The Impact of Smoothing Techniques on Vegetation Phenology Extraction: A Case Study of Inner Mongolia Grasslands

The selection of data smoothing methods is one of the key steps in extracting land surface phenology parameters from time-series remote sensing data. However, existing studies often use default parameters for denoising the time-series data, neglecting the sensitivity of phenology extraction to different combinations of smoothing parameters. Therefore, this study systematically evaluated three parametric smoothing methods—Savitzky–Golay (SG), Whittaker Smoother (WS), and Harmonic Analysis of Time-Series (HANTS)—and two non-parametric methods—Asymmetric Gaussian (AG) and Double-Logistic (DL)—on the accuracy of Start of Season (SOS) and End of Season (EOS) extraction at eight ground phenology observation sites in Inner Mongolia, based on time-series MOD13Q1- Normalized Difference Vegetation Index data and using the derivative method as the background for phenology parameter extraction at the site scale. The results showed that (1) DL and HANTS yielded similar accuracy for phenology extraction in desert steppe, while parametric smoothing methods outperformed non-parametric methods in phenology simulation in typical and meadow steppe regions. (2) We proposed the optimal phenology parameter combination for different steppe types in Inner Mongolia. For desert steppe, DL or HANTS was recommended. For SOS extraction in typical steppe ecosystems, the WS parameter combination was used. For EOS and phenology in meadow steppe, the HANTS parameter combination yielded better simulation results. (3) In desert and meadow steppes, the window radius in SG contributed more to phenology accuracy than polynomial order. The opposite was true for typical steppe. In WS, the contribution of the differential order to SOS and EOS extraction in desert and typical steppes was higher than that of the smoothing factor. The opposite was observed in meadow steppe. In HANTS, the fitting tolerance error was the key factor controlling phenology extraction accuracy. (4) Based on the optimal phenology extraction scheme, the smallest extraction error occurred in meadow steppe at the site scale. This was followed by typical steppe. Desert steppe showed relatively larger errors. This study overcomes the reliance on default parameters in previous studies and proposes a practical framework for phenology extraction for different grassland ecosystems. The findings provide new empirical evidence for method selection and parameter setting in remote sensing phenology monitoring.