## 1 Introduction  
Forest ecosystems play an important role in decreasing atmospheric CO\({}_{2}\) concentrations and mitigating global climate change [1, 2]. The carbon (C) sequestration by terrestrial ecosystems and the prediction of climate change impacts on the C balance in China have been estimated using inventory and modeling methodologies during the previous two decades at the national, regional, and biome spatial scales. C sequestration in forest ecosystems has been estimated at 3.26 to 9.11 Pg C (with a mean of 5.49 Pg C) [3]. In order to improve the accuracy of China's terrestrial C sequestration and storage estimates a combination of national inventory field plot measurements, point data from eddy covariance C flux tower stations, spatial scaling utilizing remote sensing techniques, and ecosystem model simulations were identified as means to fully account for the C budget. Forest ecosystem C fluxes have been measured using several methods, including closed chambers, a global network of eddy covariance (EC) techniques at the stand spatial scale [4, 5], and aircraft and spaceborne remote sensing platforms and sensors. Forest C fluxes have been spatially applied using these methods from individual canopies to ecosystem and regional scales. The linkage of forest ecosystem fluxes measured with EC methods and satellite remote sensing data are some of the most promising methods for scaling up the spatially limited coverage of eddy covariance sites in China that will facilitate the development of regional estimates of C fluxes [6, 7]. Remote sensing techniques provide a useful methodology to study patterns in ecosystem processes at different spatiotemporal scales [8]. Methodologies proposed for regional monitoring of vegetation C fluxes rely on vegetation indices (bio-indicators), which are related to tree canopy structure and physiology. Historically, bio-indicators were derived from broadband remote sensing data including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). These indices were developed to avoid saturation at larger leaf area index (LAI) values and to be resistant to atmospheric effects [9]. NDVI, EVI, and other multispectral vegetation indices were proposed in several studies as a proxy for LAI, canopy structure, green biomass, percent green cover, and the fraction of absorbed photosynthetically active radiation (_f\({}_{\text{APAR}}\)_) [10, 11, 12, 13]. The estimation of ecosystem C fluxes utilizing a data-driven [14] and satellite remote sensing approach was previously used to extend the net ecosystem exchange (NEE) for 1 km by 1 km cells across the conterminous U.S. for a 10-year period from 2000 to 2009 [15]. Global terrestrial C fluxes have also been up-scaled using a machine learning technique model tree ensembles (MTE), based on climate and meteorological data, land use data, and remote sensing indices. Trained MTEs were used to develop global flux fields at a 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) spatial resolution and a monthly temporal resolution from 1982 to 2008 [16]. Cross validation analyses demonstrated good performance of MTEs in predicting among site flux variability and seasonal patterns. Correlations between NEE and broadband vegetation indices (e.g., NDVI) were not as robust [17, 18]. These results indicate that utilizing remote sensing techniques to estimate NEE is difficult using broadband indices, and that gross primary production (GPP) is more important to inter annual variability in NEE than ecosystem respiration (Re). The GPP from Moderate Resolution Imaging Spectrometer (MODIS) has been evaluated using regional tower EC flux observations in North America. Spring season estimates of GPP were poorly correlated to tower EC flux data owing to the relatively rapid onset of leaf-out [19]. Several previous studies have shown that vegetation indices based on narrow spectral bands significantly improve the prediction of vegetation biophysical characteristics by reducing the saturation of spectral indices at high biomass values [20, 21].