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

arXiv: https://arxiv.org/abs/2102.02603 · HTML: https://mlsanity.com/paper/2102.02603/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [Cloud gap-filling with deep learning for improved grassland monitoring](https://mlsanity.com/paper/2403.09554/) (2024)
- [An attention mechanism based convolutional network for satellite precipitation downscaling over China](https://mlsanity.com/paper/2203.14812/) (2022)
- [Do Tensorized Large-Scale Spatiotemporal Dynamic Atmospheric Data Exhibit Low-Rank Properties?](https://mlsanity.com/paper/2507.03289/) (2025)
- [Spatio-Temporal Transformers for Long-Term NDVI Forecasting](https://mlsanity.com/paper/2602.01799/) (2026)
- [HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates](https://mlsanity.com/paper/2606.18115/) (2026)
- [Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction](https://mlsanity.com/paper/2608.02322/) (2026)

## Abstract (arXiv)

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.