Supporting data for “Multi-scale fundamental phenology mechanism in response to global climate change”

For my PhD thesis, I have collected a range of data from various sources.1). I collected data from PhenoCam observations. The digital repeat photography, along with the Green Chromatic Coordinate (GCC), provides an accurate and quantitative means for monitoring plant phenology. Second, the PhenoCam Dataset v2.0 employs a standardized approach to preprocess the data across all selected sites in a consistent way with provided GCC metric on a daily basis and official phenometrics to indicate key transition dates on an annual basis, which helps to reduce uncertainties associated with data preprocessing and phenology extractions from PhenoCams.2). I utilized data from leaf unfolding data (LUD) data derived from the AVHRR and environmental variables from GLDAS. There are two main reasons for selecting this phenology dataset. First, by using the average of the three methods, the data has demonstrated significantly reduced uncertainty and improved consistency over time. Second, the dataset has shown improved accuracy when compared with ground and phenocam data.3). I utilized phenology records PEP725 such as leaf unfolding date (LUD), Temperature, Precipitation and etc. This comprehensive dataset allowed us to conduct a thorough investigation of the factors influencing leaf-out dates across various tree species and regions.Collecting data from forest sites, publicly available scientific datasets, and satellite imagery yielded a robust and comprehensive dataset for my PhD thesis, enabling a thorough analysis of the impacts of environmental change on forest ecosystems.

在撰写我的博士论文过程中，我搜集了来自不同来源的各类数据。首先，我收集了来自PhenoCam观测的数据。数字重复摄影技术以及绿色色度坐标（GCC）共同构成了监测植物物候的精确且定量的手段。其次，PhenoCam数据集v2.0采用了标准化的方法对所选站点进行数据预处理，确保了数据的一致性，并每日提供GCC指标以及年度官方物候学指标以指示关键转换日期，这有助于降低数据预处理和从PhenoCams中提取物候学信息的不确定性。其次，我利用了从AVHRR获取的叶片展开数据（LUD）以及GLDAS的环境变量。选择此物候数据集的主要原因是，通过三种方法的平均值，数据显著降低了不确定性，并提高了随时间推移的一致性。此外，与地面和PhenoCam数据相比，该数据集的准确性也得到了提升。再者，我使用了PEP725物候记录，如叶片展开日期（LUD）、温度、降水量等。这一综合性数据集使我们能够对影响不同树种和地区叶片展开日期的因素进行深入研究。从森林站点、公开的科学数据集和卫星图像中收集的数据，为我的博士论文提供了一组坚实且全面的数据集，从而能够对环境变化对森林生态系统的影响进行彻底分析。