Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth's vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the global scale, especially over the MODIS era (2001-present). Nevertheless, these products may obscure important finer scale spatial patterns and longer-term changes. Therefore, we have developed a Bayesian hierarchical model to retrieve complete annual sequences of LSP from Landsat imagery (1984-present), which has medium spatial resolution (30 m) but relatively sparse temporal frequency. Our approach uses Markov Chain Monte Carlo (MCMC) sampling to quantify individual phenometric uncertainty, which is especially important when considering long time series with variable observation quality and density, but has rarely been demonstrated. The estimated spring LSP had strong agreement with ground phenology records at Harvard Forest (R² = 0.87) and Hubbard Brook Experimental Forest (R² = 0.67). The estimated LSP were consistent with the recently released 30 m LSP product, MSLSP30NA, in its time period of 2016 to 2018 (R² of 0.86 and 0.73 for spring and autumn phenology, respectively). Our Bayesian hierarchical model is an important step forward in extending medium resolution LSP records back in time as it accomplishes both critical goals of retrieving annual LSP from sparse time series and accurately estimating uncertainty.

地表物候学（LSP）是气候变化对地球植被影响的一致且敏感的指标。现有的估计LSP的方法需要时间序列密度，直到最近，这些序列密度只能从粗略的空间分辨率图像中获得，例如MODIS（500 m）和AVHRR（1 km）。这些数据集的LSP产品提高了我们对全球范围内物候变化的理解，尤其是在MODIS时代（2001年至今）。但是，这些产品可能会掩盖重要的更精细尺度的空间格局和长期变化。因此，我们已经开发了一种贝叶斯分层模型，可以从Landsat影像（1984年至今）中检索LSP的完整年度序列，该序列具有中等的空间分辨率（30 m），但时间频率相对稀疏。我们的方法使用马尔可夫链蒙特卡洛（MCMC）采样来量化个体物候不确定性，这在考虑具有可变观测质量和密度的长时间序列时尤其重要，但很少得到证实。春季LSP的估计值与哈佛森林（R²  = 0.87）和哈伯德布鲁克实验林（R ²  = 0.67）。估计的LSP与2016年至2018年期间最新发布的30 m LSP产品MSLSP30NA一致（春季和秋季物候的R ^2分别为0.86和0.73）。我们的贝叶斯分层模型是向后扩展中分辨率LSP记录迈出的重要一步，因为它既完成了从稀疏时间序列中检索年度LSP并准确估计不确定性的两个重要目标。