在全球气候变化的大环境下，植被物候变化的监测以及物候对气候因子的敏感性分析至关重要。当前大部分利用遥感手段对植被物候的研究集中在中国北方和高海拔地区，对中国南方地区的植被物候研究相对较少。中国南方多云、植被异质性高、常绿落叶林混合等特点给植被物候遥感探测带来了挑战。本研究将以中国南方多云区域的自然植被为研究对象，力图取得以下突破：（1）开发适合多云地区的Landsat高质量时间序列数据的方法，包括云和阴影自动探测技术和云污染像元修复技术；（2）开发稳健的基于Landsat时间序列的植被物候参数探测技术，并结合地面数据讨论遥感物候探测技术在中国南方不同植被类型中的精度和可行性；（3）定量分析中国南方植被物候对不同气候因子的响应和敏感性。

Under global climate change, it is important to monitor vegetation phenology change and analyze its relationship with climate variability. Most of the current studies using remote sensing focused on deciduous forests in north China or high altitude regions. On the contrary, the studies of vegetation phenology in south China is very limited because of the challenges from frequent clouds, high heterogeneity, and the mixture of deciduous and evergreen trees. To fill this gap, this project will explore the feasibility of remote sensing data to detect vegetation phenology in south China. It aims to: (1) develop a new technology to reconstruct high-quality Landsat time-series for cloudy regions; (2) develop a robust method to detect vegetation phenology metrics and use ground-based measurement to evaluate the accuracy of phenology detection; and (3) quantify how vegetation phenology in south China interacts with climate change and analyze its sensitivity to different climate change magnitudes.