## 3 Results and Discussion
Another important point is that the NDVI, SAVI, and MSAVI2 are indices constructed based on the red and NIR bands, and these three exhibit similar results with a slightly lower accuracy than the group of three indices discussed in the previous paragraph. The reason for this might be the fact that the aforementioned type of indices saturate at high values of biomass which is a fact in tropical environments. [PERSON] et al. (2019), in a study comparing Landsat-based NDVI and SAVI, suggested that the vegetation indices that include SWIR spectral bands have higher correlations with AGB in the case of forest stand with complex structure, while the vegetation indices that incorporate NIR associate \begin{table}
\begin{tabular}{c c c c c} \hline
**Vegetation Index** & **Adjusted R\({}^{2}\)** & _p-Value_ & **Adjusted R\({}^{2}\) (45 Plots)** & _p-Value (45 Plots)_ \\ \hline band8/band2—current paper & 0.7 & \textless{}0.001 & 0.57 & \textless{}0.001 \\ EVI & 0.65 & \textless{}0.001 & 0.51 & \textless{}0.001 \\ ARVI & 0.61 & \textless{}0.001 & 0.45 & \textless{}0.001 \\ mNDVI & 0.6 & \textless{}0.001 & 0.44 & \textless{}0.001 \\ VOG1 & 0.55 & \textless{}0.001 & 0.39 & \textless{}0.001 \\ SAVI & 0.53 & \textless{}0.001 & 0.42 & \textless{}0.001 \\ NDVI & 0.53 & \textless{}0.001 & 0.42 & \textless{}0.001 \\ MSAVI2 & 0.51 & \textless{}0.001 & 0.40 & \textless{}0.001 \\ CRI & 0.29 & 0.0014 & 0.22 & 0.0014 \\ \hline \end{tabular}
\end{table} 
Table 4: Empirical vegetation indices extracted from the Sentinel-2 satellite image and their respective correlations to the plot biomass. more strongly with forest stand with simple structure. As an example, [PERSON] et al. (2019) suggested that substituting the NIR band with the red edge on the NDI45 index improved the prediction of the biomass, a finding which was seconded by [PERSON] et al. (103). Biomass retrieval from remote sensing data from several sensors has been demonstrated frequently in the scientific literature. Spatial resolution is one of the satellite sensor's specifications that matters when quantifying biomass from space. For instance, [PERSON] et al. (2017), in a study examining herbaceous AGB of a wetland in South Africa, reported comparatively higher AGB prediction accuracies for WorldView-3 than Sentinel-2, which have spatial resolutions of 1 m and 10 m, respectively. Similarly, the spectral resolution is of importance. For example, [PERSON] et al. (2018), in a study in which they quantified grass AGB across different fertilizer treatments in South Africa, while they determined that hyperspectral-resampled Sentinel-2 and Landsat-8 data provided a good correlation (R\({}^{2}\) = 0.81 and R\({}^{2}\) = 0.76, respectively), hyperspectral data nevertheless exhibited a better correlation (R\({}^{2}\) = 0.92). A similar conclusion was reached by [PERSON] et al. (2018), who found that the accuracy of Sentinel-2 was comparatively, but acceptably, lower than that of the HyspIRI hyperspectral sensor (R\({}^{2}\) = 0.58 and R\({}^{2}\) = 0.69, respectively). Sentinel-1 and Sentinel-2 satellites have brought new capabilities in this domain, as they offer a combination of very high radiometric resolution at key spectral regions for vegetation-related information. The current study reinforces the usability of Sentinel-1 and Sentinel-2 satellites in retrieving AGB remotely. Finally, regarding the experiment on the robustness of the methodology, we reran the analysis based on 1617 tree samples collected from 45 plots between September and November 2017. The R\({}^{2}\) graph (Figure 9) presents a similar pattern with the results from the 30 plots (Figure 5), which indicates that the proposed approach can be applied with a more diversified dataset. However, the correlation for each of the band combinations was reduced, which can be attributed to the temporal dimension of this dataset; the field data collected in November 2017 were collected four months apart from the acquisition of the Sentinel-2 satellite image.