## 4 Discussion
### The Effect of Treatments and Plot Size on Model Performance and Importance of Spectral Variables
Nonetheless, a consistent pattern was observed in the top five variables, involving bands and VIs (Figure 6). CO\({}_{2}\)Flux VI and SWIR bands 1 and 2, blue, green, and RE 6 and 7 (for S2 data), along with NIR, blue, red, and green bands and SPRI and CO\({}_{2}\)Flux VIs (for PS data), were the most effective predictors for precise AGB and Corp models. CO\({}_{2}\)Flux, traditionally linked with hyperspectral data, has been successfully applied to multispectral images by substituting the bands centered on 531 nm and 570 nm with blue and green bands, as shown in various studies [77; 78; 79]. The CO\({}_{2}\)Flux, when adapted to PS and S2 images, reliably provides a carbon flux estimate comparable to hyperspectral sensors [78]. The CO\({}_{2}\)Flux was utilized in the context of extreme wetland events with S2 data to evaluate ecosystem service losses in hailstorm-affected mangroves [77]. The authors confirmed the VI's effectiveness in gauging storm impact on carbon storage. Similarly, the SPRI's effectiveness was validated in measuring Corp storage under drought-induced water stress [80]. Carbon flux-related VIs, specifically CO\({}_{2}\)Flux1 and 2 and SPRI, have surpassed traditional indices like the NDVI and NDAVI in importance. The SPRI tends to be more sensitive to daily and seasonal carbon flux changes in mangroves, while the NDVI proved to be stable in perennial mangroves [54]. The spectral mixing's impact on CO\({}_{2}\)Flux--SPRI values were lower in high-resolution sensors, such as PlanetScope and AisaFENIX--underscores CO\({}_{2}\)Flux's importance regarding S2 data [78]. Additionally, CO\({}_{2}\)Flux2's importance in PS models can be attributed to the NDAVI's resistance to background influences (moist soil and litter) [49], which are greater in PS for the vegetation studied [39]. SWIR bands effectively capture vegetation signals and are at the top of the important variables in ML models for AGB and Corp in wetlands, as evidenced by S2 data [81],Landsat OLI [18], and others [82]. These sensors discern vegetation and soil moisture content [83], showing a positive correlation with vegetation and a negative one with soil moisture [84]. Spatial predictions (Figure 7 and Figures S1 and S2 in the Supplementary Materials) show lower AGB and Corg with S2, likely due to SWIR's humidity sensitivity, yet without compromising S2 model accuracy. Visible bands (blue/red for PS, blue/green for S2) and red edge (RE6 and 7) were important due to their absorption by vegetation pigments like carotenoids, xanthophylls, and chlorophyll, indicating photosynthetic activity [51, 52]. This absorption inversely relates to NIR spectral response, which rises with AGB [85]. These bands, including in VIs, correlate strongly with AGB and Corg in herbaceous wetlands [15, 16, 29]. This study initially explored important research gaps in the modeling of AGB and Corg in herbaceous wetlands. We examined the influence of data normalization on the performance of RF models in estimating AGB and Corg, an area not previously explored, and found that models with normalized input data had lower estimation errors in the majority of cases. The NL transformation is frequently utilized in allometric equations for AGB and Corg inventories [22, 86]. Pixel values of AGB and Corg in NL can be used for these purposes and others mentioned, with an option to revert to original values via the exponential inverse function. In light of the absence of research on the influence of sample plot size on the accuracy of AGB and Corg estimation models in herbaceous marshes, we made progress in addressing this issue and found that the estimation errors decreased as the S2 model plot size increased. Our findings also show that OOB estimates are efficient for validation, yielding average prediction errors similar to those of validation sets in reference studies. This can reduce the need for extensive collection, addressing the challenges of access and collecting enough wetland biomass for test and validation sets, emphasizing that this was a significant difficulty in study.