Synopsis of New Research

1)A New Simplified Analytical Model for the Synthetic Aperture Radar (SAR) Altimetry to Improve Coastal Significant Wave Height


This project proposes the advancement of algorithm for the Sentinel 3A SAR altimetry. The advancement in the SAR altimetry demands a new algorithm in data processing protocol for retrieving the significant wave height (SWH). The shape of SAR returned echoes is significantly different from the “Brown-style” echoes of a conventional altimetry (e.g. Jason series), so a data processing protocol called as “waveform retracking” designed for the Brown-style waveform cannot be expected to work on SAR datasets unless a transformation is applied. Differ from conventional altimeters, the SAR waveforms are characterised by Doppler frequency, allowing for the formation of distinct radar-illuminated beams along the satellite track. To describe a SAR waveform, two independent variables (i.e. Doppler frequency and time delay) should be considered.

Although SAR altimetry has yielded more accurate retrievals of the SWH parameters in coastal zones, our initial studies indicate that the accuracy of parameters is not good enough for the ASEAN regions where complicated coastal topography is experienced. A new retracking algorithm had been successfully developed in our previous research (FRGS 2015-2018). Unfortunately, it only meant for the conventional altimetry, not for the SAR. In this project, a new algorithm will be developed by simplifying the analytic formula for the shape of SAR waveform under the ideal condition of small radar mispointing angle, and then applying it to the ocean returned signals (and discard the land contamination effects).
With the new algorithm, it is expected to reduce the no-data gap much closer (<1 km) to the coastline, with the accuracy as good as what it already found in the open ocean (several centimetre). The new retracker is significant in enabling an accurate long term geophysical data observations particularly in coastal oceans when combining with the previous 20 years of altimetry data.

2) Coastal Vulnerability Assessment for Towards Changing Climate

(ICONIC-RA UTM-GUP, 2020-2023)

This project aims to hindcast and forecast the environmental vulnerability towards the impact of climate change in the coastline of Terengganu. As the climate change indicators such as sea level rise (SLR) and sea surface temperature (SST) are currently showing the uptrend, this study will consider the coastal characteristics and forcing, as well as the socio-economics to assess the coastal vulnerability index (CVI) using
a deep learning of Artificial Neural Network (ANN). With the advanced technique, a linear or non-linear relationship among those variables can be modelled to hindcast the CVI, and then forecast the future CVI for the next 50 years. As the understanding about the extent and magnitude of climate change has improved, the need for more detailed modelling of the future impact towards the environmental sustainability has become increasingly urgent. The influenced of global climate change has alarmed the need to revise the beach protection criteria and mitigation plan.