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Article(id=1322959383059104049, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322959347470442879, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.03.122, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1710950400000, receivedDateStr=2024-03-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793347696318, onlineDateStr=2026-10-30, pubDate=1739548800000, pubDateStr=2025-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793347696318, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793347696318, creator=13701087609, updateTime=1793347696318, updator=13701087609, issue=Issue{id=1322959347470442879, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='2', pageStart='111', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='1739548800000', pubDateStr='2025-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793347687833, creator='13701087609', updateTime=1793347687833, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=125, endPage=130, ext={EN=ArticleExt(id=1322959383201710386, articleId=1322959383059104049, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Analysis on Noise Characteristics of Sea Surface Height Time Series and Sea Level Changes Based on Satellite Altimetry Data, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Using the BIC_tp noise model identification criterion, we analyzed the temporal noise characteristics of sea surface height (SSH) and their influencing factors at 350 virtual coastal satellite altimetry stations worldwide. Furthermore, we explored the impact of environmental loads on SSH time series and the characteristics of sea level changes across different time spans. The results indicate that the assumptions of FN+WN, FN+RW+WN, and PL+WN noise models can lead to errors in estimating the velocity parameters of SSH time series. Furthermore, there is a diversity of SSH time series noise models, and the velocity uncertainties under different noise models vary significantly. Identifying their noise characteristics is crucial for accurately obtaining sea level change parameters. The average global sea level rise rates for the different time spans of 1993 to 2006, 2007 to 2020, and 1993 to 2020 are 2.59 mm/a, 4.41 mm/a, and 3.08 mm/a, respectively, indicating that sea level is accelerating its rise. The optimal noise models and velocity changes for the SSH time series before and after environmental loading correction are not significant, indicating that the load effect has a relatively minor impact on the characteristics of SSH time series.

, authors=Shengbo YANG1, Xiaoxing HE1, *, Shunqiang HU2, Jiahui HUANG1, Jie WANG1, authorsList=Shengbo YANG, Xiaoxing HE, Shunqiang HU, Jiahui HUANG, Jie WANG, authorCompany=null, correspAuthors=Xiaoxing HE, authorNote=

YANG Shengbo, postgraduate, majors in multi-source satellite ocean environment stereoscopic observation, E-mail:

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采用BIC_tp噪声模型辨识准则分析全球350个沿海卫星测高虚拟站的海面高时序噪声特性及影响因素, 探讨环境负载对海面高时序的影响及不同时间跨度下海平面变化特性。结果表明, FN+WN、FN+RW+WN、PL+WN噪声模型假设会导致海面高时序速度参数的估计错误, 且海面高时序噪声模型存在多样性, 不同噪声模型下海面高时序的速度不确定度差异较大, 辨识其噪声特性是准确获取海平面变化参数的关键。1993—2006年、2007—2020年、1993—2020年不同时间跨度下的全球海平面平均上升速度分别为2.59 mm/a、4.41 mm/a、3.08 mm/a, 且海平面在加速上升。环境负载修正前后海面高时序的最优噪声模型与速度变化不显著, 说明负载效应对海面高时序特性的影响较小。

, authors=杨圣博1, 贺小星1, *, 胡顺强2, 黄佳慧1, 王杰1, authorsList=杨圣博, 贺小星, 胡顺强, 黄佳慧, 王杰, authorCompany=null, correspAuthors=贺小星, authorNote=

杨圣博, 硕士生, 主要研究方向为多源卫星海洋环境立体观测, E-mail:

, correspAuthorsNote=

贺小星, 博士, 副教授, 主要研究方向为卫星大地测量与海洋卫星遥感, E-mail:

HE Xiaoxing, PhD, associate professor, majors in satellite geodesy and marine satellite remote sensing, E-mail:

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YANG Shengbo, postgraduate, majors in multi-source satellite ocean environment stereoscopic observation, E-mail:

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journalId=1281212831689347082, articleId=1322959383059104049, language=CN, label=图7, caption=1993—2020年海面高时序速度空间分布, figureFileSmall=p5f6ygEB0PFmw+DysYI+QA==, figureFileBig=sEQwIvG9NwEouP7gBqIlFQ==, tableContent=null), ArticleFig(id=1322959391300911484, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=EN, label=Tab. 1, caption=

