## 3 Results
### Predictive Performance of the RF Models
In general, the models with treatments estimated based on sensor pixel area prevailed with the best fits in predicting AGB, and the models with treatment sample values prevailed with the best fits in predicting Corg with the S2 sensor. No treatment prevailed between groups with the PS datasets, but SV1m\({}^{2}\) had the highest R\({}^{2}\) of 0.86. Although the models achieved a good fit, they had moderate to low predictive accuracy when using the OOB dataset, with %RMSEOOB around 1x higher compared to the training dataset. All the models showed a tendency to overestimate, which can be seen when comparing the observed versus predicted mean values (Table 5). Note that even with a low R\({}^{2}\), the average AGB and Corg predicted with OOB data were very close to the average observed and training values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sensor** & **Treatment** & \(\mu\)**Obs** & \(\mu\)**Pred** & \(\mu\)**OOB** \\ \hline \multirow{4}{*}{G1} & & SV & 172.61 & 173.78 & 174.05 \\  & S2 & SV1m\({}^{2}\) & 658.32 & 660.43 & 664.10 \\  & & SVPA & 276,178.96 & 277,714.74 & 280,909.88 \\ \cline{2-6}  & & SV & 172.61 & 173.51 & 174.64 \\  & PS & SV1m\({}^{2}\) & 658.32 & 660.09 & 663.16 \\  & & SVPA & 6214.03 & 6278.38 & 6349.69 \\ \hline \multirow{4}{*}{G2} & & SVNL & 5.102 & 5.103 & 5.106 \\  & S2 & SV1m\({}^{2}\)NL & 6.488 & 6.502 & 6.504 \\  & & SVPANL & 12.480 & 12.489 & 12.500 \\ \cline{2-6}  & & SVNL & 5.102 & 5.107 & 5.116 \\  & PS & SV1m\({}^{2}\)NL & 6.488 & 6.493 & 6.497 \\  & & SVPANL & 8.686 & 8.687 & 8.693 \\ \hline \multicolumn{6}{c}{**Corg**} \\ \hline \multirow{4}{*}{G1} & & SV & 71.54 & 71.31 & 72.15 \\  & S2 & SV1m\({}^{2}\) & 273.82 & 278.26 & 278.69 \\  & SVPA & 114,456.71 & 113,874.3 & 114,321.65 \\ \cline{2-6}  & & SV & 71.54 & 71.69 & 71.87 \\  & PS & SV1m\({}^{2}\) & 273.82 & 274.76 & 276.37 \\  & & SVPA & 2575.28 & 2599.82 & 2625.71 \\ \hline \multirow{4}{*}{G2} & & SVNL & 4.223 & 4.222 & 4.221 \\  & S2 & SV1m\({}^{2}\)NL & 5.61 & 5.616 & 5.634 \\ \cline{1-1}  & SVPANL & 11.601 & 11.605 & 11.616 \\ \cline{1-1} \cline{2-6}  & & SVNL & 4.223 & 4.236 & 4.253 \\ \cline{1-1}  & PS & SV1m\({}^{2}\)NL & 5.61 & 5.618 & 5.636 \\ \cline{1-1}  & SVPANL & 7.807 & 7.814 & 7.824 \\ \hline \hline \end{tabular}  
* Average 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. The average AGB and Corg predicted with OOB data were very close to the average observed and training values.  
\end{table} 
Table 5: Comparison of observed and estimated average AGB and Corg values. We found a good fit between model performance and plot area variations under G2 treatments (Figure 5A), with distinct sensor relationships (Figure 5B). The AGB and Corg model errors (RMSE%) with G2 treatments showed an exponential decay as plot size increased. Conversely, the models with G1 treatments and with PS sensor data did not explain the effect of plot size variation on model performance, indicated by low R\({}^{2}\) values. The S2 models with G2 treatments were substantially better (R\({}^{2}\) = 0.89).