Afforestation projects were applied in the Poyang Lake Basin of China at the beginning of 1980s. The large-scale plantation may dramatically influence the changes in carbon storage of forests in this basin. Therefore, climate-induced variations in the carbon balance of the Poyang Lake Basin＇s forests may play an important role in the carbon cycle of China. However, we have little understanding of their long-term behavior, especially the future trend of carbon sink/source patterns under climate change and rising atmospheric CQ. The annual carbon budget of the Poyang Lake Basin＇s forests during 1981-2050 was estimated by using the Integrated Terrestrial Ecosystem Carbon-budget model （InTEC） coupled with projected climate change simulated by Regional Integrated Environmental Model System （RIEMS 2.0）. During 1981-2000, the rapid increment of annual NPP in this basin was possible due to large plantation. Soil organic carbon storage （0-30cm） of forests generally decreased by 1.0% per year at the beginning of plantation. Moreover, forests in this basin converted from carbon source in 1980s to carbon sink in 1990s. By 2040-2050, total carbon stocks of forest ecosystems will increase by 0.78Pg C, compared to recent years （2001-2010）. Under future climate and CQ concentration in AIB scenario, NEP of forests in Poyang Lake Basin lean to keep relative stable （20-30Tg C y-i） because of old forests except for some years induced by extreme droughts. Our results also showed that prediction of NEP of forests in Poyang Lake Basin was controlled by water limitation; in contrast, temperature was the main factor on inter- annual variability of NPP.
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 relfectance （NIR） were consistent in three data subsets （subsets A-C）. For all subsets, CNC was signiifcantly positively correlated with NIR in the two transects （R2=0.29, 0.33 and 0.36, P＆lt;0.05 or P＆lt;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 signiifcant positive correlation with CNC in different subsets, but like NIR, it was also with low coefifcient of determination （R2=0.38, 0.20 and 0.17, P＆lt;0.01, respectively）. Also, the correlations between CNC and the entire spectrum reflectance （or its derivative and logarithmic transformation） by PLSR owned different signiifcance in various subsets. We did not ifnd the very robust relationship like previous literatures, so the data we used were checked again. The paired T-test was applied to estimate the inlfuence of inter-annual variability of FNC on the relationships between CNC and Hyperion data. The inter-annual mismatch between period of ifeldwork and Hyperion acquisition had no inlfuence 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 relationsh
