# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 4 Discussion  
### Role and Form of VI Combinations in CCC Estimation Models  
The CCC is related to the LAI and LCC, and the estimation of chlorophyll content with remote sensing requires information on both variables [62]. The chlorophyll index mainly uses red-edge bands that are sensitive to LCC [10,13], while LAI-Vis use near-infrared bands that are sensitive to the LAI [16,52,55]. The MTCI and LAI-Vis were combined to improve the remote sensing of CCC for crops, in this study. The results indicate that the above VI combinations, with all three of the selected regression models, performed better in estimating the CCC than the MTCI alone. This implies that the VI combinations effectively fused the leaf area index and chlorophyll information.  
The type of VI combination that is appropriate depends on whether the estimated vegetation parameter is at the canopy or leaf level. A combined multiplicative VI was used for the univariate regression model. Similarly to in this study, a SAR and optical multiplication vegetation index (SOMVI) has been proposed to improve the estimation of above ground biomass [63]. Several combined VIs based on CHL-Vis and LAI-Vis have also been proposed for LCC estimation using a univariate regression model [28, 29, 30]. Canopy biomass is a canopy population parameter that is similar to CCC, while LCC is a leaf-level chlorophyll parameter that is independent of the LAI. The combined VIs that are used for LCC estimation are often in the form of ratio indices such as TCARI/OSAVI [28], which reduces the influence of the LAI on the VI. However, multiplicative forms, such as the one used in this study, are not typically used.  
The best VI combination can also differ according to the type of regression analysis model. Multiple regression or advanced machine-learning techniques can be used with more input variables to describe complex scenarios. For example, this study used a CHL-VI and a LAI-VI as binary variables for binary regression and the random forest model for CCC estimation. Multiple vegetation indices can also be used in the cost function to estimate vegetation parameters based on the direct inversion of the vegetation model [31]. For estimating the LCC in crops, a simple LUT (look-up table) has been proposed that is  
Figure 11: Accuracy of CCC retrieval for soybean crops using RFR and MERIS imagery: (**b**) MTCI and NDVI; (**c**) MTCI and LNDVI; (**d**) MTCI and SNDVI; (**e**) MTCI and RDVI; (**f**) MTCI and MTVI2. The performance of a single MTCI in a linear regression model is displayed in (**a**) for comparison.  
indexed using a CHL-VI and LAI-VI [31]. This matrix-based VI combination is a special form of physical model inversion, and has been proven to be better than linear regression models that use a VI with a single ratio. [PERSON] [12] used MTCI to estimate the CCC of soybeans based on linear regression and field spectra, achieving an RMSE of 86 \(\upmu\)g cm\({}^{-2}\), similar to the result were obtained using the MTCI alone (RMSE = 77.10 \(\upmu\)g cm\({}^{-2}\)). However, when using a combination of the MTCI and LAI-VI with three regression techniques, our results were far better, and the best performance for three regression approaches were RMSE = 51.68 \(\upmu\)g cm\({}^{-2}\) (ULR), RMSE = 63.47 \(\upmu\)g cm\({}^{-2}\) (BLR), and RMSE = 37.76 \(\upmu\)g cm\({}^{-2}\) (RFR). The results presented in this paper confirm that multiple regression models that integrate CHL-VIs and LAI-VIs, especially the random training model, show better generalization performance than the use of a single CHL-VI.