Comparison of velocity uncertainty under various noise models

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指标 [0, 0.1) [0.1, 0.2) [0.2, +∞)
|ARFIMA-ARMA| 20.3% 12.8% 66.9%
|ARFIMA-GGMWN| 19.7% 13.7% 66.6%
|ARMA-GGMWN| 88.9% 7.7% 3.4%
), ArticleFig(id=1322959391380603261, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=CN, label=表1, caption=

不同噪声模型下速度不确定度比较结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 [0, 0.1) [0.1, 0.2) [0.2, +∞)
|ARFIMA-ARMA| 20.3% 12.8% 66.9%
|ARFIMA-GGMWN| 19.7% 13.7% 66.6%
|ARMA-GGMWN| 88.9% 7.7% 3.4%
), ArticleFig(id=1322959391443517822, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=EN, label=Tab. 2, caption=

Statistical of the optimal noise models

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噪声模型 海面高时序数/个 比例/%
ARFIMA 194 55.4
ARMA 90 25.7
GGMWN 66 18.9
), ArticleFig(id=1322959391510626687, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=CN, label=表2, caption=

最优噪声模型统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
噪声模型 海面高时序数/个 比例/%
ARFIMA 194 55.4
ARMA 90 25.7
GGMWN 66 18.9
), ArticleFig(id=1322959391577735552, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=EN, label=Tab. 3, caption=

Statistics of the optimal noise models after loading correction

, figureFileSmall=null, figureFileBig=null, tableContent=
噪声模型 海面高时序数/个 比例/%
ARFIMA 192 54.9
ARMA 99 28.2
GGMWN 59 16.9
), ArticleFig(id=1322959391674204545, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=CN, label=表3, caption=

负载修正后最优噪声模型统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
噪声模型 海面高时序数/个 比例/%
ARFIMA 192 54.9
ARMA 99 28.2
GGMWN 59 16.9
), ArticleFig(id=1322959391745507714, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=EN, label=Tab. 4, caption=

Sea level rise velocity in different periods

, figureFileSmall=null, figureFileBig=null, tableContent=
时间 速度/mm·a-1
1993—2006年 2.59
2007—2020年 4.41
1993—2020年 3.08
), ArticleFig(id=1322959391804227971, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322959383059104049, language=CN, label=表4, caption=

不同时段下全球海平面上升速度

, figureFileSmall=null, figureFileBig=null, tableContent=
时间 速度/mm·a-1
1993—2006年 2.59
2007—2020年 4.41
1993—2020年 3.08
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基于卫星测高数据的海面高时序噪声特性及海平面变化分析
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杨圣博 1 , 贺小星 1, * , 胡顺强 2 , 黄佳慧 1 , 王杰 1
大地测量与地球动力学 | 地球物理解释 2025,45(2): 125-130
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大地测量与地球动力学 |地球物理解释 2025 , 45 (2) : 125 -130
基于卫星测高数据的海面高时序噪声特性及海平面变化分析
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YANG Shengbo, postgraduate, majors in multi-source satellite ocean environment stereoscopic observation, E-mail:

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杨圣博, 硕士生, 主要研究方向为多源卫星海洋环境立体观测, E-mail:

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杨圣博1 , 贺小星1, * , 胡顺强2, 黄佳慧1, 王杰1
作者信息
  • 1 江西理工大学土木与测绘工程学院, 江西省赣州市客家大道1958号, 341000
  • 2 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌市紫阳大道99号, 330022
通讯作者:

贺小星, 博士, 副教授, 主要研究方向为卫星大地测量与海洋卫星遥感, E-mail:

HE Xiaoxing, PhD, associate professor, majors in satellite geodesy and marine satellite remote sensing, E-mail:

作者简介:

杨圣博, 硕士生, 主要研究方向为多源卫星海洋环境立体观测, E-mail:

Analysis on Noise Characteristics of Sea Surface Height Time Series and Sea Level Changes Based on Satellite Altimetry Data
Shengbo YANG1 , Xiaoxing HE1, * , Shunqiang HU2, Jiahui HUANG1, Jie WANG1
Affiliations
  • 1 School of Civil and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, 1958 Kejia Road, Ganzhou 341000, China
  • 2 Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, 99 Ziyang Road, Nanchang 330022, China

