Vegetation phenology can be extracted from vegetation index (VI) time series of satellite data. The maximum value composite (MVC) procedure and smoothing filters have been conventionally used as standard methods to exclude noises in the VI time series before extracting the vegetation phenology [e.g., National Aeronautics and Space Administration (NASA) VNP22Q2 and United States Geological Survey (USGS) MCD12Q2 phenology products]. However, it is unclear how to optimize the MVC and smoothing filters to produce the most accurate phenology metrics given that cloud frequency varies spatially. This study designed two simulation experiments, namely (1) using only the MVC and (2) using the MVC and smoothing filters together to smooth the enhanced vegetation index (EVI) time series for detecting spring phenology, i.e., start of season (SOS), over the northern hemisphere (north of 30°N) on a 5° × 5° grid cell basis by the inflection point and relative threshold algorithms. The results revealed that (1) the inappropriate selection of MVC periods (e.g., too short or too long) affected the accuracy of the SOS extracted by both phenology detection algorithms; (2) a filtering process with optimal parameters can reduce the effects of the MVC period on SOS extraction to a considerable extent, i.e., 65% and 61% for iterative Savitzky–Golay (SG) and penalized cubic splines (SP) filters, respectively; (3) optimal parameters for both the MVC and smoothing filters showed significant spatial heterogeneity; and (4) validation with ground PhenoCam data indicated that optimal parameters of the MVC and smoothing filters can produce more accurate results than official vegetation phenology products that use uniform parameters. Specifically, the R² values of the NASA product and the USGS product were 0.58 and 0.67, which were increased to 0.70 and 0.81, respectively, by the optimal smoothing process. Optimal parameters of the MVC and smoothing filters provided by this study in each 5° × 5° sub-region may help future studies to improve the accuracy of phenology detection from satellite VI time series.

植被物候可以从卫星数据的植被指数 (VI) 时间序列中提取。在提取植被物候之前，最大值复合 (MVC) 程序和平滑滤波器一直被用作标准方法来排除 VI 时间序列中的噪声 [例如，美国国家航空航天局 (NASA) VNP22Q2 和美国地质调查局 (USGS) ) MCD12Q2 物候产物]。然而，鉴于云频率在空间上变化，目前尚不清楚如何优化 MVC 和平滑滤波器以产生最准确的物候指标。本研究设计了两个模拟实验，即（1）仅使用MVC和（2）同时使用MVC和平滑滤波器来平滑增强植被指数（EVI）时间序列，用于检测春季物候，即季节开始（SOS） , 通过拐点和相对阈值算法在 5° × 5° 网格单元基础上在北半球（30°N 以北）上空。结果表明：（1）MVC周期选择不当（如过短或过长）影响了两种物候检测算法提取的SOS的准确性；(2) 具有最优参数的滤波过程可以在相当程度上降低MVC周期对SOS提取的影响，即迭代Savitzky-Golay(SG)和惩罚三次样条(SP)滤波器分别降低65%和61% ; (3) MVC 和平滑滤波器的最佳参数显示出显着的空间异质性；(4) 地面 PhenoCam 数据的验证表明，MVC 和平滑滤波器的最佳参数可以产生比使用统一参数的官方植被物候产品更准确的结果。具体来说，NASA 产品和 USGS 产品的R ²值分别为 0.58 和 0.67，通过优化平滑过程分别增加到 0.70 和 0.81。本研究在每个 5° × 5° 子区域提供的 MVC 和平滑滤波器的最佳参数可能有助于未来的研究提高卫星 VI 时间序列物候检测的准确性。