## 1 Introduction
A recent study [13] has demonstrated that chlorophyll content, light use efficiency, and chlorophyll fluorescence narrowband indices have more advantages for monitoring GPP seasonal variations than NDVI and EVI broadband indices associated with the structure of canopy in an evergreen canopy with small physiological changes. Hyperspectral remote sensing advances have allowed the development of smaller, more stable, accurately calibrated sensors that can quantify biophysical properties on the basis of more exact spectral absorbing and scattering characteristics of the land surface [22, 23]. Hyperspectral vegetation indices (HVIs) take advantage of fine scale spectral sampling to develop a narrowband equivalent of a broadband VI. A potentially greater contribution of hyperspectral systems is their ability to create new indices that incorporate wavelengths not sampled by any broadband system and to quantify absorptions that are specific to important biochemical and/or biophysical quantities of vegetation [24]. This technology advancement provides some opportunities for assessing additional narrowband indices, including PRI (photochemical reflectance index), NDNI (normalized difference nitrogen index), a series of related biochemical component and red edge indices (e.g., leaf chlorophyll index, chlorophyll vegetation index, canopy chlorophyll content index, and D\({}_{\max}\), the maximum derivative of reflectance in 650-750 nm). The hyperspectral data are primarily derived from space-borne sensors (e.g., EO-1 Hyperion) [25, 26], airborne sensors (e.g., AVIRIS, Carnegie Airborne Observatory AToMS) [27, 28], and ground vegetation spectral measurements collected from FluxNet EC towers [29]. Although it is underutilized in the monitoring of C fluxes, hyperspectral remote sensing has much to offer toward the improved accuracy of global carbon exchange models, e.g., monitoring light use efficiency (LUE) [30]. The Hyperion Imaging Spectrometer aboard the EO-1 spacecraft [31] focuses on hyperspectral data for assessing land cover/land use, mineral resources, coastal processes, and earth/atmospheric processes that are widely applied in many aspects of ecology. Hyperion data have been used for mapping plant canopy biochemistry (e.g., nitrogen concentration) [32]. It has also been employed to test indices for estimating basal area and canopy cover of savannah woodland in central Brazil [33], and to analyze the variation in chlorophyll content and LAI with bands from the red edge of the vegetation spectrum [34]. Hyperion data are also being used to monitor ecosystem C fluxes. Earth Observing System (EOS) sites provide key data for monitoring properties of major ecosystem types associated with high spectral resolution measurements (\(\leq\)10 nm, 400-2500 nm) to provide synoptic evaluation of the factors significantly affecting the ability of the vegetation to sequester C and to reflect radiation due to changes in vegetation pigment and water content, structural and chemical composition. Preliminary results suggest a strong correlation between CO\({}_{2}\) fluxes and a number of bio-indicators associated with pigment content, regardless of vegetation type and site [35]. During the previous two decades, there has been an increase in the number of global CO\({}_{2}\) flux sites conducting continuous year-round monitoring. Scaling-up fluxes from site-specific tower measurements to regional spatial scales has been the focus of methods development for remote sensing and mathematical modeling. Measurements from EC flux towers are likely to have a C flux estimate bias when they are scaled up to regional and global assessments. Although broadband indices have been employed in large-scale studies, they do not consistently provide detailed information of spectra and spatial considerations. Meanwhile, narrowband bio-indicators need to be investigated for their utility in monitoring ecosystem features [36].