    • YANG Shengbo, postgraduate, majors in multi-source satellite ocean environment stereoscopic observation, E-mail:

出版时间: 2025-02-15 doi: 10.14075/j.jgg.2024.03.122
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摘要
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采用BIC_tp噪声模型辨识准则分析全球350个沿海卫星测高虚拟站的海面高时序噪声特性及影响因素, 探讨环境负载对海面高时序的影响及不同时间跨度下海平面变化特性。结果表明, FN+WN、FN+RW+WN、PL+WN噪声模型假设会导致海面高时序速度参数的估计错误, 且海面高时序噪声模型存在多样性, 不同噪声模型下海面高时序的速度不确定度差异较大, 辨识其噪声特性是准确获取海平面变化参数的关键。1993—2006年、2007—2020年、1993—2020年不同时间跨度下的全球海平面平均上升速度分别为2.59 mm/a、4.41 mm/a、3.08 mm/a, 且海平面在加速上升。环境负载修正前后海面高时序的最优噪声模型与速度变化不显著, 说明负载效应对海面高时序特性的影响较小。

卫星测高  /  海面高时间序列  /  噪声特性  /  海平面变化  /  环境负载

Using the BIC_tp noise model identification criterion, we analyzed the temporal noise characteristics of sea surface height (SSH) and their influencing factors at 350 virtual coastal satellite altimetry stations worldwide. Furthermore, we explored the impact of environmental loads on SSH time series and the characteristics of sea level changes across different time spans. The results indicate that the assumptions of FN+WN, FN+RW+WN, and PL+WN noise models can lead to errors in estimating the velocity parameters of SSH time series. Furthermore, there is a diversity of SSH time series noise models, and the velocity uncertainties under different noise models vary significantly. Identifying their noise characteristics is crucial for accurately obtaining sea level change parameters. The average global sea level rise rates for the different time spans of 1993 to 2006, 2007 to 2020, and 1993 to 2020 are 2.59 mm/a, 4.41 mm/a, and 3.08 mm/a, respectively, indicating that sea level is accelerating its rise. The optimal noise models and velocity changes for the SSH time series before and after environmental loading correction are not significant, indicating that the load effect has a relatively minor impact on the characteristics of SSH time series.

