## 4 Discussion
### Factors Which Affect the Performance of VIIUPD-Derived VCI  
It is not surprising that the VIUPD-derived VCI is proved to be a better index than NDVI-derived VCI for measuring agricultural drought crossing the CONUS. Firstly, the NDVI uses only the red and near-infrared bands (bands 1 and 2 from MODIS data), other bands of surface reflectance, which are also sensitive to vegetation water status [37; 38; 39; 40; 41] can be taken into consideration to improve the sensitivity of remote sensing-based drought monitoring. In this study, we took the blue reflectance band as an example to show that the bands other than NIR and red reflectance can also have high correlations with _in situ_ drought indices (see Figure 11). According to the Figure 11, the performances of the blue band and red band had similar spatial distributions, and this spatial distribution is also similar to that of NDVI and VIUPD based VCIs. In some special regions, the blue band had even higher correlations than which of NIR and red. For example, even the blue and red band had similar spatial distributions when correlated to SPI-09, the blue reflectance band had higher r-value than NIR and red reflectance band did in many CDs in the Great Plains. In the process of photosynthesis, the red and blue band are both the absorption spectra for vegetation, but previous studies suggested that blue band can reduce the effect of noise such as cloud or aerosols [42; 43]. The VIUPD establishing method, which is based on the universal pattern decomposition method (UPDM), can combine and make full use of information from all the observation bands [22]. The characteristics of different bands can be combined in VIUPD. However, all the bands used cannot perform well in the cold regions, so the VIUPD-derived VCI performed worse in these regions than others did. Secondly, NDVI is significantly affected by difference in spectral bandwidth, especially for the red band, and that changes in spatial resolution lead to less pervasive but more land cover specific effects on NDVI [44]. In this case, NDVI from the bandwidth of specific satellite sensor (MODIS, in this study) may not best reflect the vegetation water status in some particular regions. VIUPD has been proved to be a sensor-independent index which can greatly avoid the effect of difference in spectral bandwidth [22; 23]. Thirdly, NDVI may be a limited index for monitoring the leaf status that is related to drought condition in some circumstances. The experiment in [PERSON] _et al._[PERSON] _et al._ (2015) showed that the reflectance corresponding to the red and NIR bands is almost identical to that of typical yellow leaves, while for typical dead leaves, the reflectance in the red band is lower than in the infrared band. Figure 11: Spatial distribution of climate divisions with correlations (r-value) between red band, NIR band and blue band with _in situ_ drought indices for the entire growing season (April to October) 2002–2011. The green color stands for the negative correlation and the red color stands for the positive correlation. Therefore, the NDVI of yellow leaves may be lower than that of dead leaves in some circumstances. This characteristic of the NDVI may lead to confusion for drought monitoring, because when the NDVI of dead leaves is higher than that of yellow leaves, it indicates the area of yellow leaves has experienced a worse drought condition than the area of dead leaves, which is not logical. However, the VIUPD can largely avoid this drawback of NDVI through a combination of four pattern decomposition coefficients: the patterns of vegetation, soil, water, and the supplementary pattern and yellow leaf was used as the supplementary pattern [22; 36]. Some factors can also lead to the fact that VIUPD-derived VCI performed better than other commonly used remote sensing-based drought indices such as TCI, PCI and SMCI. The VIUPD-derived VCI showed better performance in many CDs than TCI.