## 4 Discussion  
It is an important approach in the field of hyperspectral nondestructive monitoring to analyze the response characteristics of spectral features, to propose an improved method for the vegetation index, and to obtain an LAI inversion model with a higher accuracy and applicability. The study has changed the calculation formulas of mathematical transformation based on the normalized difference vegetation index (NDVI) through introducing the characteristic information of green and blue bands, and constructed a series of \(\mathrm{A_{VNDVI}}\) derivative parameters. Furthermore, we analyzed the sensitive spectral band of \(\mathrm{A_{VNDVI}}\) combined with other research results [3; 9; 25] and discussed the coefficients of R2, RMSE, and RE between VNDVI, \(\mathrm{A_{VNDVI}}\) and LAI of rapeseed. The results showed that: (1) NDVI and its derivative parameters such as VNDVI and \(\mathrm{A_{VNDVI}}\) have a good correlation with the leaf area index of rapeseed. The determination coefficients R2 are greater than 0.83, the values of RMSE vary from 0.4133 to 0.4916, and the absolute values of RE are distributed between 0.9737% and 2.0991%, which indicates the good accuracy and reliability of the inversion models. It is shown that the information complementation and redundancy effects of the red, green, and blue bands can improve the overall performance of the inversion models of vegetation indices to a certain extent. The results are basically consistent with the conclusions of [3; 26]. (2) The coefficients of determination R2 of the inversion models between the series derivative parameters of NDVI and LAI are slightly raised. Some parameters, such as GNDVI, \(\mathrm{A_{GNDVI}}\), and GRNDVI, have certain advantages in RMSE and RE, but the advantages of other parameters are not obvious. (3) The cumulative derivative parameters--\(\mathrm{A_{VNDVI}}\), introducing more spectral feature information, greatly improve the anti-interference and evidently decrease the sensitivity. It is a worthwhile consideration scheme to estimate LAI with \(\mathrm{A_{VNDVI}}\) series parameters in the case of large interference factors in the background. In addition, if the balance point which is more suitable for the specific scene needs to be explored between sensitivity and anti-interference, fine-grained analysis of sensitive bands can be carried out. It should be pointed out that the above results are based on data analysis of the planting cycle of a single rapeseed variety, and whether the inversion models can be applied to other rapeseed varieties needs to be verified by more experimental data.  
Figure 3: (**a**) Relative sensitivity and anti-interference curves of NDVI and VNDVI; (**b**) Relative sensitivity and anti-interference curves of NDVI and \(\mathrm{A_{VNDVI}}\).  
**Author Contributions:** Conceptualization, C.Q.; Methodology, C.Q. and G.L.; Software, C.Q.; Validation, C.Q.; Formal Analysis, C.Q. and H.T.; Investigation, C.Q. and F.L.; Resources, G.L.; Data curation, C.Q.; Writing--Original Draft Preparation, C.Q.; Writing--Review & Editing, R.Z. and Z.Z.; Visualization, C.Q. and X.L.; Supervision, G.L.; Project Administration, G.L.; Funding Acquisition, G.L.  
**Funding:** This research was funded by [Chinese National Natural Science Foundation] grant number [11571103]. [Crop's Image Processing Innovation Team of Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China] grant number [620190107013], [The Specific Fund of the CPC Central Committee Leading Local Science and Technology Development for Hunan Province in 2016] grant number [2014 TP1001], and [Natural Science Foundation of Hunan Province] grant number [2017]J5063].  
**Conflicts of Interest:** The authors declare no conflict of interest.