satellite altimetry  /  sea surface height time series  /  noise characteristics  /  sea level change  /  environmental loading
杨圣博, 贺小星, 胡顺强, 黄佳慧, 王杰. 基于卫星测高数据的海面高时序噪声特性及海平面变化分析. 大地测量与地球动力学, 2025 , 45 (2) : 125 -130 . DOI: 10.14075/j.jgg.2024.03.122
Shengbo YANG, Xiaoxing HE, Shunqiang HU, Jiahui HUANG, Jie WANG. Analysis on Noise Characteristics of Sea Surface Height Time Series and Sea Level Changes Based on Satellite Altimetry Data[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (2) : 125 -130 . DOI: 10.14075/j.jgg.2024.03.122
正文
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海岸带具有丰富的生态资源与先天区位优势，在全球经济与科技发展中具有重要作用[1]。由于气候变化引起的海平面加速上升，导致海岸带侵蚀等自然灾害日益加剧[2-3]。因此，准确获取沿海区域海面高变化参数，对进一步了解海平面变化趋势及进行灾害预警具有重要意义[4]。传统海平面变化监测通过验潮站获取高精度的水位观测值，但验潮站分布稀疏且不均，同时受地壳垂向运动影响，无法准确获取绝对海平面变化参数[5]。此外，随着空间大地测量技术的快速发展，传统观测手段在全球覆盖、数据自动化处理等方面面临许多挑战。卫星测高技术的发展为海平面变化监测提供了新思路，相较于验潮站，卫星测高观测不受地壳垂向运动的影响，具有范围覆盖全球、数据连续性好、自动化程度高等优势[6-7]。目前卫星测高已提供超过25 a的海平面观测数据，为海平面变化趋势研究及动态监测提供了重要保障[8]。
研究表明，基于卫星测高数据获取的海面高时间序列中存在有色噪声，会影响海平面速度相关参数的估计[9-12]。但这些研究中采用的模型较单一，且对海面高时序的噪声特性有待进一步研究。本文采用多种组合噪声模型分析卫星测高的全球海面高时间序列噪声特性，探讨环境负载效应对卫星测高数据的影响，并分析不同时段下海平面变化特征。
1 数据与方法
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1.1 卫星测高数据
本文采用哥白尼海洋环境监测服务(CMEMS)发布的数据产品GLORYS12V1分析全球350个卫星测高虚拟站[13]测高数据，时间跨度为1993—2021年，时间分辨率为1 d，空间分辨率为0.083°×0.083°。
1.2 环境负载数据
采用GFZ提供的环境负载模型[14]，包括非潮汐大气负载(NTAL)、非潮汐海洋负载(NTOL)、水文负载(HYDL)、海平面负载(SLEL)，空间分辨率为0.5°×0.5°，时段选取与卫星测高数据一致，其中NTAL和NTOL时间分辨率为3 h，HYDL和SLEL时间分辨率为24 h。为保证时间分辨率统一，将NTAL和NTOL时间分辨率统一转化为24 h。
1.3 海面高序列噪声模型及速度估计
海面高时间序列中包含的随机误差的统计性质可通过不同噪声模型进行描述。本文采用分数阶自回归滑动平均模型(ARFIMA)、自回归滑动平均模型(ARMA)、闪烁噪声+随机漫步噪声+白噪声模型(FN+RW+WN)、闪烁噪声+白噪声模型(FN+WN)、高斯马尔可夫噪声+白噪声模型(GGM+WN)、幂律噪声+白噪声模型(PL+WN)，通过功率谱密度(PSD)和改进贝叶斯信息准则(BIC_tp) 确定海面高时序的最优噪声模型。为方便阅读，下文省略后4种模型中“+”。
频域分析能确定时序中各频率分量随时间的变化情况，有助于分析时间序列的噪声特性[15]。利用PSD分析海面高时序的频域特征[10]：
$P(f)=P_0\left(f / f_0\right)^k$
式中，f为频率，P0和f0为归一化常数，k为噪声的谱指数。通过绘制PSD图分析不同噪声模型的频谱特征，初步评估噪声模型与海面高时序的拟合情况。考虑到数据窗效应及功率谱分析在低频部分的分辨率较低，为进一步准确辨识海面高时序的最优噪声模型，本文采用BIC_tp准则对噪声模型进行估计[16]，表达式为：
$\mathrm{BIC}_{-} \mathrm{tp}=-2 \lg (L)+\lg \left(\frac{n}{2 \pi}\right) v$
式中，L为似然函数，n为时间序列长度，v为模型未知参数个数。
噪声模型辨识是描述海面高时序的噪声性质和减少海平面变化速度估计误差的重要因素。为顾及海面高时序噪声模型的特性，海平面变化速度采用Bos等[17]提出的线性拟合方法进行估计，不同噪声模型下速度估计的不确定度为[18]：
$\begin{gathered}m_v= \\\pm \sqrt{\frac{A_{\mathrm{PL}}^2}{\Delta T^{2-\frac{k}{2}}} \frac{\varGamma(3-k) \varGamma(4-k)(N-1)^{k-3}}{\left[\varGamma\left(2-\frac{k}{2}\right)\right]^2}}\end{gathered}$
