## 4 Discussion
### Factors Which Affect the Performance of VIIUPD-Derived VCI
One reason is that the TCI is mainly based on the LST data and the LST data is only derived from the thermal band information. Other information except the temperature can also be a good indicator to drought. In addition, drought indices--such as PCI, and SMCI--cannot be directly obtained from the surface reflectance data, but are obtained from inversion models from the observation sensors. The inversion algorithm itself is a key factor that affects the drought-monitoring performance of the indices. For example, there was a large difference between the VUA-based TCI and MODIS-based TCI [2; 35]. SMCI did not function better in areas with a high density of vegetation cover and high temperature than VIUPD-derived VCI. That is because SMCI is based on the microwave data, and the microwave brightness temperatures at X and C band are sensitive to only the soil moisture in the top 1 to 1.5 cm of the soil, when the vegetation density is too great, the revision model will not achieve convergence [45]. This study analyzed the utility of VIUPD-derived VCI for drought monitoring; overall, VIUPD-derived VCI showed better performance than NDVI-derived VCI and other commonly used drought indices. However, the VIUPD-derived VCI still has its own limitations and applicability. Analysis shown above indicated that the performance of VIUPD-derived VCI was greatly correlated to temperature. When in the CDs with lower temperature, the VIUPD-derived VCI performed worse. This may because the bands which used to establish VIUPD were the seven bands from the MOD09A1 product. These bands used had a worse performance for drought monitoring in the colder regions. Future studies would evaluate the sensitivity of VIUPD-derived VCI based on other, different bands. [PERSON] _et al._[46] monitored the drought condition using SIF data, future study can also include SIF information for establishing VIUPD. There are still some uncertainties affecting the analysis of this study. First of all, we have to admit that there is no absolute \"true\" drought measure. By using USDM, SPI, PDSI and Z-index as the standard for comparison, they establish a base level and the fact that the USDM, SPI, PDSI and Z-index have a certain level of subjectivity because of individuals' reports. There is still a possibility that these indices which used as standard did not properly monitor the drought condition. Other factors may also affect the results of this research, such as the remaining cloud noise of the images and spatial resolution. This study also presented a new way to calculate VCI. Almost all the studies used VCI based on NDVI. Our research indicated that the establishment of VCI should not be limited in NDVI, and VIUPD can also be a good vegetation index to build VCI. Other future applications can also build VCI based on other superior vegetation index under specific circumstances. This study only focused on the utility of VIUPD-derived VCI for drought monitoring and compared VIUPD-derived VCI with other commonly used remote sensing based drought indices. Future work should focus on why the VIUPD-derived VCI is limited in its performance for short-term drought monitoring and what factors could affect the performance of VIUPD-derived VCI.