# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 4 Discussion
### Influence of LAI-VIs Selection of VI Combinations  
The key to the success of the combined approach employed was the excellent performance of the LAI-VIs in reducing the saturation associated with high LAIs. The combination of the MTCI and LAI-VIs resulted in a greater sensitivity to CCC compared to the use of a single MTCI. As shown in Figure 5, high LAIs are the main cause of saturation when estimating CCC. The purpose of adding LAI-VIs is to increase the sensitivity of the MTCI to a high CCC in the presence of a high LAI. Therefore, we investigated the performance of each VI combination in CCC estimation. The results in Sections 3.3 and 3.4 show that the modified NDVIs, especially the LNDVI, performed better than the NDVI. The RDVI and MTVI2, which also proved to be resistant to saturation, were used in the combined VIs and performed better than the NDVI. The sensitivity of LAI-VIs to high LAIs can affect the performance of the training regression model. The relationships between LAI-VIs and the LAI can also be affected by other factors such as canopy coverage, as demonstrated in previous studies, and different LAI-VIs can have different capabilities for estimating the LAI [28; 64; 65]. The MTVI2 is a modification of the triangular vegetation index (TVI) that preserves the sensitivity at high LAIs and reduces the effects of soil contamination [16]. The relationships between the LAI and LAI-VIs based on the simulated spectra, field spectra, and MERIS satellite data are shown in Figure 12: the MTVI2 was more closely related to the LAI than the other LAI-VIs were, in agreement with previous studies. This might be why the combined VI that included the MTVI2 showed the best results.  
Comparing the Performance of the Proposed CCC Estimation Models for Satellite Data with Variable LAIs  
It is important to examine the performance of models for CCC estimation when using satellite data. Satellite data are affected by several factors such as noise and neighboring effects. It is still challenging to apply retrieval models to satellite data [66]. The results from  
Figure 12: Coefficient of determination produced by the linear regression between the LAI and LAI-VIs—including the NDVI, LNDVI, SNDVI, RDVI, and MTVI2—from the simulation dataset, field spectra, and MERIS satellite data.  
this study, which only used the MTCI, confirm the findings from previous studies showing that low-LAI and high-LAI conditions can cause difficulties when using VIs to estimate vegetation chlorophyll [19; 20; 21; 24]. The results indicate that the RFR model using a VI combination showed the best improvement in CCC estimation, while the performance improvement for MERIS satellite data varied for different LAI values (Figure 13b). The scatter points in Figure 13b are closer to a 1:1 line when LAI > 2, indicating that the MTCI and MTVI2 combination (using random forest regression) could reduce the overestimation of CCC that occurred when using the MTCI alone in the ULR model at high LAIs (Figure 13a). The proposed RFR method improved the large overestimation of values. However, some overestimation still occurred at low LAIs (LAI < 2) (Figure 13b). This is mainly because the chlorophyll VI calculated based on red-edge reflectance was affected by the soil background more than the NIR band when the canopy was sparse or at low coverage [25].