## 4 Discussion
### The Effect of Treatments and Plot Size on Model Performance and Importance of Spectral Variables  
Our tests with prediction models using different treatments indicated that the transformation of the data into an NL (G2) contributed to an improvement in the performance of the models. The models with G2 treatments overall achieved R\({}^{2}\) values slightly higher (0.08) than the untransformed ones (G1) (Table 4). The transformation of the data also reflected the effect of the variation in plot size on the performance of the models, explaining between 46% and 89% of the variation between the root mean square error percentage (RMSE%) and plot size (Figure 5). It also tended to centralize importance on the most important spectral variable. This indicates that G2 models depend heavily on one or a few predictors, while G1 models allow the distribution of importance to a wider range of variables. An NL transformation was applied to soil Corg for input into prediction models, including RF [23]. This reduces the variability of the data for more stable training. The NL transformation is effective when proportional variations in the dependent variable produce linear variations in the independent variable [72], aligning with the AGB-Corg relationship and PS and S2 predictors [15; 39]. Additionally, in estimating AGB and Corg content in mangrove species, it was observed that the NL transformation mitigates the increasing AGB variance with increasing tree size or canopy structure, thus reducing heteroscedasticity [73]. By evaluating linear models with and without NL transformation, the author found that NL models more accurately reflect the biomass-independent variable relationship, supporting [74] and our findings. Regarding the effects of plot size on model performance, generally, treatments estimated based on the sensor pixel area performed better in AGB predictions and sample value treatments performed better in Corp predictions. By normalizing the data and mitigating the variance, the treatments in G2 revealed the hidden relationship in G1 between plot size and model performance. This made it noticeable that the prediction errors (RMSE%) of the models decreased with the increase in plot size with the S2 sensor data. In the models with PS data, the test with larger plots did not have the same effect (Figure 5). Research on AGB and Corp prediction has shown that plot size and sample number affect model performance, particularly in forest ecosystems, planted forests, and mangroves with use of Lidar and other sensors, which are noted for reducing model errors as plot size increases [22; 31; 32; 33; 75]. As factors that may contribute to this observation, larger plots mitigate co-registration errors due to increased spatial overlap, enhancing resilience to GPS positioning errors and reducing spatial variance among plots [25; 33]. The way in which plot and sample sizes affect RF model accuracy was examined using Pleiades sensor data (spatial resolution from 50 cm to 2 m) in plantation forests in Iran [76]. Larger plots (300 m\({}^{2}\) and 500 m\({}^{2}\)) yielded marginally higher accuracy (RMSE% -0.51 to 0.65) compared to 100 m\({}^{2}\) plots (RMSE% \(\sim\)0.59 to 0.70). However, factors like sample size and total area sampled had a more pronounced impact on performance. These results do not suggest a clear advantage of preferred plot size for precise AGB estimates, though they motivate us to explore additional factors to enhance PS model performance. Without comparable studies on plot size effects in wetland AGB and Corp models with herbaceous vegetation, our results align with those based on forest and other vegetation cover estimates. Larger plots improve S2 model performance (Figure 5A,B), suggesting that smaller plots may compromise the reliability of estimates. Predictor variables' importance varied across treatments on the same sensor, indicating that their correlations with the dependent variables differ between treatments.