An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions

Weizeng Shao,Jingsong Yang,Yuyi Hu,Huan Li,Jian Sun,Ferdinando Nunziata,Juncheng Zuo

doi:10.3390/rs10091367

Abstract

In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach.

Highlights

The tropical cyclone, which is a rapidly atmospheric rotating storm system characterized by strong winds, central low-pressure, and heavy rainfall, is among the most dangerous and destructive of natural phenomena
Wave retrieval under cyclonic conditions is a topic of general interest in the field of synthetic aperture radar (SAR) application
Our work presents the possibility of cyclone wave retrieval from S-1 SAR images with a wide swath coverage (>200 km)

Summary

Introduction

The tropical cyclone, which is a rapidly atmospheric rotating storm system characterized by strong winds, central low-pressure, and heavy rainfall, is among the most dangerous and destructive of natural phenomena. Ocean wave retrieval algorithms include theoretical-based retrieval schemes, e.g., the “Max-Planck Institute” (MPI) [7,8,9], the Semi Parametric Retrieval Algorithm (SPRA) [10], the Partition Rescaling and Shift Algorithm (PARSA) [11], and the Parameterized First-guess Spectrum Method (PFSM) [12,13,14], as well as empirical models, e.g., CWAVE_ERS [15], CWAVE_ENVI [16], CWAVE_S1 [17], CSAR_WAVE [18,19], and QPCWAVE_GF3 [20] All these algorithms have been developed to exploit SAR measurements collected at low and moderate sea states, due to the lack of SAR datasets collected at high sea state conditions.

Data Description

Validation

Conclusions