**3. Results**

#### *3.1. AGB from Field Sampling*

Field data of the mangrove plantation were collected a few times in the study area, and a total of 127 sampling units were obtained. The AGB was calculated by the allometric equation of the specific species. The plantation *S. apetala* had a density of 1623 trees per ha. The heights of the tree ranged from 2 to 20 m, with an average of 13.64 m. As it is a fast-growing species, the AGB of the mangrove plantation ranged from 90.65 to 237.74 t/ha, with an average of 159.70 t/ha, exhibiting a wide extent (43.88 t/ha of standard deviations), owing to their different ages. The field data revealed decreasing trends of AGB values in accordance with the sequence of growth from shore to sea.

#### *3.2. Importance of Input Variables for AGB Estimation*

The importance of the input variables was quantified by the RF algorithm to evaluate the relationship between them and the AGB (Figure 4). The results showed that the most important variable was the UAV-based DSM, implying that it is key to the AGB estimation. The next most-important variables were the VIs (RVI, NDVI, etc.) derived from GF-2 optical images, followed by those (KD3, HV, etc.) derived from the GF-3 SAR images.

**Figure 4.** Importance of variables according to the random forest (RF) model.

#### *3.3. Results and Accuracy Assessment of AGB Model*

The RF models were developed using the observed AGB as output variables and the variables derived from images from multiple sources as input variables. The RMSE and RMSEr were acquired by the observed and the predicted AGB values based on five-fold cross-validation. As shown in Table 3, the mean of the predicted AGB values in the four experiments was 156 t/ha, in line with the mean observed value. The model using input variables derived from the GF-3 SAR images yielded the lowest estimation accuracy of the AGB of mangroves, followed by the model that used the GF-2 optical images. The estimation accuracy of the model obtained through the integration of GF-2 and GF-3 images was better than the model that used either GF-2 or GF-3 data, with a reduction of 2.32% and 3.49% in RMSEr, respectively. The combination of GF-2, GF-3, and DSM data produced the highest accuracy (RMSE = 25.69 t/ha, RMSEr = 16.53%) of all models. A two-sided t-test revealed significant differences (*p* ≥ 0.95) in the predicted AGB values between models, using the combination of GF-2, GF-3, and DSM data, and those using only GF-2 or GF-3 images, but no significant difference (*p* < 0.95) was observed between the values obtained by models formed by a combination of GF-2, GF-3, and DSM data, and those obtained by models formed through the combination of GF-2 and GF-3 images.


**Table 3.** The accuracy of mangrove biomass estimation based on different input variables.

Scatterplots of the predicted versus the measured AGB values are presented to show the accuracy of the models with different input variables using the RF algorithm and five-fold cross-validation in the study area. As shown in Figure 5, the predicted AGB values of all models were above the 1:1 line at lower values, indicating that AGB values of the mangrove plantation had been overestimated, but they were the opposite at higher values. The coefficient of determination (R2) of the model derived by integrating GF-2, GF-3, and UAV-based DSM data was 0.61, followed by the model obtained by a combination of GF-2 and GF-3 images. The other two models yielded lower R<sup>2</sup> values.

#### *3.4. Mapping AGB of Mangrove Plantation*

The model derived using a combination of GF-2, GF-3, and UAV-based DSM data produced the highest accuracy, and was used to map the spatial distribution of the AGB across the study area (Figure 6). The AGB map exhibited significant spatial variability, ranging from 106.163 t/ha to 266.162 t/ha, with an average of 137.89 t/ha. The AGB values of the species of mangrove in the southeast of the study area were higher than those in the west and northwest. The biomass decreased progressively from shore to sea, and trees near the shore had larger canopies and higher AGB values because they were older than those in other areas. Trees growing outside the forest edge and off the shore were younger and exhibited smaller biomass. The mangrove plantation map was consistent with the results of the field surveys, visual interpretation of remotely sensed images, and prior knowledge of Qi'ao Island.

**Figure 5.** Scatter diagram of regression models detailing the linear regression, coefficient of determination (R2), and relative root-mean-square error (RMSEr) between field-measured aboveground biomass (AGB) and predicted AGB from (**a**) GF-2 optical images; (**b**) GF-3 SAR images; (**c**) a combination of GF-2 and GF-3; and (**d**) a combination of GF-2, GF-3, and DSM data.

**Figure 6.** Spatial distribution of mangrove biomass.
