Phenology curve estimation via a mixed model representation of functional principal components: Characterizing time series of satellite-derived vegetation indices
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基于函数主成分混合模型的物候曲线估计
- 研究问题
- 如何从多年卫星植被指数序列提取像元层面的代表性年度物候曲线，并据此确定返青、成熟、衰老和休眠等关键事件？
- 方法与思路
- 作者先以谐波回归平滑年度序列，再用层次聚类形成近同质组，拟合基于函数主成分的混合模型，并用微积分准则提取物候参数。
- 材料陈述的贡献
- 摘要提出代表性物候曲线及事件参数的估计流程，并称墨西哥保护区应用支持其刻画草地和农业区近周期植被动态的能力。
- 阅读关注点
- 核对年度平滑、同质组选择和主成分混合模型的衔接，以及事件判定准则、估计不确定性和非近周期植被的适用表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核验；摘要未提供误差指标与不确定性结果，近周期案例表现不能推及所有植被。
原文摘要
Vegetation phenology consists of studying synchronous stationary events, such as the vegetation green up and leaves senescence, that can be construed as adaptive responses to climatic constraints. In this paper, we propose a method to estimate the annual phenology curve from multi-annual observations of time series of vegetation indices derived from satellite images. We fitted the classical harmonic regression model to annual-based time series in order to construe the original data set as realizations of a functional process. Hierarchical clustering was applied to define a nearly homogeneous group of annual (smoothed) time series from which a representative and idealized phenology curve was estimated at the pixel level. This curve resulted from fitting a mixed model, based on functional principal components, to the homogeneous group of time series. Leveraging the idealized phenology curve, we employed standard calculus criteria to estimate the following phenological parameters (stationary events): green up, start of season, maturity, senescence, end of season and dormancy. By applying the proposed methodology to four different data cubes (time series from 2000 to 2023 of a popular satellite-derived vegetation index) recorded across grasslands, forests, and annual rainfed agricultural zones of a Flora and Fauna Protected Area in northern Mexico, we verified that our approach characterizes properly the phenological cycle in vegetation with nearly periodic dynamics, such as grasslands and agricultural areas. The R package sephora was used for all computations in this paper.