## 4 Evaluation of MODIS-Compatible VIIRS EVI
(**a**) Density plot of MODIS EVI minus VIIRS EVI (\(\delta_{1}\)) _vs_. MODIS EVI; (**b**) Density plot of MODIS EVI minus MODIS-compatible VIIRS EVI (\(\delta_{2}^{*}\)) _vs_. MODIS EVI. Figure 6: (**a**) Spatial distribution of MODIS EVI minus VIIRS EVI (\(\delta_{1}\)) over geographic coordinate for the evaluation; (**b**) Spatial distribution of MODIS EVI minus MODIS-compatible EVI (\(\delta_{2}^{\bullet}\)). Figure 7: (**a**) Ratio of absolute mean of difference between MODIS EVI minus MODIS-compatible VIIRS EVI (\(\delta_{2}^{\bullet}\)) to that between MODIS EVI minus VIIRS EVI (\(\delta_{1}\)) for angular-dependent data (characterized by backward or forward scatterings and view zenith angle); (**b**) Ratio of STD for \(\delta_{2}^{\bullet}\) to that for \(\delta_{1}\); (**c**) Ratio of RMSE for \(\delta_{2}^{\bullet}\) to that for \(\delta_{1}\); (**d**) Frequency of angular distribution in data for evaluation. backward scattering does not show a strong angular dependency, whereas that of forward scattering showed a monotonic increasing trend. Dependencies of the improvement of RM, RS, and RR on EVI values from MODIS are shown in Figure 8. Note that the maximum MODIS EVI in our evaluation was 0.688. The RM differed largely across EVI bins (Figure 8a). The bias was the smallest for the EVI range of 0.3-0.4 and was the largest for higher EVI values (from 0.5 to 0.7). Whereas the RS was less than unity and varied a little across EVI bins (Figure 8b), the RR varied greatly from 0.5 to 1.25 (Figure 8c). According to the histogram of this dataset (Figure 8d), most of samples were in the EVI range between 0 and 0.5. Since the coefficients of the MODIS-compatible VIIRS EVI are sensitive to canopy greenness [17], a smaller number of samples in higher EVI values would have been a source of the large RM and RR for that EVI value range. It should be noted that cloud leakage in VCM could degrade the performance of the atmospheric correction algorithm, which may likely be associated with densely vegetated areas due to persistent cloud coverage. Land cover dependencies of RM, RS, and RR are shown in Figure 9a-c, respectively. Note that data corresponding to \"Water\" and \"Permanent snow and ice\" were excluded from this evaluation. The RM was less than 0.4 except for the urban and built-up (ur) cover type (Figure 9a). The RS was close to unity (Figure 9b). The RR was smaller than unity, and showed large improvements for barren and sparsely vegetated area (bs), closed shrublands (cs), and open shrublands (os) followed by savanna (sv) and grass land (gr) (Figure 9c). The urban and built-up area, in general, shows totally different spectral characteristics from vegetated areas, which might have caused the largest RM and RR. Figure 9d shows the land cover frequency of this evaluation dataset. The sample sizes of os, gr, and bs were large, and a general negative correlation between the sample size and the improvements in statistics was observed. The forest cover types where high EVI values are expected (first five cover types in Figure 9a-d) showed some improvements in the statistics, although samples with large EVI values resulted in larger errors. The MODIS/VIIRS EVI tended to fall approximately between 0.1 and 0.5 (0.25 in average) for forest types, in which the MODIS-compatible VIIRS EVI showed higher performance than in the higher EVI value range.