融合随机森林与线性回归的相干X波段雷达平均波周期反演

刘含; 王素悦; 张清河; 沈钊阳

doi:10.11834/jrs.20254569

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融合随机森林与线性回归的相干X波段雷达平均波周期反演
Fusion of random forest and linear regression for mean wave period retrieval using coherent x-band radar
2026年30卷第1期页码：132-143
收稿：2024-12-06，

纸质出版：2026-01-07
DOI： 10.11834/jrs.20254569
稿件说明：

移动端阅览

刘含，王素悦，张清河，沈钊阳.2026.融合随机森林与线性回归的相干X波段雷达平均波周期反演.遥感学报，30（1）： 132-143 DOI： 10.11834/jrs.20254569.

Liu H，Wang S Y，Zhang Q H and Shen Z Y. 2026. Fusion of random forest and linear regression for mean wave period retrieval using coherent x-band radar. National Remote Sensing Bulletin， 30（1）：132-143 DOI： 10.11834/jrs.20254569.

摘要

由于波浪破碎及其演化，波数频率谱中出现了群线及高次谐波，并伴随着主浪能量的减少。群线往往被认为是相干微波雷达获取波周期高估的主要原因。已有研究试图去除部分或全部群线能量，以提高波周期估计的准确性。然而，由于波数频率谱中能量分布的不确定性，从谱的角度反演波周期时总是存在一定的偏差。为此，本文提出了一种基于相干X波段雷达的随机森林和线性回归的集成模型的平均波周期估计方法。该方法直接从速度空时序列的波浪运动规律中估计波周期；首先，从雷达获取的时间多普勒谱推导得到回波速度空时序列；其次，从速度空时序列中进行特征提取并与ECMWF数据构建模型预测最小峰距，找到波峰和波谷对应的位置；最后，利用海浪波长与周期的关系反演平均波周期。并通过仿真验证了该方法的有效性。此外，利用部署在中国山东省沿海的相干X波段雷达收集的近3 d数据集进行分析，反演得到平均波周期，垂直极化（VV）和水平极化（HH）的均方根误差分别为0.15 和0.22 s。结果表明，该方法可以实现对海浪参数的实时估计。

Abstract

Breaking and its evolution lead to some nonlinear features in the wavenumber-frequency spectrum observed by radar

such as group line and higher harmonic energy

accompanied by a decrease in the energy of the dominant wave. Group line is often considered the main reason for the overestimation of mean wave periods obtained by coherent microwave radar. Many methods have attempted to remove part or all the energy from group lines to improve the accuracy of mean wave period estimation. However

a certain degree of bias always occurs when inferring wave periods from the spectrum because of the uncertainty in the energy distribution within the wavenumber-frequency spectrum. The primary objective of this study is to develop a novel

robust method for estimating mean wave periods by using coherent X-band radar data

specifically addressing the challenges posed by these nonlinear features in the wavenumber-frequency spectrum. To address this issue

this paper proposes a mean wave period estimation method based on a random forest and linear regression ensemble model using coherent X-band radar

which estimates mean wave periods directly from the motion characteristics of waves in the spatial–temporal velocity series. The proposed method integrates random forest and linear regression into a powerful ensemble. It first extracts temporal velocity series from the time-Doppler spectrum and conducts comprehensive feature extraction from spatial–temporal data. Utilizing ECMWF data as a reference

the model predicts minimum peak distances to accurately identify wave crests and troughs while estimating the mean wave period on the basis of the fundamental wavelength-period relationship. First

temporal velocity series are derived from the time-Doppler spectrum. Next

features are extracted from the spatial–temporal velocity series

and a model is constructed in conjunction with European Centre for Medium-Range Weather Forecasts (ECMWF) data to predict the minimum peak distance

identifying the positions of the wave crests and troughs. Finally

the mean wave period is inferred using the relationship between wave wavelength and period

and the method is validated by simulation. In addition

an approximately three-day dataset collected with a coherent X-band radar deployed along the coast of Shandong Province in China is reanalyzed and used to retrieve mean wave period. Compared with the ECMWF data

the mean wave periods retrieved by the proposed method have root-mean-square differences (RMSDs) of 0.15 and 0.22 s for Horizontal-Horizontal and Vertical-Vertical polarizations

respectively

and also have correlation coefficients of 0.96 and 0.89

respectively. Validation of the method using simulations and applied to this dataset shows that the proposed method can invert wave parameters effectively and with high accuracy. Results indicate that the proposed method can achieve real-time estimation of wave parameters from the spatial–temporal velocity series with a reasonable performance. In conclusion

this study presents a new method for estimating mean wave periods that integrates radar data

machine learning

and ECMWF data

addressing the shortcomings of existing approaches. The ensemble model demonstrates excellent performance and generalization capability

making it effective for real-time wave monitoring and forecasting. Future research could focus on expanding the dataset and refining the model to enhance its predictive capabilities in diverse marine environments.

关键词

Keywords

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