## 5 Conclusions  
In this study, we conducted the WOR AGB estimates using vegetation indices (VIs) that are derived from canopy hyperspectral data at different growth stages (seeding stage, bolting stage, flowering stage, and pod stage). The different fertilizer-N gradients field experiments were conducted in two consecutive WOR growing season spanning from September 2014 to May 2015 in Wuxue city and September 2015 to May 2016 Shayang city. Correlation analyses were performed between canopy hyperspectral data and AGB. The linear and non-linear (logarithmic, parabolic, power, and exponential) regression statistical models simulated the quantitative relation between VIs and AGB at different growth stages.  
Through comprehensive analysis of the AGB estimation models, the results demonstrated that WOR AGB could be accurately estimated by canopy hyperspectral data at different growth stages. CIred edge had relatively higher precision for AGB estimation at the vegetative stage, but it was not effective in estimating AGB at the reproductive stage, because of the disturbance of flowers and pods on visible bands. NDVI (800, 1200) retained good robustness at the vegetative stage and reproductive stage, it could reduce the influence of flower and pod in estimating AGB, the model 37 based on NDVI (800, 1200) significantly improved AGB estimation accuracy (R\({}^{2}\) > 0.80, RMSE < 1530 kg/hm\({}^{2}\), RPD > 2.3) without saturation phenomenon at total for four stages. We concluded that NDVI (800, 1200) could be used to accurately estimate WOR AGB at different growth stages. Moreover, it was vital to pay more attention to near-infrared (NIR) bands that could represent the phenology of vegetation growth [33]. Selecting suitable VIs and modeling algorithms could significantly affect the success of AGB estimation of the crops with variable plant morphology [59], such as winter oilseed rape.  
Although the appropriate VIs and effective wavelengths that are described in this paper were constructed using the data collected in different years, growth stages, and fertilizer-N level; it is worth noting that this newly developed estimation based on empirical models might be site-specific, and more field studies are needed to examine their robustness of other oilseed rape cultivars under a wider range of conditions. Future work will focus on exploring new vegetation indices and wavelengths for improving the accurate and robust estimation of large-scale WOR AGB, and combining different platforms that carried various remote sensing sensors for precision agriculture, including ground platform, unmanned erial vehicle (UAV) platform, and satellite platform; the application of multiscale remote sensing technology will help farmers to monitor crop growth status accurately and properly control fertilizing amount.  
All authors have made significant contributions to this research. [PERSON] designed the paper and experiments, [PERSON] and [PERSON] carried out the analysis and provided the writing of this paper. [PERSON] and [PERSON] provided important insights and suggestions on this research. All authors read and approved the final manuscript.  
This research was supported by the National 863 Project of China (2013 AA102401).  
We acknowledge the support and the use of the facilities and equipment provided by the School of Remote Sensing and Information Engineering, Wuhan University, China. We are very thankful to the research groups led by [PERSON] and [PERSON], College of Resources and Environment, Huazhong Agricultural University, China for your hard work to design experiment and collect field samples.  
The authors declare no conflicts of interest.