Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (4): 737-748.doi: 10.12082/dqxxkx.2021.200669

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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)


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