Cherry blossom and ginkgo leaf coloration phenology dataset of China from 2009 to 2019 extracted from big data

Ground‐based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species. This study used a logistic curve fitting method to extract phenological timing data of specific species from social media, based on which the timing data for spring flowering of cherry blossoms and autumn coloration of ginkgo in China were validated and published. This makes a significant contribution, as this dataset is an effective complement to the phenological data not available, and the method also provides a reference for obtaining phenological data for other specific species.