## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Location Number** & _AGB_ (kg) (Ground) & **FVC (MSAVI)** \\ \hline SL1 & 18659.89 & 0.26 \\ \hline SL2 & 160815.21 & 0.77 \\ \hline SL3 & 34821.50 & 0.54 \\ \hline SL4 & 56166.84 & 0.71 \\ \hline SL5 & 65778.21 & 0.70 \\ \hline SL6 & 61020.63 & 0.66 \\ \hline SL7 & 57353.44 & 0.47 \\ \hline SL8 & 55605.20 & 0.46 \\ \hline SL9 & 36807.44 & 0.35 \\ \hline SL10 & 33626.20 & 0.44 \\ \hline SL11 & 22331.11 & 0.24 \\ \hline SL12 & 21460.73 & 0.27 \\ \hline SL13 & 17964.20 & 0.29 \\ \hline SL14 & 26194.07 & 0.34 \\ \hline SL15 & 24666.35 & 0.34 \\ \hline SL16 & 28177.79 & 0.35 \\ \hline SL17 & 29352.34 & 0.44 \\ \hline SL18 & 13470.94 & 0.29 \\ \hline SL19 & 17159.58 & 0.33 \\ \hline SL20 & 27933.05 & 0.35 \\ \hline SL21 & 20521.26 & 0.26 \\ \hline SL22 & 17844.75 & 0.36 \\ \hline \end{tabular}
\end{table} 
Table 5: Ground forest \(AGB\) and selected image forest \(FVC\). The result shows that all the evaluation between the image-derived and the field calculated forest \(AGB\) agreed within one standard deviation. The above evaluation was performed based on a single tree species, i.e., _Shorea robusta_. This tree species dominates in the study area by approximately 90%. In this study, we have ignored the other tree species to avoid the complexity in the developed method. Thus, we can consider that this study provided approximately more than 90% accuracy. The uncertainty of the final outcome was tested using the chi-squared test. The chi-squared test value was 0.19, i.e., 19% of uncertainty in the estimate. Finally, the developed model was used to calculate the forest \(AGB\) of the study area using the Sentinel-2 image (Figure 4). Based on the forest \(AGB\) map produced, the total calculated forest \(AGB\) of the study area is estimated at 7,914,875,916.57 kg (7,914,875.92 tons) in 170,704,500 m\({}^{2}\) (17,070.45 hectares). Consequently, the study area contains forest \(AGB\) on an average of 463.66 tons per hectare. Further, the efficacy of the method was compared to the [PERSON] et al. model [47] in forest \(AGB\) estimates. The [PERSON] et al. model for forest \(AGB\) estimate is a linear relationship between crown projected area (\(CPA\)) achieved from an optical remote sensing image and field based \(AGB\). Figure 4: The forest \(AGB\) calculated from Sentinel-2 imagery in kg per pixel (**a**) and tons per hectare (**b**) for the Tundi reserved forest, Jharkhand, India. The model above was derived for Sentinel-2 and ground \(AGB\) obtained from the study area, which is shown in Equation (16):  
\[AGB(i,j)=17419\times\{CPA(i,j)\}-7430.2,\text{ and }R^{2}=0.667 \tag{16}\]  
Likewise, the effectiveness of the developed method was compared to the [PERSON] et al. model [9]. This model was developed based on random forest regression for forest \(AGB\) estimation. These models were applied to calculated forest \(AGB\) for the validation, which is shown in Tables 5 and 6. The results show that five with [PERSON] et al. model and six by [PERSON] et al. model among seven validation points matched within one standard deviation with the ground estimates. Note that the forest \(AGB\) standard deviation used for comparison was calculated from the total ground data. Further, the potency of the proposed approach was tested using correlation coefficient and mean absolute error. The results achieved by this approach, the [PERSON] et al. [47] model, and the [PERSON] et al. [9] model were evaluated by comparing their outcomes with ground forest \(AGB\) estimates. The correlation between results from Sentinel-2 image and ground data by the above approach was higher (0.9937) compared to [PERSON] et al. model (0.9796) and [PERSON] et al. model (0.9884). Whereas the mean absolute error between outcomes from image and ground data by the proposed approach was lower (582.97) than the [PERSON] et al. model (18428.41) and the [PERSON] et al. model (1509.64). The results revealed that the developed approach is superior compared to the [PERSON] et al. [47] and [PERSON] et al. [9] models.