## 3 Results and Discussion
The special case is EVI derived from MODIS and OLI, which is more sensitive to CCC (MTCI will be discussed later). Because CCC is the product of total LAI and Cab, EVI is considered to be more sensitive to green LAI. Based on the fact that photosynthesis is mainly driven by chlorophyll, EVI might be more representative for photosynthesis-related greenness than other VIs (especially NDVI) due to its stronger sensitivity to CCC. The usefulness of MODIS- and Landsat-derived EVI (and two-band EVI, EVI2) in gross primary production (GPP) estimation has been demonstrated by several studies at both ecosystem [24; 25; 26] and global scales [8; 27; 28]. The strong sensitivity of EVI to CCC shown in Figure 2 can be a reason for the good performance of EVI in indicating photosynthetic capacity. However, it is noted that LAI is still the dominating parameter for EVI derived from MSI and OLCI. Compared to OLI and MODIS, the main difference is that these two sensors are designed with narrow red and NIR channels. Because chlorophyll does not directly affect NIR reflectance, the potentially stronger sensitivity of broad red band canopy \(\rho\) to CCC is supposed to be the reason for the observed stronger sensitivity of broad-band EVI to CCC. Different GSA results for EVI from broad- and narrow-band sensors indicate that band settings (central wavelength and full width at half maximum,  
Figure 2: Feature importance scores of considered predictors given by random forest (RF) training. VIs are more sensitive to parameters with higher feature importance scores. It should be noted that UNVI and MTCI are shown in the same column because they are calculated, respectively, for two sensors. FWHM) may change the sensitivity of a VI and should be considered for applications with multi-sensor analysis and future spaceborne sensor design [29]. For all four sensors, the sensitivity of NDVI is dominated by LAI, and far behind is CCC as the second influential factor. Generally, LAI can be retrieved from canopy \(\rho\) at red and NIR bands on the basis of the RT theory [30]. Therefore, a direct relationship can be established between NDVI and LAI, although NDVI can saturate when LAI is high. In this sense, NDVI, which is probably the most ever-used VI, mainly represents variations of LAI across time and space. In addition, compared to EVI, NDVI exhibits a relatively stronger soil sensitivity. This is because a soil adjustment parameter (\(L\)) is introduced into EVI, which makes EVI more resistant against canopy background changes. Yet, constant \(L\) also gives EVI additional sensitivity to NIR band \(\rho\), especially in dense canopy cases where blue and red band \(\rho\) are close to zero. A similar situation applies for NIRv, whose variation is mainly driven by NIR \(\rho\) when NDVI saturates in dense canopy cases. For NIR bands, solar photons are strongly scattered by leaves and are weakly absorbed by non-photosynthetic constituents [31]. The way that NIR photons are scattered depends on canopy structure (represented by LAI and leaf inclination angle, LIA, for homogeneous canopies) and the direction of incident light (represented by solar zenith angle, SZA), while absorption is dominated by brown elements (represented by brown pigment content, Cbrown and dry matter content, Cdm) [3]. Therefore, the strong sensitivity of EVI and NIRv to NIR band \(\rho\) makes them exhibit relatively strong sensitivities to LAI, LIA, Cdm, and SZA. The sensitivity of EVI and NIRv to canopy structure and non-photosynthetically related parameters should be considered when applied at large scales across different ecosystems with different canopy architectures. UNVI is calculated for MODIS and OLI, and MTCI is calculated for the other two sensors. UNVI was proposed on the basis of the universal pattern decomposition method (UPDM), which takes the spectral response function of the sensor into account to alleviate sensor dependency and projects the observed spectra into spectral space defined by universal patterns [32].