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A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction

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  • Yangjian Zhang, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  • ,
  • Li Wang, Chinese Academy of Sciences
  • ,
  • Yuanhuizi He, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  • ,
  • Ni Huang, Chinese Academy of Sciences
  • ,
  • Wang Li
  • Shiguang Xu, Chinese Academy of Sciences
  • ,
  • Quan Zhou, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  • ,
  • Wanjuan Song, Chinese Academy of Sciences
  • ,
  • Wensheng Duan, China Aerospace Science and Industry Corporation
  • ,
  • Xiaoyue Wang, University of Chinese Academy of Sciences, The Key Laboratory of Land Surface Pattern and Simulation, CAS - Institute of Geographical Sciences and Natural Resources Research
  • ,
  • Shakir Muhammad, Institute of Space Technology
  • ,
  • Biswajit Nath, University of Chittagong
  • ,
  • Luying Zhu, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  • ,
  • Feng Tang, School of Land Science and Technology
  • ,
  • Huilin Du, Nanjing University
  • ,
  • Lei Wang, International Research Center of Big Data for Sustainable Development Goals
  • ,
  • Zheng Niu, Chinese Academy of Sciences, University of Chinese Academy of Sciences

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics ex-tractions.

Original languageEnglish
Article number2280
JournalRemote Sensing
Number of pages21
Publication statusPublished - May 2022

    Research areas

  • CCTM model, function-based model, time series compression, time series reconstruction, trend-fitting

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