  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 and temporal
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
the region of 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.
