The Normalized Difference Vegetation Index (NDVI) is one of the most widely used remote sensing indicators for assessing vegetation status, with applications across a broad range of studies including phenology, ecology, and hydrology. However, NDVI data are usually degraded by clouds, snow cover, and other factors. In this study, we propose a novel spatiotemporal method—the Time-varying Graph Signal Reconstruction (TGSR) method—for the reconstruction of low-quality NDVI data. This method regards spatiotemporal NDVI data as time-varying graph signals and reconstructs them by maximizing the differential smoothness of the signal. We applied the TGSR method to sample patches (128 × 128 pixels) from MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m Grid products across East Asia, and quantitatively compared the results with those from two benchmark methods: the Savitzky-Golay (SG) filter method and the Spatiotemporal Tensor (ST-Tensor) method. Results demonstrate that the TGSR method outperforms both benchmark methods in capturing temporal dynamics and preserving spatial structure of NDVI data, achieving an average root mean square error of 0.0302, compared to 0.0354 for SG and 0.0331 for ST-Tensor. The TGSR method exhibits no bias toward noise type—whether positively biased, negatively biased, or missing data—and shows strong robustness against varying noise intensities, performing well even with only 15% reliable data. Moreover, the TGSR method effectively addresses challenges related to spatiotemporal continuous gaps and land cover changes. The present findings highlight the strong potential of the TGSR method for generating high-quality NDVI datasets and its applicability to similar remote sensing time-series products.
Chen et al. (2026) studied this question.