## 3 Results
### Predictive Performance of the RF Models  
The performance of models is detailed in Table 4 and the scatterplots (Figure 4), which show the relationship between the observed and predicted in-bag and OOB AGB values. The predictive performance of the models was assessed based on the lowest %RMSE OOB and %RMSE, in that order, and the highest R\({}^{2}\) between sensors and treatments in the same group. The model with the best AGB and Corg prediction performance is highlighted in bold in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**AGB**} & & & & & \\ \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSE\%**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSE**} \\  & & & & & & & **OOB** & **OOB\%** \\ \hline \multirow{4}{*}{G1} & \multirow{4}{*}{S2} & SV & 0.85 & 21.46 & 12.35 & 39.60 & 22.75 \\  & & SV1m\({}^{2}\) & 0.85 & 87.55 & 12.65 & 157.26 & 22.58 \\  & & SVPA & 0.85 & 34,246.41 & 12.33 & 58,938.28 & 20.98 \\ \cline{2-8}  & & SV & 0.83 & 22.89 & 13.19 & 62.74 & 35.93 \\  & & SV1m\({}^{2}\) & 0.86 & 85.19 & 12.31 & 163.32 & 23.49 \\  & & SVPA & 0.84 & 804.33 & 12.81 & 1502.88 & 23.67 \\ \hline \multirow{4}{*}{G2} & \multirow{4}{*}{S2} & SVNL & 0.85 & 0.12 & 2.37 & 0.21 & 4.04 \\  & & SV1m\({}^{2}\)NL & 0.83 & 0.13 & 1.95 & 0.22 & 3.34 \\  & & SVPANL & **0.87** & **0.11** & **0.91** & **0.21** & **1.71** \\ \cline{2-8}  & & SVNL & 0.85 & 0.12 & 2.37 & 0.22 & 4.24 \\  & & SV1m\({}^{2}\)NL & 0.85 & 0.12 & 1.83 & 0.21 & 3.17 \\  & & SVPANL & 0.85 & 0.12 & 1.37 & 0.21 & 2.41 \\ \hline \multicolumn{2}{c}{**Corg**} & & & & & \\ \hline \multirow{4}{*}{G1} & \multirow{4}{*}{S2} & SV & **0.89** & **7.41** & **10.39** & **16.17** & **19.71** \\  & & SV1m\({}^{2}\) & 0.79 & 41.83 & 14.39 & 57.38 & 22.41 \\  & & SVPA & 0.84 & 14,228.77 & 12.50 & 24,846.43 & 21.73 \\ \cline{2-8}  & & SV & 0.85 & 8.79 & 12.26 & 16.54 & 21.83 \\  & & SV1m\({}^{2}\) & 0.84 & 36.51 & 12.71 & 63.21 & 21.88 \\  & & SVPA & 0.84 & 318.91 & 12.27 & 573.27 & 23.02 \\ \hline \multirow{4}{*}{G2} & \multirow{4}{*}{S2} & SVNL & 0.86 & 0.12 & 2.73 & 0.21 & 5.08 \\  & & SV1m\({}^{2}\)NL & 0.85 & 0.12 & 2.09 & 0.23 & 4.06 \\  & & SVPANL & 0.86 & 0.12 & 1.00 & 0.20 & 1.70 \\ \cline{1-1} \cline{2-8}  & & SVNL & 0.86 & 0.11 & 1.49 & 0.21 & 2.72 \\ \cline{1-1}  & & SV1m\({}^{2}\)NL & 0.83 & 0.13 & 2.26 & 0.21 & 3.69 \\ \cline{1-1}  & & SVPANL & 0.85 & 0.12 & 2.67 & 0.21 & 5.02 \\ \hline \hline \end{tabular}  
* The best AGB and Corg prediction performance is highlighted in bold.  
\end{table} 
Table 4: Performance of models for prediction of _Cyperus byssaceus_ AGB and Corg from S2 and PS data using different treatments. Figure 4: The predicted results of S2 models (**A**) and PS (**B**) with different treatments. Values in grams per treatment area variation: SV (0.25 m\({}^{2}\)), SV1m\({}^{2}\) (1 m\({}^{2}\)), SVPA (S2 = 400 m\({}^{2}\) and PS = 9 m\({}^{2}\)), and the same in NL. Dark blue dots (Prediction), light blue dots (OOB Prediction). The best-fitting models have a higher R\({}^{2}\), a lower slope of the OOB Prediction line in relation to Prediction, and less dispersion of the points. The best model among the sensors was that of S2 using treatments SVPANL for AGB (R\({}^{2}\) 0.87) and SV for Corg (R\({}^{2}\) 0.89). Their higher R\({}^{2}\) values better explained the variability in observed AGB and Corg, and %RMSE and %RMSE OOB showed less relative dispersion of predicted values in relation to observed values compared to the best PS sensor models. The treatment that was repeated the most with the best fit (thrice) in group 2 and among all the models was SVPANL. In group 1, there was no predominance of the best-performing treatment, but the model with SV treatment was repeated twice and achieved the best R\({}^{2}\) (0.89) of all the models.