## 4 Conclusions and Outlooks  
In this study, a GSA was made for several commonly used satellite-derived VIs based on homogeneous-canopy RTM simulations and RF learning. With combined parameters of CCC and CWC introduced by the RF-based GSA, we obtained the following main findings:  
* For most VIs, LAI is the most influential parameter, indicating that most VI variations are driven by LAI changes. * MODIS- and OLI-derived EVI is most sensitive to CCC, i.e., the product of Cab and LAI, but Sentinel-derived EVI is still most sensitive to LAI. This finding seems to support the previous conclusion that MODIS- and/or OLI-EVI are more appropriate for modeling GPP. In addition, it can be also concluded that the sensitivity of a VI depends on band settings of the used sensor. * EVI and NIRv are sensitive to Cdm, LIA, Chrown, and SZA due to their sensitivity to NIR reflectance. Therefore, special attention should be paid when these two VIs are used across biomes with different canopy architecture and dry matter content. * Although both UNVI and TCTG are derived from all-band information, UNVI shows a stronger sensitivity to CWC. UNVI is therefore expected to perform better in agricultural drought monitoring models. * PRI and MTCI are most sensitive to Car and Cab, respectively. Some uncertainties still exist for the GSA results in this study. A primary uncertainty is the limitation of the PROSAIL model. Because this is a case study based on the PROSAIL model, the results only apply for homogeneous canopies. For non-homogeneous canopies, such as forests, VIs can exhibit stronger anisotropy and show additional sensitivity to structural parameters, such as stem density, crown size, and fractional crown coverage [38]. Appropriate radiative transfer models for forest canopies exist, such as DART [39] and INFORM [40], but are not studied in our research, which focuses on homogeneous canopies. For example, a recent paper [6] shows large differences between  
Figure 3: Feature importance scores and scatter plots between true and predicted values for MODIS-derived PRI and OLI-derived TCTG. the sensitivity of PROSAIL (for agricultural crops) and that of INFORM (for forests) to various canopy traits, demonstrating that our study needs to be extended to also cover those vegetation types. This seems even more important when considering the relatively large footprint of sensors such as OLCI and MODIS, as those sensors usually mix the signal stemming from several cover types. It should be also noted that for a model-based GSA, the results can be dependent on the design of the input parameters in the LUT. In this study, the range of Cab was set from 10 to 80 ug/cm\({}^{2}\) on the basis of the fact that crop leaves have relatively high chlorophyll concentration. Therefore, the sensitivity of VIs to Cab is relatively weak compared to that in a recently published paper [6]. The boundaries of input parameters and the corresponding influences on GSA results should be further studied in future works. Sensor noise and the process of atmospheric correction were also neglected for the sake of convenience. A GSA based on canopy-atmosphere integrated RTM simulations may provide more realistic results [10]. With regard to the RF-based GSA method, although RF has been widely used to identify influential factors in situations without a physical model, its potential for GSA should be further studied, especially compared to variance-based methods. It should be noted that the RF-based method also supports a result-based GSA. Therefore, future studies can validate the GSA results with in situ data. This study shows the sensitivities of several satellite-derived VIs that were used at relatively large scales based on PROSAIL simulation and RF-based GSA method. Combined variables, CWC and CCC, were introduced in the RTM-based GSA. The results may help to improve our understanding of the variability of these VIs across time and space.