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 Spatio-Temporal 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.

归一化差异植被指数 (NDVI) 时间序列数据的应用不可避免地受到云引起的间隙和噪声的阻碍。尽管已经开发了许多重建方法，但由于没有充分利用空间、时间和周期相关性，它们并没有有效地解决多云和多雨地区时间序列存在较大差距的问题。在本文中，提出了一种自适应时空张量完成方法（称为ST-Tensor）方法，通过同时充分利用多维时空信息来重建易云地区的长期NDVI时间序列。为此，通过考虑空间邻居、年际变化和周期特征之间的相关性，构建了一个高度相关的张量，为了通过自适应加权低秩张量完成模型重建丢失的信息。一个迭代 ℓ然后实施第_{1 种}趋势过滤方法以消除残留的时间噪声。这种新方法使用在东南亚大陆获得的 2001 年至 2018 年的 MODIS 16 天复合 NDVI 产品进行了测试，那里的多雨气候通常会导致数据出现较大的差距和噪声。定性和定量结果表明，ST-Tensor 方法在解决不同的缺失数据问题方面比之前的五种方法更有效，尤其是时间连续间隙和时空连续间隙。还表明 ST-Tensor 方法在跟踪 NDVI 季节性轨迹方面比其他方法表现更好，因此是为易云地区生成高质量长期 NDVI 时间序列的绝佳选择。