An advanced structure correction spectral model using UAV multispectral images and LiDAR-based crown boundaries for estimating crown leaf nitrogen concentration in subtropical Liriodendron sino-americanum plantation

Abstract

Since nitrogen (N) underpins plant vitality by forming proteins, nucleic acids, and chlorophyll, quantifying it through leaf nitrogen concentration (LNC, %) becomes pivotal for growth assessment and precision forestry. However, the three-dimensional structure of the canopy, crown shadow and other background factors complicate the estimation of LNC from crown bidirectional reflectance factor (BRF). To address these challenges, we employ canopy scattering coefficients (CSC) to analyze light behavior within canopies. Accurate estimation of N relies on the association of N with chlorophyll, dry matter, water and canopy structure. To improve the LNC prediction, we developed an enhanced spectral index model called the Difference combined Simple Ratio index (DSR), which improves LNC estimation by minimizing the effects of canopy structure and shadows. Results indicate that the shadow-filtering index methods effectively eliminated shadowed pixels, enhancing the correlation between single-band reflectance and LNC. The CSC-based DSR is the best crown-level LNC estimation for Liriodendron sino-americanum plantation (R² > 0.77, RMSE < 0.7). The estimation model exhibits significant potential for mapping crown-scale LNC distributions in Liriodendron sino-americanum plantations, as well as improving the understanding of the confounding effects of canopy structure and shadows on the LNC estimation.