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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 1-9     DOI: 10.6046/zrzyyg.2021071
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A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples
LI Weiguang1,2(), HOU Meiting3()
1. Hainan Meteorological Service, Haikou 570203, China
2. Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570203,China
3. China Meteorological Administration Training Centre, Beijing 100081, China
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Abstract  

Remote-sensing-based time series data of vegetation have been increasingly available with the accumulation of remote sensing data. These data are vital for ascertaining the changes in an ecosystem and analyzing relevant driving factors. However, some factors (e.g., cloud cover and instrument errors) restrict the observation quality of the vegetation products of remote sensing, creating data gaps in continuous and high-quality observation data. The data gaps can be filled based on the spatio-temporal dependence of the earth’s surface characteristics, which is called the spatio-temporal reconstruction of time series data. High-quality spatio-temporal reconstruction of time series data is an important prerequisite for the accurate extraction of changes in time series data. Taking the remote-sensing-based time series data of vegetation indices as examples, this study briefly reviewed the widely used reconstruction methods of time series data firstly. These methods generally include two steps: interpolation and smoothing. The interpolation can be divided into three major types, namely time-based, space-based, and spatio-temporal interpolation. Then, taking the simulated normalized vegetation index (NDVI) time series and actual GIMMS NDVI time series as examples, different proportions of data gaps in the two time series were created. Then, this study compared the effects of four types of data reconstruction methods (i.e., linear interpolation, singular spectrum analysis (SSA), Whittaker, and time series harmonic analysis (HANTS)) on the reconstruction results of the two time series. The results show that the four methods have their own advantages and disadvantages, and the Whittaker method showed relatively good performance overall. However, the performance of interpolation methods might vary within different regions, and thereby the data reconstruction methods need to be further verified.
Keywords remote sensing of vegetation      time series      data reconstruction      interpolation      smoothing     
ZTFLH:  TP79  
Corresponding Authors: HOU Meiting     E-mail: 163great@163.com;houmt@outlook.com
Issue Date: 14 March 2022
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Cite this article:   
Weiguang LI,Meiting HOU. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021071     OR     https://www.gtzyyg.com/EN/Y2022/V34/I1/1
Fig.1  Comparison of reconstruction results of simulated NDVI time series
RMSEall RMSEgap
数据缺失比例
/%		 线性插值 SSA Whittaker HANTS 线性插值 SSA Whittaker HANTS
10 <0.000 1 0.096 6 <0.000 1 0.045 6 0.012 6 0.105 4 0.000 8 0.061 2
20 <0.000 1 0.004 8 <0.000 1 0.045 4 0.014 7 0.004 0 0.000 8 0.067 7
30 <0.000 1 0.077 5 <0.000 1 0.043 6 0.015 0 0.085 3 0.001 0 0.073 1
40 <0.000 1 0.102 3 <0.000 1 0.023 8 0.024 1 0.110 0 0.002 4 0.049 2
Tab.1  Comparison of RMSE for different data reconstruction methods based on simulated NDVI time series
Fig.2  Comparison of different reconstruction methods for NDVI time series
Fig.3  Local detailed comparison of different reconstruction methods for NDVI time series
RMSEall RMSEgap
数据缺失比例
/%		 线性插值 SSA Whittaker HANTS 线性插值 SSA Whittaker HANTS
10 <0.000 1 0.034 1 0.007 8 0.044 4 0.048 2 0.052 3 0.044 0 0.055 5
20 <0.000 1 0.035 8 0.026 1 0.042 3 0.056 5 0.043 0 0.056 6 0.061 9
30 <0.000 1 0.034 0 0.000 9 0.042 3 0.058 4 0.046 1 0.060 4 0.057 3
40 <0.000 1 0.031 2 <0.000 1 0.040 4 0.061 0 0.045 3 0.066 0 0.061 2
Tab.2  Comparison of RMSE for different data reconstruction methods based on NDVI time series
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