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

IF 3.3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2023-12-10 DOI:10.1002/gdj3.231

Shenghong Wang, Haolong Liu, Xinyue Qin, Junhu Dai, Jun Liu

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引用次数: 0

Abstract

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.

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从大数据中提取的 2009 年至 2019 年中国樱花和银杏叶色物候数据集

地面物候观测数据是目前最准确的物候监测数据。有效利用社交媒体上的可用信息检索物候数据，对于缓解观测点缺失地区物候数据缺乏的问题具有重要价值。本研究开发了一种逻辑曲线拟合方法，从社交媒体数据中提取特定物种的物候数据。在验证了观测点数据与温度之间的关系后，重建并发布了中国两种典型物候现象的时序数据，即春季樱花开花和秋季银杏叶变色。数据可用期为 2010 年至 2019 年的 176 个城市和 2009 年至 2018 年的 155 个城市。该数据集是对现有物候数据的有效补充，该方法也为获取特定物种的物候数据提供了参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.