A Vertical-Layered Leaf Chlorophyll Content (LCC) Dataset for Forest Canopies in China

This dataset provides field measurements of vertical stratification of leaf chlorophyll content (LCC) in typical forest ecosystems in China. It aims to support research on the vertical-layered distribution of chlorophyll within the canopy in fields such as ecology, agronomy, and remote sensing. Data collection took place during the 2024 and 2025 growing seasons (specific months based on actual records). Three forest type study areas were selected: temperate deciduous broadleaf forest in Huailai, Hebei; subtropical evergreen broadleaf forest in Duanzhou, Guangdong; and cold-temperate mixed forest in Antu, Jilin. Multiple plots were established in each study area, and all plots were geolocated using RTK positioning with an accuracy of better than 1 meter. The dataset generation process strictly adheres to standardized field sampling and laboratory analysis procedures. For field sampling, two vertical sampling methods were designed to accommodate the canopy structures of different tree species: (1) a fixed-plot sampling method using scaffolding; and (2) a flexible sampling method using high-reach pruners and ladders. For each leaf sample, information such as Species, Height_Above_Ground, and Healthy_Status was recorded (see the dataset table for details). After collection, the leaves were transported to the laboratory, where LCC was determined using spectrophotometry. Fresh leaf samples were extracted with ethanol, and a LAMBDA 25 spectrophotometer was used to measure absorbance at 470 nm, 646 nm, 663 nm, and 665 nm. Chlorophyll a and b contents were calculated using the Arnon formula and summed to obtain LCC (unit: μg/cm²). Each sample was measured in triplicate, and the average value was taken. The relative standard deviation was calculated to assess repeatability (see the technical validation section for error analysis). Concurrently, SPAD values were measured on the same leaves using a handheld SPAD-502 chlorophyll meter to construct an LCC-SPAD conversion model. For some sampling sites where a spectrophotometer was unavailable, SPAD values were measured in the field and converted to chlorophyll content (LCC) using empirical relationships from the literature corresponding to the site’s climate zone. The dataset is in Microsoft Excel format (.xlsx) and is compatible with common data analysis software. The worksheet “Vertical_LCC” contains a total of 245 stratified LCC records. Each row represents a single observation, and the column labels and their meanings are as follows:Plot_ID: Unique plot identifier;Longitude: Longitude in the WGS84 coordinate system, unit: degrees;Latitude: Latitude in the WGS84 coordinate system, unit: degrees;Sampling_Date: Sampling date for the sample, format MM/DD/YYYY;Species: Plant species name, using Latin names;Vertical_Stratum: Vertical stratum of the sample, increasing from bottom to top; herbaceous plants or shrubs growing under the canopy are designated as stratum 0;Height_Above_Ground: Height of the leaf above ground, unit: meters (m), accuracy 0.1 m;LCC: Leaf chlorophyll content, unit: micrograms per square centimeter (μg/cm²);Canopy_Position: Orientation of the sampled leaf within its canopy, e.g., E, S, W, N;Healthy_Status: Health status of the sampled leaf; healthy leaves are left blank, while stressed leaves are recorded as Unhealthy;Stress_Type: The specific type of stress affecting unhealthy leaves, such as pest or disease types;Remarks: Notes providing additional explanations for abnormal conditions or sampling details. Based on these data, linear or nonlinear regression methods were used to fit LCC-SPAD conversion models for different tree species (see Table 1 in the article for model parameters and accuracy). These models can be used to convert SPAD rapid measurements to LCC. Error analysis showed that the coefficient of determination (R²) for all models was greater than 0.7, and the residual distribution was uniform. An evaluation of spectrophotometer repeatability errors showed that the coefficient of variation (CV) for three measurements on the same leaf was less than 2% on average, indicating good repeatability of the measurement results. In summary, this dataset provides high-quality, systematic LCC data on the vertical stratification of the forest canopy. It features a reasonable sampling method and distinct stratification effects, and is accompanied by conversion functions to assist data processing. It can serve as critical foundational data for forest productivity modeling, remote sensing product validation, and pest and disease monitoring research. The dataset has undergone rigorous quality control and the error range is within acceptable limits. Users may utilize the data directly as needed or refer to the methods described in this paper for further analysis.