式中，N为时间序列长度，ΔT为时序数据的时间分辨率，Γ为伽马函数，k为谱指数，APL为时序的有色噪声振幅。采用Hector软件对海面高时序进行解算，以获取海平面变化速度及不确定度。
2 卫星测高数据处理与结果分析
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2.1 卫星测高数据预处理
为提高卫星测高数据质量，数据分析前采用四分位距法(IQR)去除粗差造成的异常值[19]，计算式如下：
$\mathrm{IQR}=Q_3-Q_1$
异常值检测范围为：
$\left[Q_1-3 \times \mathrm{IQR}, Q_3+3 \times \mathrm{IQR}\right]$
式中，IQR为四分位间距，Q3和Q1分别为海面高时序中残差时序的3/4和1/4分位数，用3倍四分位距剔除时序中的异常值[20]。
2.2 卫星测高序列噪声特性分析
分别采用6种噪声模型对350个海面高时间序列进行分析。图1为海面高时序的残差序列功率谱估计结果(以6个时序为例)，可以看出，ARFIMA、ARMA、GGMWN三种噪声模型的拟合情况更优，FNWN、FNRWWN、PLWN模型无法完全拟合数据的变化情况，尤其在低频区域，与数据实际特性存在较大偏差，会影响速度参数估计的可靠性。
速度不确定度是评估速度可靠性的重要因素，进一步分析不同噪声模型背景下海平面变化的速度不确定度，有助于评估噪声模型对海面高时序的适用性。图2为ARFIMA、ARMA、GGMWN三种噪声模型下速度不确定度的统计结果，其中ARMA、GGMWN两种噪声模型下有82.9%和84.3%的时序速度不确定度小于0.4 mm/a，ARFIMA模型下有76.9%的时序速度不确定度小于1 mm/a；而FNRWWN、FNWN、PLWN三种噪声模型下存在速度不确定度异常，导致对速度不确定度的估计错误，这与PSD图拟合情况一致。
综合不同噪声模型下功率谱密度与速度不确定度分析结果可知，FNRWWN、FNWN、PLWN三种噪声模型不适用于海面高时序，而ARFIMA、ARMA、GGMWN三种噪声模型能更准确地反映海面高时序的噪声特性。
分析这3种噪声模型对海平面变化速度及速度不确定度的影响。图3(单位mm/a)为不同噪声模型下速度差值的绝对值统计结果，其中82%和81.1%的时序在ARFIMA与ARMA、GGMWN模型下的速度差值小于0.2 mm/a，99.7%的时序在ARMA与GGMWN模型下的速度差值小于0.2 mm/a，说明噪声模型对海平面变化速度的估计影响较小。表1(单位mm/a)为不同噪声模型下速度不确定度差值的绝对值统计结果，可以看出，ARFIMA与ARMA、GGMWN模型的速度不确定度相差较大，其中66.9%、66.6%的时序速度不确定度差值大于0.2 mm/a，表明不同噪声模型对海面高时序不确定度的影响较大。
为准确得出海平面变化速度，采用BIC_tp估计准则分析不同海面高时序的最优噪声模型，表2为最优噪声模型统计结果。由表2可知，350个海面高时间序列中，最优噪声模型表现为ARFIMA、ARMA、GGMWN的时序分别占55.4%、25.7%、18.9%，表明不同海面高时序的最优噪声模型存在差异。图4为最优噪声模型的空间分布情况，可以看出，海面高时序的最优噪声模型无明显空间分布特征，其潜在影响因素有待进一步研究。
2.3 环境负载效应对海面高的影响分析
从卫星测高获取的海面高时序中去除环境负载模型对应的位移时序，可有效修正环境负载对海面高时序的影响。表3为修正后的海面高时序最优噪声模型统计结果，对比表2和3可知，环境负载修正后，ARFIMA模型占比下降0.5%、ARMA模型占比上升2.5%、GGMWN模型占比下降2%，最优噪声模型比例改变较小。
经统计，负载修正后25个海面高时序的最优噪声模型发生变化，结果无显著规律，其中12个时序的最优噪声模型由ARFIMA变为ARMA，5个时序的最优噪声模型由ARMA变为ARFIMA或GGMWN。分析表明，绝大部分海面高时序的最优噪声模型在负载修正前后未发生改变，环境负载对准确估计卫星测高海面高时序噪声模型的影响较小。
统计海面高时序在负载修正前后速度差的绝对值，结果见图5(单位mm/a)。由图可知，负载修正前后，74.6%的时序速度变化在0.2 mm/a以内，仅有5.1%的时序速度变化超过0.4 mm/a，表明环境负载对海平面变化速度估计的影响较小。
2.4 基于卫星测高的海平面变化分析
为研究不同时间跨度下海平面变化情况和时序噪声特性，将卫星测高获取的28 a海面高时间序列分为1993—2006年和2007—2020年两个时段，并进行最优噪声模型分析，结果见图6。对比图6与表2可知，相较于28 a时序，在1993—2006年与2007—2020年期间，最优噪声模型为ARFIMA的时序数量降幅较大，分别下降4.6%和10%，而最优噪声模型为ARMA的时序数量上升6.6%和10.6%，表明时间跨度对海面高时序最优噪声模型具有一定影响。
