## 5 Discussions  
The coefficients obtained through the calibration/optimization using the one-year global dataset were (\(K_{1}^{*}\), \(K_{2}^{*}\), \(K_{3}^{*}\), \(K_{4}^{*}\)) = (1.026, \(-\)0.001, 0.874, 1.022). The optimum coefficients obtained for North America in August 2013 in our previous study [17], represented here by \(K^{\prime}_{i}\) (\(i\) = 1, 2, 3, 4), were (\(K_{1}^{\prime}\), \(K_{2}^{\prime}\), \(K_{3}^{\prime}\), \(K_{4}^{\prime}\)) = (0.947, 0.010, 0.265, 0.995). These differences in the optimum coefficient values resulted from the spatiotemporal coverages of the calibration datasets used in the present and previous studies. It should be noted that \(K_{i}^{*}\) obtained in this study fell in or approached the range of coefficients simulated by radiative transfer models (PROSAIL and 6S code) [17]. The maximum and minimum of \(K_{i}\) (\(i\) = 1, 2, 3, 4) obtained in the model simulation were (0.980, 1.024), (\(-\)0.002, 0.004), (0.695, 0.935), (0.879, 1.020), respectively. \(K_{2}^{*}\) (\(-\)0.001) and \(K_{3}^{*}\) (0.874) fell within their corresponding ranges, and \(K_{1}^{*}\) (1.026) and \(K_{4}^{*}\) (1.022) slightly greater than the ranges. Since the simulation of coefficients was performed by varying optical thickness of vegetation canopy and aerosol layer but by fixing angular condition, considerations of angular variations in the present study could widen the ranges of coefficients characterized by the maximum and minimum to include \(K^{*}_{i}\) obtained in this study. Further investigation is required to fully understand the relationship between the optimum \(K^{*}_{i}\) values from actual sensor data and ranges of coefficients simulated using radiative transfer models. The derived equation of MODIS-compatible VIIRS EVI was a non-linear function of \(K_{i}\), and therefore the optimization of \(K_{i}\) corresponds to the non-linear regression. The small non-linear biases, however, still remained in the \(\delta_{2}^{*}\) (MODIS EVI minus MODIS-compatible VIIRS EVI) in Figure 3b. The comparison of our algorithm with alternative models such as polynomials is certainly important and worthy to evaluate, which is a focus of future work. The calibrated MODIS-compatible VIIRS EVI showed less compatibility with MODIS EVI over higher EVI values (\(>\)0.5). MODIS-VIIRS reflectance pairs that had large positive values in \(\delta_{2}^{\star}\) (MODIS EVI minus MODIS-compatible EVI) also had positive values in \(\delta_{1}\) (MODIS EVI minus VIIRSE EVI). However, a Hyperion-based bandpass simulation analysis conducted over AERONET sites only found negative values in MODIS EVI minus VIIRSEVI [15]. This discrepancy implies that these large positive \(\delta_{1}\) values were likely caused by other potential factors than the bandpass differences, such as cloud leakage in VCM which decreases VIIRSE EVI and increases \(\delta_{1}\) and \(\delta_{2}^{\star}\), and the maturity status (quality level) of the input surface reflectance data. The VIIRS surface reflectance product was on a \"beta\" maturity status for the data period used in this study. Likewise, the VIIRS sensor data record (SDR), the input to the VIIRS surface reflectance product, reached a provisional status in March 2013 [31]. Quality issues associated with the VIIRS surface reflectance data that we noticed included cloud leakage [17; 35], aerosol optical thickness [36; 37], and VIIRS algorithmic differences from MODIS. The leakage of small clouds resulted in large biases in the surface reflectance intermediate product (IP) which were not documented in QFs [16]. Therefore, spatial averaging for generating CMG pixels could have operated differently between MODIS and VIIRS due primarily to the cloud leakage in VCM. The MODIS/VIIRS pixels involved in area-averaging for generating CMG pixels heavily depend on algorithmic accuracy for cloud detection over the cloudy area.