论文标题
贝叶斯分层模型，以估算用统一的Landsat 8和Sentinel-2图像估算土地表面物候参数
A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images
论文作者
论文摘要
我们开发了贝叶斯陆地物候（LSP）模型，并使用从统一的Landsat Sentinel-2（HLS）数据集中得出的增强植被指数（EVI）观察结果检查其性能。在先前的工作的基础上，我们提出了一个双重逻辑功能，一旦贝叶斯模型内的couch，就会为所有LSP参数提供后验分布。我们评估了正常，截短的正常和β可能性的疗效，以提供强大的LSP参数估计。提出了两个案例研究，并用于探索所提出模型的各个方面。第一个是通过HLS瓷砖内的森林像素进行的，探讨了长期平均LSP参数点和不确定性估计的可能性和时空变化的HLS数据可用性的选择。第二个是在年度时间步长以HLS瓷砖内的一小部分感兴趣的区域进行的，进一步研究了样本量和可能性的选择对LSP参数估计的影响。结果表明，虽然当植被指数有界时，在理论上优先截断了正常和β的可能性，但是当指数观察的数量足够大，并且值不在索引结合附近时，所有三个可能性都相似。两种案例研究都表明，如何使用像素级LSP参数后分布通过随后的分析来传播不确定性。作为本文的伴侣，我们提供了一个开源\ r软件包\ pkg {rsbayes}以及用于重现分析结果的补充数据和代码。所提出的模型规范和软件实现可在大量的栅格时间序列数据集中在像素级的Pixel级别上提供计算上有效的，统计上稳健的LSP参数后分布。
We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP parameters. We assess the efficacy of the Normal, Truncated Normal, and Beta likelihoods to deliver robust LSP parameter estimates. Two case studies are presented and used to explore aspects of the proposed model. The first, conducted over forested pixels within a HLS tile, explores choice of likelihood and space-time varying HLS data availability for long-term average LSP parameter point and uncertainty estimation. The second, conducted on a small area of interest within the HLS tile on an annual time-step, further examines the impact of sample size and choice of likelihood on LSP parameter estimates. Results indicate that while the Truncated Normal and Beta likelihoods are theoretically preferable when the vegetation index is bounded, all three likelihoods performed similarly when the number of index observations is sufficiently large and values are not near the index bounds. Both case studies demonstrate how pixel-level LSP parameter posterior distributions can be used to propagate uncertainty through subsequent analysis. As a companion to this article, we provide an open-source \R package \pkg{rsBayes} and supplementary data and code used to reproduce the analysis results. The proposed model specification and software implementation delivers computationally efficient, statistically robust, and inferentially rich LSP parameter posterior distributions at the pixel-level across massive raster time series datasets.
