2026-10-12T04:49:06Z https://www.scidb.cn/oai

10.57760/sciencedb.29939 2026-07-02T16:07:10Z

2026-07-02 A vertically stratified leaf chlorophyll content dataset for broadleaf forests in China from 2024 to 2026 doi:10.57760/sciencedb.29939 en This dataset provides vertically stratified leaf chlorophyll content (LCC) measurements collected from 2024 to 2026 across three representative forest ecosystems in China, including a subtropical evergreen broadleaf forest, a temperate deciduous broadleaf forest, and a cold-temperate mixed coniferous–broadleaf forest. The dataset is designed to characterize within-canopy vertical variability in LCC across multiple forest types and environmental conditions.The dataset includes observations from 19 plots and multiple dominant tree species. Vertical canopy sampling was conducted using both fixed-platform and flexible field methods, enabling measurements across multiple canopy layers within each plot. For each vertical stratum, LCC values were derived from in situ SPAD measurements and laboratory spectrophotometric analysis, using species-specific or literature-based calibration relationships. In addition to LCC, the dataset provides detailed metadata including spatial coordinates, sampling date, species identity, canopy position, vertical stratification level, and leaf health status.A multi-temporal dataset is available for selected plots at the temperate site, enabling analysis of seasonal dynamics in canopy chlorophyll distribution. Comprehensive quality control procedures, including instrument calibration, replicate measurements, and outlier screening, were applied to ensure data reliability and consistency.This dataset can be used for studies of canopy vertical structure, vegetation physiological traits, forest ecosystem monitoring, and the calibration and validation of remote sensing products. It also provides benchmark data for radiative transfer modelling and multi-scale integration of canopy biochemical and structural information. Leaf Chlorophyll Content; Vertical stratification; Forest canopy; SPAD; Field spectroscopy; China; Pest and disease stress; Remote sensing validation; Vegetation monitoring Wangzehao Wu Hu Zhang Jing Li Qinhuo Liu EMBARGO https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank