# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### Vegetation Indices  
Several LAI-VIs were selected to introduce LAI information into the MTCI. The normalized difference vegetation index (NDVI) was used for comparison, which exhibits saturation for different crops when the LAI is \(>\)2 [48; 49; 50]. Several researchers have modified the NDVI to mitigate the effect of saturation when estimating the LAI. A linearized NDVI (LNDVI) was derived by introducing a linearity-adjustment factor, \(\beta\), into the NDVI equation. The LNDVI is more sensitive to spectral angles (reflectances) and has denser isolines with an increase in spectral angles (VI values) from the red to near infrared (NIR) space. Therefore, it has improve linearity and maintains a higher sensitivity to the fraction of the vegetation in densely vegetated areas [22]. [PERSON] et al. [51] presented a stretched NDVI (S-NDVI) that was constructed using a scaling transformation function to eliminate saturation when the vegetation fraction became too large. In comparison with the NDVI, the S-NDVI did not reach saturation for LAIs of 2.5-5.0. In addition to such modified NDVIs, other spectral indices also maintain better relationships with the LAI for densely vegetated regions. The renormalized difference vegetation index (RDVI) [52] was proposed to combine the advan  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sites**} & **Crop** & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**CV**} \\  & **Species** & & & & & & & \\ \hline \multirow{2}{*}{XTS} & \multirow{2}{*}{ Wheat} & 2002 & 227 & 137.18 & 45.42 & 237.57 & 48.39 & 0.35 \\  & & 2004 & 44 & 145.57 & 73.77 & 231.13 & 38.13 & 0.26 \\ \multirow{2}{*}{US-Ne2} & \multirow{2}{*}{ Soybean} & 2002 & 27 & 67.83 & 3.31 & 186.33 & 48.58 & 0.72 \\  & & 2004 & 21 & 110.90 & 5.70 & 274.36 & 78.64 & 0.71 \\ \multirow{2}{*}{US-Ne3} & \multirow{2}{*}{ Soybean} & 2002 & 18 & 72.01 & 5.36 & 119.87 & 40.12 & 0.56 \\ \hline \hline \end{tabular} N = number of samples; Mean = mean value; Min = minimum value; SD = standard deviation; CV = coefficient of variation.  
\end{table}
Table 2: Summary statistics for the measured wheat and soybean CCCs (\(\mu\)g cm\({}^{-2}\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Band Center (nm)** & **Band Width (nm)** \\ \hline B1 & 412.5 & 10 \\ B2 & 442.5 & 10 \\ B3 & 490 & 10 \\ B4 & 510 & 10 \\ B5 & 560 & 10 \\ B6 & 620 & 10 \\ B7 & 665 & 10 \\ B8 & 681.25 & 7.5 \\ B9 & 705 & 10 \\ B10 & 753.75 & 7.5 \\ B11 & 760.625 & 3.75 \\ B12 & 775 & 15 \\ B13 & 865 & 20 \\ B14 & 885 & 10 \\ B15 & 900 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of MERIS onboard the ENVISAT satellite.  
tages of the difference vegetation index (DVI) [53] for low LAIs and those of the NDVI for high LAIs. [PERSON] et al. [54] compared 56 hyperspectral vegetation indices and concluded that the RDVI had the greatest positive relationship with the LAI, and remained far from saturation in the presence of large LAIs. [PERSON] et al. [16] designed a new triangular vegetation index (MTVI2) that proved to be the best predictor of the LAI. The MTVI2 was found to be insensitive to changes in chlorophyll and did not exhibit saturation at high LAIs. The vegetation indices used in this study are shown in Table 4.