Enhanced Spatial–Temporal Savitzky–Golay Method for Reconstructing High-Quality NDVI Time Series: Reduced Sensitivity to Quality Flags and Improved Computational Efficiency

Abstract

The Spatial–Temporal Savitzky–Golay (STSG) method for noise reduction can address the problem of temporally continuous Normalized Difference Vegetation Index (NDVI) gaps and effectively increase local low NDVI values without overcorrection. However, STSG largely depends on the quality flags of the NDVI time-series data, and inaccurate quality flags yield misleading final results. STSG also requires extensive computing time when used in large-scale applications. This study proposes an enhanced method, called cuSTSG, to address the aforementioned limitations of STSG. First, cosine similarities between the annual NDVI time series were used to identify and exclude the NDVI values with inaccurate quality flags from the NDVI seasonal growth trajectory. Second, computational performance was improved by reducing redundant computations and parallelizing computationally intensive procedures using the Compute Unified Device Architecture (CUDA) on graphics processing units (GPUs). Experiments on four MODIS NDVI time-series datasets of various sizes and regions showed that compared with the original STSG, cuSTSG reduced the mean absolute errors of the final products by 4.90%, 7.77%, 11.76% and 2.06%, respectively. The results also showed that cuSTSG on a GPU achieved 75+ speed-up compared with the Interactive Data Language-implemented STSG, and 30+ speed-up compared with the C++-implemented STSG. cuSTSG can effectively mitigate the impacts of inaccurate quality flags on final products and generate high-quality NDVI time series at large scales with high accuracy and performance. The source code of cuSTSG is available at https://github.com/HPSCIL/cuSTSG.