## 5 Conclusions  
We utilized an optimized RF regression model based on PS and S2 optical sensor data and field collection to estimate AGB and Corg in a wetland in southern Brazil. Model efficacy was assessed against field data treatments, including variations in plot size and data normalization. Sensor accuracy and key spectral indicators for AGB and Corg were also evaluated. Our results lead to the following conclusions.  
1. Regarding RF parameters, different Ntrees impacted model errors, notably in non-normalized treatments, enhancing RF model precision. Thus, optimized RF models provide more accurate estimates. OOB estimates served effectively for validation, with average prediction errors within the limits found in validation sets in reference studies. This result is useful in light of wetland data collection challenges.
2. Normalized sample data treatments enhanced RF model accuracy for AGB and Corg prediction. Estimation errors decrease as S2 model plot size increased, indicating smaller plots may compromise estimate reliability with S2.  
Utilizing S2 and PS sensors underscored the value of medium spatial resolution satellite data for enhancing estimate accuracy and high-resolution data for delineating AGB and Corg spatial variability, respectively, in wetlands. Sensor performances were close; however, S2 was more efficient.
* The RF method, employing the combination of VI CO\({}_{2}\)Flux and S2's SWIR, blue, green, and RE bands 6 and 7 as predictors, excelled in AGB and Corg prediction. Leveraging an ML algorithm with VI and bands indicative of carbon fluxes and biomass changes proved beneficial, and these predictors serve as spectral indicators of these ecological functions.
* In addition to optimizing the parameters of the RF model, optimizing the input set of AGB and Corg collected in the field, i.e., evaluating normalization and plot sizes, has contributed to more accurate estimates. This approach holds promise for improved monitoring of the ecological processes of AGB and Corg storage in wetlands and for contributing to the understanding of these ecosystems as carbon sinks, vital for offsetting emissions and meeting national and global GHG reduction targets.
* We encourage future work that compares the effects of different plot sizes, sample data normalization methods, sensors, and VIs in RF models and other ML approaches on the accuracy of AGB and Corg estimates in marshes, as well as in other wetlands with emergent herbaceous vegetation, such as salt marshes and peatlands. This will contribute to the continued advancement of knowledge on improving the modeling of AGB and Corg in wetlands.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/land14030616/s1](https://www.mdpi.com/article/10.3390/land14030616/s1)]([https://www.mdpi.com/article/10.3390/land14030616/s1](https://www.mdpi.com/article/10.3390/land14030616/s1)). Figure S1: Spatial modeling of AGB for all models separated by sensor (A) S2 and (B) PS; Figure S2: Spatial modeling of Corg for all models separated by sensor (A) S2 and (B) PS.  
The authors would like to thank the Gravatat Municipal Environment Foundation for support in the fieldwork and the aerial photos granted. This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--finance code 001, award 88887.488339/2020-00, and the Rio Grande do Sul State Foundation for Research Support (FAPERGS).  
The datasets analyzed in this study can be found on Copernicus Open Access Hub and the Planet Research and Education Program on the Planet Explorer website. Further inquiries about the data can be directed to the corresponding author.  
The authors declare no conflicts of interest.