Comment on essd-2023-159

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Plant phenology refers to the cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is of great significance for understanding the response of ecosystems to global change and simulating the material and energy balance of terrestrial ecosystems. Based on 24552 in-situ phenology observation records of 24 typical woody plants from the Chinese Phenology Observation Network (CPON), we map the species phenology (SP) and ground phenology (GP) of forests over China from 1951&ndash;2020, with a spatial resolution of 0.1&deg; and a temporal resolution of 1 day. A model-based upscaling method was used to generate SP maps from in-situ SP observations, and then weighted average and quantile methods were used to generate GP maps from SP maps. The validation shows that the SP maps of 24 woody plants are largely consistent with the in-situ observations, with an average error of 6.9 days in spring and 10.8 days in autumn. The GP maps of forests have good agreement with the existing Land Surface Phenology (LSP) products derived by remote sensing data, particularly in deciduous forests, with an average difference of 8.8 days in spring and 15.1 days in autumn. The dataset provides an independent and reliable phenology data source on a long-time scale of 70 years in China, and contributes to more comprehensive research on plant phenology and climate change at regional and national scales. The dataset can be accessed at <a href="https://doi.org/10.57760/sciencedb.07995" target="_blank" rel="noopener">https://doi.org/10.57760/sciencedb.07995</a> (Zhu et al., 2023).