地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (4): 737-748.doi: 10.12082/dqxxkx.2021.200669

• 遥感科学与应用技术 • 上一篇    下一篇

改进DINEOF算法在中国渤海叶绿素a遥感缺失数据重构中的应用研究

张志恒(), 章超, 孟麟, 唐凯, 朱红春*()   

  1. 山东科技大学测绘科学与工程学院,青岛 266590
  • 收稿日期:2020-11-05 修回日期:2021-01-08 出版日期:2021-04-25 发布日期:2021-06-25
  • 通讯作者: 朱红春
  • 作者简介:张志恒(1995— ),男,山东泰安人,硕士生,主要从事遥感影像云覆盖区域数据恢复的研究。E-mail: nyzhangzhiheng@163.com
  • 基金资助:
    国家自然科学基金项目(41971339);国家重点研发计划项目(2018YFC1407605);山东科技大学科研创新团队支持计划项目(2019TDJH103)

Application of Improved DINEOF Algorithm in the Reconstruction of Missing Remote Sensing Data of Chlorophyll a in the Bohai Sea, China

ZHANG Zhiheng(), ZHANG Chao, MENG Lin, TANG Kai, ZHU Hongchun*()   

  1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  • Received:2020-11-05 Revised:2021-01-08 Online:2021-04-25 Published:2021-06-25
  • Contact: ZHU Hongchun
  • Supported by:
    National Natural Science Foundation of China(41971339);National Key Research and Development Program of China(2018YFC1407605);Shandong University of Science and Technology Research Innovation Team Support Program(2019TDJH103)

摘要:

叶绿素a(chl-a)是重要的海洋环境水色参数,但是受云雾覆盖的影响,卫星遥感chl-a产品中普遍存在数据缺失的现象,大大降低了数据的应用效果。经验正交函数插值方法(DINEOF)是目前在长时间序列缺失数据重构方面应用最广泛的数据插值重构方法,本研究针对DINEOF方法在空间小尺度上过度平滑的缺陷,设计了一种面向渤海chl-a的分层重构方法(SDS-DINEOF),该方法重点考虑了渤海chl-a分布呈现近岸高、中部低的规律,将渤海海域等距离分为32个区域,对位于每个区域的子数据集分别进行重构;利用该方法对2019年全年每日10时13分的渤海GOCI卫星chl-a产品进行了重构,并将其重构结果与DINEOF重构结果进行了对比分析。分析结果表明:应用SDS-DINEOF方法相比DINEOF方法,chl-a重构精度和时间效率上均得到了提升,其中整体精度提高了3.52%,重构时间节约了125%,尤其是在距离陆地最远的渤海中部区域,应用该方法重构精度提升最为显著。本文取得的研究结果对于海洋遥感数据产品的质量提高和应用效率的提升,具有较为重要的理论意义和实际应用价值。

关键词: 卫星遥感, 渤海, GOCI, chl-a产品, 缺失率, 重构, DINEOF, SDS-DINEOF, 分层

Abstract:

Chlorophyll a (chl-a) is an important parameter of water color in marine environment. Quantitative observation from satellite remote sensing is the main way to obtain large-scale oceanic chl-a. However, due to the influence of cloud and fog coverage, data missing is a common phenomenon in satellite remote sensing chl-a products, which greatly reduces the application effect of data. The data-interpolating empirical orthogonal function (DINEOF) method is currently the most widely used data interpolation and reconstruction method for time series data. In this study, a layered reconstruction method for Bohai Sea chl-a (SDS-DINEOF) is designed to address the shortcoming of excessive smoothing at fine scales using the DINEOF method. This method utilizes the distributional characteristics of Bohai chl-a, that is high values in the nearshore and low values in the central water, and divides the Bohai Sea into 32 equidistant regions. We reconstruct the missing data of the daily Bohai GOCI satellite chl-a products at 10:13 a.m in each region separately using this method. The reconstructed results were compared with the DINEOF results. The results show that: (1) Both the SDS-DINEOF and the DINEOF methods can reconstruct missing data completely. The reconstruction results of the former have more detailed information than the latter. The seasonal average results of SDS-DINEOF show that the value of chl-a in the Bohai coastal waters is generally high, while the value of central water is low in summer and autumn, and high in winter and spring; (2) The SDS-DINEOF has a higher overall reconstruction accuracy and reconstruction efficiency compared with the DINEOF. The average accuracy is increased by 3.52%, and the reconstruction time is saved by 125%. The reconstruction accuracy of each layered region has been improved, with the most significant improvement in the 31st and 32nd floors located at the central water in Bohai Sea. The reconstruction accuracy of most single images in the time series has been improved. The reconstruction results of chl-a images in July and August 2019 are significantly improved; (3) As the data missing rate increases, the reconstruction accuracy and efficiency of DINEOF and SDS-DINEOF will both decrease, though the reconstruction accuracy and efficiency of the SDS-DINEOF method are always higher than that of the DINEOF method; (4) During the construction process, the interpretation rate of the data and the reconstruction time by the best model of each layer are restricted and affected by the reconstructed sub-data set itself. The results obtained in this paper have important theoretical and practical significance for improving the quality of marine remote sensing data products.

Key words: satellite remote sensing, Bohai Sea, GOCI, chl-a products, missing ratel, reconstruction, DINEOF, SDS-DINEOF, layer