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A precise estimate of canopy leaf nitrogen concentration (CNC, based on dry mass) is important for researching the carbon assimilation capability of forest ecosystems. Hyperspectral remote sensing technology has been applied to estimate regional CNC, which can adjust forest photosynthetic capacity and carbon uptake. However, the relationship between forest CNC and canopy spectral reflectance as well as its mechanism is still poorly understood. Using measured CNC, canopy structure and species composition data, four vegetation indices (VIs), and near-infrared reflectance (NIR) derived from EO-1 Hyperion imagery, we investigated the role of canopy structure traits and plant functional types (PFTs) in modulating the correlation between CNC and canopy reflectance in a temperate forest in northeast China. A plot-scale forest structure indicator, named broad foliar dominance index (BFDI), was introduced to provide forest canopy structure and coniferous and broadleaf species composition. Then, we revealed the response of forest canopy reflectance spectrum to BFDI and CNC. Our results showed that leaf area index had no significant effect on NIR ( P>0.05) but indicated that there was a significant correlation ( R2=0.76, P<0.0001) between CNC and BFDI. NIR had a more significant correlation with BFDI than with CNC for all PFTs, but it had no obvious correlation with CNC for single PFT. Partial correlation analysis showed that four VIs had better correlations with BFDI than with CNC. When the effect of BFDI was removed, the partial correlation between CNC and NIR was insignificant ( R=0.273, P>0.05). On the contrary, removing the CNC effect, the partial correlation between BFDI and NIR was positively significant ( R=0.69, P<0.0001). These findings proved that canopy structure and coniferous and broadleaf species composition had a greater influence on the remote sensing signal than canopy nitrogen concentration. The functional convergence of plant traits resulted in the relation of CNC and canopy structure and determined the positive correlation between CNC and NIR. We maintain that the repeatable relationship between CNC and NIR can be used in the remote sensing retrieval of CNC during various forest types. Nevertheless, the relationship cannot b
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