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.