**3. Results**

#### *3.1. Mangrove Tree Characteristics in CGBRS*

Table 5 gives the characteristics of the mangrove trees in the 121 sampling plots. The AGBs ranged from 7.26 to 305.41 Mg ha−1, with a mean of 97.54 Mg ha−1. The mangrove heights varied from 6.47 to 17.35 m, and their DBHs ranged from 6.69 to 22.19 cm. The mangrove tree densities ranged from 170 to 1680 trees ha−<sup>1</sup> (Table 5).


**Table 5.** Characteristics of the mangrove trees in CGBRS.

#### *3.2. Modeling Results, Assessment, and Comparison*

Table 6 and Figure 5 compare the performances of the five regression methods with all input variables derived from S-2 MSI, VIs, and ALOS-2 PALSAR-2 images for mangrove AGB estimation in the study area. The XGBR model incorporating the S-2 (11 MS bands), ALOS- 2 PALSAR-2 (5 bands), and VIs (7 bands) data achieved the highest performance (Table 6), with an *R*<sup>2</sup> of 0.805 and an RMSE of 28.13 Mg ha−<sup>1</sup> in the testing dataset (23 predictor variables based on the fused S-2, the VIs and the ALOS-2 PALSAR-2 data), implying a good fit between the model estimates and field-based

measurements. The next-highest performers were the GBR and RFR models. In contrast, the SVR and GPR models were unsuitable for retrieving the mangrove AGB at the study site (Table 6).


**Table 6.** Performance comparison of ML techniques on mangrove AGB estimation.

**Figure 5.** Scatter plots of the estimated (X axis) versus the measured (Y axis) mangrove AGB in the five ML models, integrating the data of S-2, ALOS-2 PALSAR-2, and VIs in the testing phase. (**a**) GBR, (**b**) XGBR, (**c**) RFR, (**d**) SVR, (**e**) GPR.

Table 7 lists the performances of the XGBR method in five scenarios (SCs) of mangrove AGB prediction, using different combinations of the S-2, ALOS-2 PALSAR-2, and VIs data.

**Table 7.** Performance of the XGBR model using different numbers of variables. (Bold values highlight the best-performing model).


As clarified in Table 7, the XGBR model yielded a promising result in SC3 using the combined S-2 and VIs, but the model achieved a poor result in SC2 using the ALOS-2 PALSAR-2 alone. The performance in SC1 using the S-2 dataset alone was moderate. We concluded that fusing all data in SC4 boosted the prediction performance of XGBR for estimating the mangrove AGB in the study area. The visual results of the testing phase (Figure 5) reconfirm the high performance of mangrove AGB estimation by XGBR with the 23 variables of the fused data. Particularly, the green scatter points cluster around the blue line and the RMSE is small.
