Abstract:

【Objective】Chlorophyll is a crucial indicator of plant physiological status. This study explores methods for estimating leaf chlorophyll content using measured chlorophyll fluorescence and Fluspect-B model simulation data.【Method】The study analyzes the measured data of chlorophyll fluorescence spectrum and chlorophyll content of leaves at various canopy heights of 11 typical tree species in Maoershan Forest Farm. Additionally, it utilizes a database that simulates the relationship between chlorophyll fluorescence spectrum and chlorophyll content across different tree species. Statistical models are developed using multiple linear regression, artificial neural networks, and random forest modeling techniques based on the measured data. A hybrid model that integrates simulated data and a hybrid model combining measured and simulated data are employed to estimate leaf chlorophyll content. Additionally, the distribution characteristics of leaf chlorophyll content across different tree species and canopy heights are analyzed.【Result】Among the statistical models, random forest exhibits the highest effectiveness, achieving an estimation accuracy of coefficient of determination (R²) was 0.830 5 and root mean square error (RMSE) was 7.109 8 for leaf chlorophyll content (μg/cm²). The hybrid model incorporating both measured and simulated data demonstrates superior accuracy compared to the statistical models, yielding R² of 0.913 3 and RMSE of 6.374 9 μg/cm², respectively. The fitting accuracy for chlorophyll content of broad-leaved trees generally surpasses that of coniferous trees, particularly for upper leaf datasets at different canopy positions, which show better fitting effects than middle and lower layers.【Conclusion】The mixed model utilizing both measured and simulated data outperforms the purely statistical model based only on measured data. The mixed model exhibits good fitting accuracy, enabling precise estimation of chlorophyll content. The method based on chlorophyll fluorescence spectrum data proves viable for estimating forest vegetation chlorophyll content, laying a foundational dataset for large-scale chlorophyll content estimation and forest ecosystem carbon sink research.

Key words: chlorophyll fluorescence, chlorophyll content, hybrid models, remote sensing inversion model, forest vegetation, carbon sink estimation