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A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction

文献类型：期刊论文

作者Zhang, Yangjian5,6,9; Wang, Li9; He, Yuanhuizi6,9; Huang, Ni9; Li, Wang7,9; Xu, Shiguang9; Zhou, Quan6,9; Song, Wanjuan9; Duan, Wensheng8; Wang, Xiaoyue6,10
刊名REMOTE SENSING
出版日期2022-05-01
卷号14期号:9页码:21
关键词CCTM model function-based model time series reconstruction time series compression trend-fitting
DOI10.3390/rs14092280
通讯作者Wang, Li(wangli@radi.ac.cn)
英文摘要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 extractions.
WOS关键词HARMONIC-ANALYSIS ; NDVI DATA ; DATA SET ; QUALITY ; MODIS ; SATELLITE ; INDEX ; EXTRACTION ; DIFFERENTIATION ; GENERATION
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030404] ; National Natural Science Foundation of China[41871347] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2018084]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000796159000001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/177285]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Li
作者单位1.Univ Chittagong, Dept Geog & Environm Studies, Chittagong 4331, Bangladesh
2.China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China
3.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China
4.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
5.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Aarhus Univ, Ctr Biodivers Dynam Changing World BIOCHANGE, Ny Munkegade 114, DK-8000 Aarhus C, Denmark
8.Beijing Inst Radio Measurement, Beijing 100854, Peoples R China
9.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
10.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yangjian,Wang, Li,He, Yuanhuizi,et al. A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction[J]. REMOTE SENSING,2022,14(9):21.
APA Zhang, Yangjian.,Wang, Li.,He, Yuanhuizi.,Huang, Ni.,Li, Wang.,...&Niu, Zheng.(2022).A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction.REMOTE SENSING,14(9),21.
MLA Zhang, Yangjian,et al."A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction".REMOTE SENSING 14.9(2022):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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