AUTHOR=Tian Jiasheng , Wang Jin , Shi Jian 

TITLE=Performance analysis of the improved second-order retracking algorithm and its application for significant wave height estimation

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1569799

DOI=10.3389/fmars.2025.1569799

ISSN=2296-7745

ABSTRACT=IntroductionCompared to the measurement bias of sea surface height (<5 cm), the measurement bias of significant wave height (SWH) is around 10% (typically resulting in a 40 cm deviation for a 4 m SWH), making it challenging to meet the increasing demand for disaster prevention and reduction.MethodsIn this study, the presented second-order retracking algorithm (MLE6) is investigated to specify furtherly the accuracy of SWH inversion. MLE6 includes skewness coefficient (λs) and electromagnetic bias coefficient (λem), in addition to four conventional parameters. The effects of non-linear or non-Gaussian random ocean surfaces on estimating SWH are analyzed and an improved adaptive algorithm is presented by considering the real radar point target response (PTR). The echoes simulated by MLE6 were compared with those of a three-term convolution model (Brown model) that considered the non-Gaussian rough sea surface elevation distribution.ResultsMLE6 showed closer alignment with the Brown model compared to the conventional model (MLE4), and exhibited better accuracy in SWH inversion. The improvement achieved by MLE6 in inverting SWH was approximately 3–7 cm. The improved adaptive algorithm, which incorporated the actual PTR of SWIM, further improved the accuracy of SWH inversion by 3–4 cm when compared to the adaptive algorithm used by SWIM.DiscussionMLE6 showed better accuracy in retrieving SWH than MLE4 with considering non-gaussian ocean. The improved adaptive algorithm, considering the realistic radar PTR and non-gaussian ocean, increased the accuracy of SWH inversion by an additional 4 cm from the surface wave investigation and monitoring (SWIM) measurements.