The land surface phenology (LSP) associated with vegetation dynamics plays an important role in influencing the land surface processes and land–atmosphere interactions. Satellite observations have been widely used in studies for the monitoring of LSP across large areas based on different phenology retrieval algorithms and the routine production of LSP from remote sensing data has yet come to fruition. Here we used six phenology retrieval methods, including the amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), relative changing rate (RCR), third-order derivative (TOD), and curvature change rate (CCR), to retrieve the start of the growing season (SOS) and the end of the growing season (EOS) from the Advanced Very High Resolution Radiometer (AVHRR) data. We improved the curve fitting method to reduce uncertainties owing to data preprocessing. The results indicated that both SOS and EOS retrieved by six different methods had similar spatial distribution and the retrieved dates could vary largely at the pixel level. In the Northern Hemisphere, from 1982 to 2018, the trends of SOS retrieved vary across methods and only the EOS extracted by the relative change curvature method had a significant advanced trend. In the Southern Hemisphere, from 1982 to 2018, SOS results derived from four methods (i.e., AT, SOD, TOD, and CCR) showed significantly delayed trends, EOS results extracted by all the methods demonstrated insignificant trends. The phenology retrieval methods were assessed using the field observation data from the Pan European Phenology Project (PEP725) and from time series of leaf area index (LAI) measured at flux towers. The satellite-retrieved dates of both SOS and EOS were positively correlated with field observation and the relationships are largely dependent on how field phenology metrics are defined. We presented longer time series (1982–2018) data of phenology metrics with fewer gaps and multiple phenology retrieving methods as compared to the MODIS land cover dynamics product. Based on our assessments, one might use the SOS generated by FOD and the EOS generated by RCR as they provide results the most consistent with field data among all the tested methods. If studies aim to use the earliest SOS (or latest EOS) in a year, one might use the data retrieved based on TOD or CCR. The global dataset is delivered for uses in studies and applications associated with LSP.

与植被动态相关的地表物候 (LSP) 在影响地表过程和陆地 - 大气相互作用方面起着重要作用。卫星观测已广泛用于基于不同物候检索算法的大面积LSP监测研究，并且从遥感数据中常规生产LSP尚未取得成果。这里我们使用了六种物候检索方法，包括幅度阈值（AT）、一阶导数（FOD）、二阶导数（SOD）、相对变化率（RCR）、三阶导数（TOD）和曲率变化率 (CCR)，从高级甚高分辨率辐射计 (AVHRR) 数据中检索生长季节的开始 (SOS) 和生长季节的结束 (EOS)。我们改进了曲线拟合方法，以减少数据预处理带来的不确定性。结果表明，通过六种不同方法检索到的 SOS 和 EOS 具有相似的空间分布，并且检索到的日期在像素级别可能会有很大差异。在北半球，从1982年到2018年，不同方法检索到的SOS趋势不同，只有相对变化曲率法提取的EOS有显着的提前趋势。在南半球，从1982年到2018年，AT、SOD、TOD、CCR等4种方法的SOS结果呈现显着延迟趋势，所有方法提取的EOS结果均呈现不显着趋势。物候检索方法使用来自泛欧洲物候学项目 (PEP725) 的实地观察数据和在通量塔测量的叶面积指数 (LAI) 时间序列进行评估。SOS 和 EOS 的卫星检索日期与实地观察呈正相关，这种关系在很大程度上取决于如何定义实地物候指标。与 MODIS 土地覆盖动态产品相比，我们提供了更长的时间序列（1982-2018）物候指标数据，具有更少的差距和多种物候检索方法。根据我们的评估，可以使用 FOD 生成的 SOS 和 RCR 生成的 EOS，因为它们提供的结果与所有测试方法中的现场数据最一致。如果研究旨在使用一年中最早的 SOS（或最新的 EOS），人们可能会使用基于 TOD 或 CCR 检索到的数据。全球数据集用于与 LSP 相关的研究和应用。