太阳成8722(中国)有限公司-GREEN NO.1

标题: Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion

作者: Chu, D (Chu, Dong); Shen, HF (Shen, Huanfeng); Guan, XB (Guan, Xiaobin); Chen, JM (Chen, Jing M.); Li, XH (Li, Xinghua); Li, J (Li, Jie); Zhang, LP (Zhang, Liangpei)

来源出版物: REMOTE SENSING OF ENVIRONMENT 卷: 264 文献号: 112632 DOI: 10.1016/j.rse.2021.112632 出版年: OCT 2021

摘要: The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical correlations. In this paper, an adaptive SpatioTemporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative l1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.

入藏号: WOS:000688182900001

语言: English

文献类型: Article

作者关键词: NDVI time series; Gap filling; Low-rank tensor completion; Spatio-temporal information; Time-series filtering; MODIS NDVI

地址: [Chu, Dong; Shen, Huanfeng; Guan, Xiaobin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Shen, Huanfeng; Zhang, Liangpei] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Guan, Xiaobin; Chen, Jing M.] Univ Toronto, Dept Geog & Planning, Toronto, ON M5S 3G3, Canada.

[Chen, Jing M.] Fujian Normal Univ, Sch Geog Sci, Fuzhou 350117, Peoples R China.

[Li, Xinghua] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.

[Li, Jie] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China.

[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

通讯作者地址: Guan, XB (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: guanxb@whu.edu.cn

影响因子：10.164