静止轨道海洋水色成像仪(Geostationary Ocean Color Imager, GOCI)提供了时间分辨率达小时级的海洋水色数据,使得对海洋环境的逐时变化监测成为可能。然而受到海洋上空云、雾和霾的影响,数据出现连续高缺失率甚至完全缺失的情况,使得数据使用价值大大降低。在经验正交函数重构法(Data INterpolating Empirical Orthogonal Functions, DINEOF)的基础上,突出时间要素在重构中的地位,运用异常像元检测、拉普拉斯平滑滤波和时间模态2次分解插值,提出了适用于静止海洋水色卫星数据的重构方法——DINEOF-G。利用此方法对杭州湾2017年的GOCI总悬浮物质量浓度数据进行重构,结果表明该方法相比经典方法在重构精度上提高了8%,数据重构率提高了36%,且重构结果较好地反映了杭州湾总悬浮物质量浓度的季节变化规律和空间分布特征。
Abstract
Geostationary Ocean Color Imager (GOCI) provides ocean water color data with time resolutions up to the hour, making it possible to monitor the marine environment in a time-varying manner. However, due to the influence of clouds, fog and haze over the ocean, the continuous high-missing rate or even complete loss of data makes the data use value greatly reduced. Based on the Data INterpolating Empirical Orthogonal Functions, highlighting the position of time elements in reconstruction, a reconstruction method using abnormal pixel detection, Laplacian smoothing and temporal coefficient twice decomposition interpolation was proposed for geostationary satellite ocean color data (DINEOF-G). This method is used to reconstruct the total suspended matter data of Hangzhou Bay in 2017. The results show that the proposed method improves the reconstruction accuracy by 8% and the data reconstruction rate by 36% compared with the classical method. The reconstructed data reflects the seasonal variation and spatial distribution characteristics of the total suspended matter in Hangzhou Bay.
关键词
数据重构 /
DINEOF /
GOCI /
杭州湾 /
总悬浮物质量浓度
Key words
data reconstruction /
DINEOF /
GOCI /
Hangzhou Bay /
total suspended matter
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基金
国家自然科学基金项目资助(41671391,41701436);海洋公益性行业科研专项经费资助(201505003)
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