Canopy foliar Nitrogen Concentration (CNC) is one of the most important parameters influencing vegetation productivity in forest ecosystems. In this study, we explored the potential of imaging spectrometry (hyperspectral) remote sensing of CNC in conifer plantations in China's subtropical red soil hilly region. Our analysis included data from 57 field plots scattered across two transects covered by Hyperion images. Single regression and partial least squares regression (PLSR) were used to explore the relationships between CNC and hyperspectral data. The correlations between CNC and nearinfrared reflectance (NIR) were consistent in three data subsets (subsets A –C). For all subsets, CNC was significantly positively correlated with NIR in the two transects (R2=0.29, 0.33 and 0.36, P <0.05 or P <0.01, respectively). It suggested that the NIR-CNC relationship exist despite a weak one, and the relationship may be weakened by the single canopy structure. Besides, we also applied a shortwave infrared (SWIR) index—Normalized Difference Nitrogen Index (NDNI) to estimate CNC variation. NDNI presented a significant positive correlation with CNC in different subsets, but like NIR, it was also with low coefficient of determination (R2=0.38, 0.20 and 0.17, P <0.01, respectively). Also, the correlations between CNC and the entire spectrum reflectance (or its derivative and logarithmic transformation) by PLSR owned different significance in various subsets. We did not find the very robust relationship like previous literatures, so the data we used were checked again. The paired T-test was applied to estimate the influence of inter-annual variability of FNC on the relationships between CNC and Hyperion data. The inter-annual mismatch between period of fieldwork and Hyperion acquisition had no influence on the correlations of CNC-Hyperion data. Meanwhile, we pointed out that the lack of the canopy structure variation in conifer plantation area may lead to these weak relationships.
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