracking; tide gauge; validation
- Identifier
- http://hdl.handle.net/1959.13/1398483
- Identifier
- uon:34440
- Identifier
- ISSN:2072-4292
- Rights
- © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
- Language
- eng
- Full Text
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