Local Peak Savitzky–Golay for Spatio-Temporal Reconstruction of Landsat NDVI Time Series: A Case Study Over the Qinghai–Tibet Plateau

The incompleteness of the normalized difference vegetation index (NDVI) time series (TS) restricts its expanded applications in key domains. Although spatio-temporal hybrid methods show promise in TS reconstruction, reliance on auxiliary data in most existing approaches introduces errors and increases workload. Furthermore, NDVI values marked as contaminated in the quality assessment (QA) data are underutilized. Ultimately, when utilizing spatial information, most methods are ineffective for the representation of land-use changes. Considering these issues, we propose a local peak Savitzky–Golay (LPSG) method for spatio-temporal reconstruction of Landsat NDVI TS. First, we construct a local peak neighborhood weighted interpolation (LPNWI) method that fully utilizes all original values to fill gaps. Second, we design a slope change decision tree (SC-DT) method for identifying residual noise, thereby mitigating the impact of QA errors on reconstruction results. Third, multidimensional calibration with weighted spatial reference (MDC-WSR) method is proposed to enhance utilization of spatial information by improving traditional correlation coefficient calculations and generating a multiyear spatial reference, which effectively reflects land-use changes. Experiments on Landsat NDVI TS data in the Qinghai–Tibet Plateau (2013–2022) show that: 1) LPSG outperforms other methods in mitigating the impact of QA errors, preserving TS peaks and details, and maintaining spatial continuity; 2) LPSG exhibits superior performance, with average RMSE reductions ranging from 0.00018 to 0.00750 compared to other methods under both correct and incorrect QA; and 3) LPSG demonstrates good robustness under various gap conditions and effectively restores TS of pixels affected by land-use changes.