## 3 Results
### Comparison of the Spatial Variability of the VIUPDD and NDVI Derived VCIs  
Figure 3 shows maps describing the spatial variability and average correlation coefficients of the NDVI-derived VCI and VIUPDD-derived VCI, and the corresponding values for station-based indices from April to October 2002-2011. Spatially, the NDVI-derived VCI produced stronger correlations with PDSI, SPI-3, SPI-6, and SPI-9 than with SPI-1, SPI-2, or the Z-index. The high correlation values (\(r>0.7\)) were mainly located in southeastern areas, such as Texas, New Mexico, and Arizona. Due to the strong influence of environmental factors, such as mean annual precipitation, permeability, and irrigation [4], the NDVI-derived VCI had a low correlation in the northwestern and northeastern United States. As shown in Figure 2, in general, the spatial variability of VIUPDD-derived VCI is similar to NDVI-derived VCI, but the VIUPDD-derived VCI can significantly increase the correlation with station-based drought indices. When correlated to the station-based drought indices, the VIUPDD-derived VCI produced strong correlations for more climate divisions than the NDVI-derived VCI did. While, for the VIUPDD-derived VCI, the high correlation values (\(r>0.7\)) were located in southern regions (e.g., Texas) and western regions (e.g., California and Nevada), and also in humid-hot regions in the southeastern CONUS (e.g., Florida). Weaker correlations were found mainly in humid areas for the NDVI-derived VCI, whereas the VIUPDD-derived VCI exhibited significantly improved performance in humid areas. Similarly to the NDVI-derived VCI, the VIUPDD-derived VCI produced weaker correlations with _in situ_ drought indices in humid regions in the northeastern area around the Great Lakes, between the US and Canada, and in evergreen forest regions in the northwest. However, the VIUPDD-derived VCI also produced stronger correlations in other humid CDs in the eastern and northwestern CONUS than NDVI-derived VCI (see Figure 3). In addition, in order to make a comprehensive comparison between VIUPDD-derived VCI and NDVI-derived VCI, when correlated to _in situ_ drought indices, the number of CDs which passed at least \(p\)-value of 0.05 for testing statistical significance is measured (in Figure 4). Figure 4 indicated that both VIUPDD-derived VCI and NDVI-derived VCI have fewer CDs that passed the \(p\)-value test for correlating to SPI-1, and more CDs when correlating to PDSI. It is also obvious that there are many more CDs that passed the \(p\)-value test for VIUPDD-derived VCI than NDVI-derived VCI correlating to all station-based drought indices. To further evaluate the drought-monitoring ability of the VIUPD-derived VCI, the performance of the VIUPD-derived VCI and NDVI-derived VCI were compared in three sub-periods (April to May,  
Figure 4: The number of climate divisions (CDs) passed at least \(p\)-value of 0.05 for testing statistical significance when comparing VI-based VCIs and station-based drought indices. \(X\)-axis stands for the station based drought indices which NDVI and VIUPD-derived VCIs are correlated to. \(Y\)-axis stands for the number of CDs which passed at least \(p\)-value of 0.05 for testing statistical significance. Figure 3: Spatial distribution of climate divisions with the correlations (r-value) between remote-sensing-based VCI and _in situ_ drought indices for the entire growing season (April to October). June to July, and August to September). It can be seen from Figure 5 and Table 6 that the r-values of NDVI and VIUPD based VCIs varied among the sub-periods. Both the VIUPD-derived VCI and NDVI-derived VCI performed better in June to July and August to September than in April to May. [PERSON] _et al._[3] indicated that vegetation response to moisture availability varies significantly between months, and it depends on the plant growth stage.