进一步分析不同时段下海平面变化情况。根据350个海面高时序的年均上升速度估计全球海平面上升速度，由表4可知，1993—2006年和2007—2020年全球海平面上升速度分别为2.59 mm/a和4.41 mm/a，2007—2020年海平面上升速度是1993—2006年的1.7倍，表明全球海平面长期呈上升状态，且上升速度在加快；1993—2020年全球平均海平面上升速度为3.08 mm/a，这与已有研究结果基本吻合。
图7为1993—2020年海面高时序速度的空间分布，由图可知，不同区域的海平面上升速度不同，存在空间分布差异。
通过不同区域的海面高时序估计区域海平面上升速度，其中欧洲区域的海平面上升速度较慢，为2.79 mm/a，北美东海岸和南美东海岸的海平面上升速度较快，分别为3.14 mm/a和3.50 mm/a。
3 结语
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本文以350个近岸卫星测高虚拟站的海面高时序数据为研究对象，采用6种噪声模型系统分析了海面高时序的噪声模型特性，研究了不同时段下海平面变化情况，同时探讨了环境负载位移时序对海平面变化速度估计的影响，得到以下结论：
1) 结合功率谱密度与BIC_tp估计准则，系统分析了卫星测高海面高时序的最优噪声模型特性及不同噪声模型的适用性，结果表明，FNRWWN、FNWN、PLWN噪声模型不适用于卫星测高海面高时序；海面高时序的最优噪声模型具有多样性，1993—2020年的最优噪声模型主要为ARFIMA(约占55.4%)、ARMA(约占25.7%)、GGMWN(约占18.9%)。
2) 不同噪声模型的选取对海平面变化速度估计的影响相对较小，但对海平面变化速度不确定度估计的影响较大，因此选择最优噪声模型是准确获取速度参数的关键。选取最优噪声模型需充分考虑海面高时序噪声的多样性，针对不同海面高时序，采用BIC_tp估计准则从ARFIMA、ARMA、GGMWN模型中选取最优的噪声模型。
3) 利用卫星测高获取的海面高时序数据估计全球海平面上升速度，1993—2006年、2007—2020年、1993—2020年全球海平面平均上升速度分别为2.59 mm/a、4.41 mm/a、3.08 mm/a，海平面在加速上升。
4) 环境负载对海面高时序的最优噪声模型影响很小，92.9%的海面高时序最优噪声模型在负载修正前后不变，且负载修正前后海面高时序速度的变化不大，74.6%的海面高时序速度变化小于0.2 mm/a。
基金
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  • 江西省主要学科学术和技术带头人培养计划(20225BCJ23014)
  • 国家自然科学基金(42104023)
  • 国家自然科学基金(42364002)
  • 江西省重点研发计划(20243BBI91033)
文献
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2025年第45卷第2期
25
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文章信息
doi: 10.14075/j.jgg.2024.03.122
  • 接收时间：2024-03-21
  • 首发时间：2026-10-30
  • 出版时间：2025-02-15
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  • 收稿日期：2024-03-21
基金
Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province(20225BCJ23014)
江西省主要学科学术和技术带头人培养计划(20225BCJ23014)
National Natural Science Foundation of China(42104023)
国家自然科学基金(42104023)
National Natural Science Foundation of China(42364002)
国家自然科学基金(42364002)
Key Research and Development Program of Jiangxi Province,(20243BBI91033)
江西省重点研发计划(20243BBI91033)
作者信息
    1 江西理工大学土木与测绘工程学院, 江西省赣州市客家大道1958号, 341000
    2 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌市紫阳大道99号, 330022

通讯作者:

贺小星, 博士, 副教授, 主要研究方向为卫星大地测量与海洋卫星遥感, E-mail:

HE Xiaoxing, PhD, associate professor, majors in satellite geodesy and marine satellite remote sensing, E-mail:

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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2024.03.122
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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