## 1 Introduction  
The normalized difference vegetation index (NDVI), which is one of the important spectral parameters reflecting crop growth and nutrient information, has been widely used in leaf area index (LAI) estimation, chlorophyll inversion, and protein assessment. Many researchers have shown that there is a good correlation between NDVI and LAI. Therefore, measuring NDVI can characterize vegetation information and the growth trend of crops.  
To improve the application effect of the normalized difference vegetation index, researchers analyze the shortcomings and deficiencies of NDVI from different aspects, put forward the corresponding improvement methods, and furthermore build a series of derivative parameters. There are generally two main ideas, as follows: (1) Keeping the same spectral characteristics of the red band and near-infrared band of NDVI, and improving the mathematical transformation. This method can enhance the vegetation characteristics of vegetation information and weaken the non-vegetation interference information, so as to improve the sensitivity of vegetation index. (2) On the basis of the original mathematical function of NDVI, new spectral parameters are introduced by a certain mathematical expression for improving applicability and sensitivity. [PERSON] et al. [1] and [PERSON] et al. [2] adopted the first improvement idea and amplified the reflectivity components of NIR (Near Infrared) and Red n times, thus enlarging the difference between the near-infrared and infrared reflectance parameters. Consequently the estimation accuracy of NDVI for the vegetation coverage of winter wheat is improved. Meanwhile, the literature [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] has taken the second kind of idea. [PERSON] et al. [3] thought that green and blue bands were sensitive to the vegetation coverage in varying degrees. Accordingly, new types of NDVI derivative parameters such as BNDVI (Blue NDVI), GRNDVI (Green and Red NDVI), GBNDVI (Green and Blue NDVI), RBNDVI (Red and Blue NDVI), etc., were constructed. Then, the estimation ability of these vegetation indices for rice LAI was analyzed by experiments. [PERSON] et al. [6] constructed a new vegetation index to assess the leaf area index of winter wheat after fusing the short infrared ray characteristic band with the index of visible and near-infrared wave bands, which effectively improved the correlation between the vegetation index and LAI. [PERSON] et al. [9] introduced the green wave band in the index of NDVI to build the GNDVI (Green NDVI) index, and achieved a high estimation accuracy in correlation with vegetation coverage. [PERSON] et al. [12] also used the green band to construct a new vegetation index, and achieved an ideal result in the study of the fitting degree with the photosynthesis of Douglas fir. The above research has already achieved preferable results in the quantitative analysis of the spectral vegetation index and crop biological parameters. But in terms of research ideas, it seems to be relatively simple. Only one of the two methods is usually used, namely, improving mathematical transformation or introducing new band characteristic parameters. Therefore, it is worth further exploring whether the two methods can be combined to construct new derivative parameters of NDVI to improve the estimation accuracy and applicability of the quantitative analysis of the vegetation index. On the basis of the parameters such as NDVI, GNDVI, BNDVI etc., the study attempts to enhance vegetation feature information and construct accumulative types of derivative parameters of NDVI through the accumulation of correlation information, analyzing the correlation between these parameters and the leaf area index of rapeseed simultaneously.