## 3 Results
### Importance of Predictor Variables  
Figure 6 shows the importance of predictor variables for the final optimized models. We expected the VIs to predominate as the most important variables for the models in both sensors, but our results were different. Although at least one VI was present among the five most important variables in all the models, the single bands were present in greater quantity, indicating that they contained abundant information pertaining to the performance of models in estimating AGB and Corg.  
The two most important variables in the S2 models were VI CO\({}_{2}\)Flux1, followed by the band SWIR, and this order of importance was maintained in all the AGB and Corg models. For the PS models, the NIR band was the most important variable, followed by the blue band in the AGB models and VI SPRI in the Corg models.  
For both sensors, the models with Group 2 treatments (NL) inclined to centralize the importance in the first variable, while the Group 1 models (without NL) better divided the importance between the first five variables. As a result, this set of the five most important variables showed higher importance values in the Group 1 models (from 65% to 74%) than in Group 2 (57% to 71%). We also found that these most important variables differed in the AGB and Corg models and between the respective treatments.  
Spatial modeling of AGB and Corg was performed for the top-performing models per sensor (Figure 7) and for all models in the in the Supplementary Materials (Figures S1 and S2). RF predictions using S2 and PS data showed divergent spatial distribution trends. In the area highlighted in Figure 7, where sampling occurred, AGB and Corg values were similar for S2  
Figure 5: Variability in the prediction error RMSE% related to plot size in the two treatment groups (G1 and G2), differentiated by AGB and Corg models (**A**) and by sensor (**B**). The highest R\({}^{2}\) better explains the effect of plot size variation on performance of G2 models with NL data.  
and PS across treatments, with comparable spatial distribution. However, in the central _Cyperus_ boundary area, characterized by less human disturbance and higher moisture, predicted values were lower with S2 data (Figures S1 and S2), with little spatial heterogeneity (range: AGB 758 to 907 g/m\({}^{2}\), Core 293 to 381 g/m\({}^{2}\)), while for PS, these values were range AGB 643 to 954 g/m\({}^{2}\) and Core 270 to 403 g/m\({}^{2}\). The PS models' highest values were found in areas with woody and shrub species, transition zones, and wet fields, while in the S2 models, they were found in cultivated areas. The lowest values were observed in regions with higher moisture or open water.  
Figure 6: Importance of predictor variables for the final optimized S2 models (**A**) and PS models (**B**) and their treatment sets. Higher values indicate more important predictor variables.  
Despite the maximum values of AGB and Corg being higher in the S2 models, the ranges of predicted values within the same treatment were greater with PS data (Figures S1 and S2). For instance, in the SV and SV1m\({}^{2}\) treatments, the range values were 10 g and 41 g higher for AGB, respectively.