## 4 Discussion  
### AGB and Corg Estimation Accuracy and Efficiency of Sensors  
The most accurate estimates were achieved with the S2 models, with RMSE OOB (validation) between 19.7% and 22.7% of the mean observed data, while the PS models ranged between 21% and 35.9%. The best S2 AGB and Corg models achieved R\({}^{2}\) of 0.87 and 0.89 and RMSE% OOB of 1.71% and 19.71%, respectively. These mean validation error values are similar to those achieved in recent studies using RF and Sentinel-2 in predicting AGB of wetlands with similar herbaceous vegetation: 15% [16], 25% [15], and 22.35% [65] for mangrove vegetation.  
When compared to a study that used the same sensors and linear multiple regression to estimate AGB and Corg of Scirpus giganteus in Banhado Grande [39] (R\({}^{2}\) = 0.46 and 0.45, RMSE = 166.73 g/m\({}^{2}\) and 67.47 g/m\({}^{2}\), respectively), the RF results were better with S2 data (R\({}^{2}\) = 0.85 and 0.79, RMSE = 157.26 g/m\({}^{2}\) and 57.38 g/m\({}^{2}\)). This can be attributed to S2's ability to capture complex non-linear relationships between vegetation and RS information [16], and in general, ML methods have shown better performance than linear methods in wetlands [7; 16; 18; 29].  
Figure 7: Spatial modeling of AGB (**1**) and Corg (**2**) for the top-performing models per sensor: (**A1**,**A2**) S2 and (**B1**,**B2**) PS. The highlighted areas show the differences in the spatial heterogeneity of the estimate.  
Wetlands research has not yet extensively utilized the PS sensor. Linear regressions was used to estimate mangrove biomass [66], where S2 and PS sensors produced models with R\({}^{2}\) of 0.89 and 0.80, respectively. The authors suggest that PS's lower VIs may have reduced model accuracy compared to RapidEye and S2 sensors. Lower VIs for PS are also reported in other studies [39], and this may have contributed to the lower performance of our PS RF models. S2 showed superior accuracy in assessing Spartina alterniflora phenological heterogeneity [67], though both satellites had comparable metrics (R\({}^{2}\) 0.63), the PS providing greater spatial detail in phenology and biomass.  
Although PS images provide fine detail in VIs and biomass, the complexity of VI trajectories can lead to greater errors in models [8]. Figure 7 and Supplementary Figures S1 and S2 illustrate PS models' clearer AGB and Corg variations compared to S2, yet S2 surpasses these in prediction accuracy. Performance metrics are similar, but S2's are superior. Spectral prediction model differences may stem from variations in radiometric quality, pixel size, and bandwidth, that is, band range in nm [68]. The spectral consistency between sensors improves with greater between band wavelength overlap [69]. Here, S2's blue and NIR bands are broader than PS's, and S2 includes additional SWIR and red edge bands.  
Studies comparing PS and S2 sensors, among others, with different spatial resolutions for predicting AGB and Corg indicate that spectral and temporal information is more relevant than high spatial resolution. Thus, images with larger pixels provide more accurate estimates [70; 71] because they capture a larger area of reflectance, reducing variations in climate, angle of incidence, and other sources of potential error between satellites [68].  
The R\({}^{2}\) values, although high during model training (between 0.80 and 0.89), decreased in the OOB datasets (between 0.15 and 0.29), suggesting a need for additional input variables to understand generalization. Nonetheless, our OOB R\({}^{2}\) values exceed those for validation sets from similar RF regression studies in wetlands. An R\({}^{2}\) of 0.14 was reported for the validation dataset using bands and VIs [65], which increased to 0.74 using their mean, median, and percentile values. Incorporating these metrics could thus enhance RF model precision for AGB and Corg estimation in marshes with scarce field data.