**4. Discussion**

Estimating the AGB of forests based on satellite remote-sensing images remains challenging for tropical and subtropical mangrove forests, owing to various factors that interfere in the relationship between the AGB and the variables of images, such as the complex nature of their environments and complex forest structures [17]. The AGB of a forest has a close relationship, with its canopy-related information, structure, and height of trees, where single remote-sensing data cannot simultaneously provide this information [76]. Previous studies have investigated the combination of optical images (Landsat, SPOT, Sentinel-2, etc.) and SAR images (Sentinel-1, ALOS, etc.) to improve the accuracy of estimation of the AGB of forests [17,18]. The feasibility and applicability of data from the new GF-2 and GF-3 satellites from the Chinese civilian High-definition Earth Observation Satellite (HDEOS) program, launched in 2014 and 2016, respectively, by the China National Space Administration (CNSA), need to be tested.

#### *4.1. Overall Performance of Random Forest Model*

The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations. A random forest model was selected to establish non-linear relationships between the AGB and the input variables, because of its ease of use and prediction accuracy [43]. Previous studies already used machine-learning algorithms, such as support vector regression (SVR) or artificial neural network (ANN) for AGB estimation, which produces satisfactory results. Wang et al. (2016) investigated the applicability of RF, SVR, and ANN for remotely estimating wheat biomass, and the results indicated that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage [77].

A major advantage of RF is bootstrap sampling and variable sampling, in which the subset of all variables is randomly selected using the best split for each node of the standard regression tree. In these situations, The RF model can decrease the algorithm's risk for overfitting and multicollinearity, due to relative insensitivity to variations in input variables, thereby improving generalization and robustness to predicted data. Therefore, some variables in this study are correlated. However, as demonstrated by Cutler et al., the RF model is not sensitive to collinearity, and has the ability to model complex and nonlinear interactions among predictor variables [78]. This is helpful, as it is commonly hard to determine which variables need to be removed when two or more variables correlate with each other [79]. Therefore, the RF algorithm provides a useful exploratory and predictive tool for estimating mangrove biomass.

#### *4.2. Contribution of Input Variables to Measuring AGB of Mangrove Plantation*

This study addressed the above issues using GF-2 optical and GF-3 data. The results indicated that the potential of the optical images and C-band FP SAR images for the AGB prediction of artificially planted mangroves were similar. The SAR-based results have been slightly weaker than the results with optical data, mainly due to weaker spatial resolution of the available SAR data in this study (5×5 m pixel resolution after multi-look method processing). On the other hand, the radiometric calibration referred to ALOS satellite processing using the SARscape, which may cause the errors of biomass estimation [57,58]. The advantages of SAR images are their multi-temporal acquisitions and independence of cloud cover, making the atmospheric correction of optical images more difficult [18].

The model of the AGB developed using a combination of GF-2 and GF-3 images yielded a higher estimation accuracy than those built using GF-2 or GF-3 images alone. This finding is consistent with previous studies, which have noted that integrating optical and SAR images can improve the accuracy of estimation of the AGB of forests, mainly because factors influencing biomass estimation, such as canopy-related information (canopy density, vegetation status) and forest structure, can be reflected by them [18,80].

However, optical or SAR images often incur saturation problems in canopies owing to dense vegetation, leading to the underestimation of biomass [81]. When considering variables of the DSM based on GF-2 and GF-3 images, the accuracy of the AGB model improved by 2.7% in terms of RMSEr. DSM data were also identified as the most important variable by the RF algorithm, because they were collected on a similar date with field measurements and can reflect the relative height of trees in a mangrove plantation, which is important for biomass estimation [42]. Tree height is usually computed from corresponding digital terrain models (DTM) subtracted from digital surface models (DSM). However, the DTM for dense mangrove forests is unavailable, due to the inability to penetrate their dense and complex canopy structures. The DTM is a stable constant for mangrove forests, because they mainly grow over even terrain [42]. The DSM can thus be considered to measure the relative heights of mangrove trees instead of the canopy height model (CHM). Previous studies have shown that the DSM derived from SfM and aerial photographs can solve the saturation problem and improve the estimation accuracy of biomass [82,83].

In this study, the P3, B, G, and R variables are least important (Figure 4). These variables with relatively low importance may be caused by the insensitivity to AGB estimation or be affected by multicollinearity. For example, the near-infrared (NIR) band of optical images is more widely used to estimate vegetation biomass content, because of its spectral reflection features in green vegetation, and visible (red, green, and blue) bands are usually used to emphasize vegetation health and classification, thus, they may be insensitive to AGB estimation. However, if the features have a correlation, it can be challenging to rank the importance of the features.

#### *4.3. Spatial Distribution Patterns of AGB of Mangrove Plantation*

As an initial effort for restoration and reforestation, the mangrove species *S. apetala* was introduced to the study area in 1999. As a result of its ability to spread, the artificial planting of *S. apetala* is becoming increasingly controversial as it may invade other mangrove ecosystems. The map of the AGB of the mangrove plantation derived here can provide baseline data for subsequent analyses and applications (e.g., carbon sequestration). The AGB values were predicted and mapped by a model that used GF-2 optical, GF-3 SAR, and UAV-based DSM data. The *S. apetala* afforestation process runs seaward from land, which implies a gradient distribution of AGB. The spatial distribution of the resulting AGB corresponded to the sequence of the mangrove plantation over time. To further verify the reliability of the model and understand the spatial distribution of the AGB, areas occupied by the mangrove plantation that had grown before 2011 were extracted by WorldView-2 images [43]. They exhibited significantly higher AGB values than the other areas, due to their age and fast growth. This is shown in Figure 7. The results thus indicate that the map of the AGB of the unevenly aged mangrove plantation showed a greater heterogeneity of AGB values.

#### *4.4. Limitation and Sources of Errors*

We found a reasonable relationship among GF-2 optical, GF-3 SAR images and, field measured AGB by using the RF algorithm. The combination of multi-source datasets (GF-2, GF-3 images, and DSM) yielded a higher estimation accuracy. However, there are some limitations and sources of errors from the AGB estimation using multi-source images, caused by position errors of geometric calibration, and the time difference of images and field measurements.

The errors of geometric calibration among multi-source images were unavoidable, though we used a geometric calibration to a 1:10,000 topographic map using ground control. The mismatch of the pixels derived from multi-source may cause the uncertainty of AGB estimation. Similarly, we go<sup>t</sup> the AGB samples by field investigation, and the closest remotely sensed data available. The differences in time acquisitions may bring additional sources of errors in the AGB estimation, due to the fast-growing characteristic of the *S. apetala*. The study from Ren et al. (2010) suggested that AGB accumulation rates at the *S. apetala* plantations decreased with the stand ages [48]. The AGB accumulation rates were 20.3 t/ha from 4 to 5 years stand, 5.6 t/ha from 5 to 8 years stand, and 2.85 t/ha from 8 to 10 years

stand, respectively. In our study area, most of the plantations have reached over 5 years stand [84], and previous study has demonstrated mean AGB accumulation of *S. apetala* over the study area to be 4.17 t/ha per year [85]. The average of observed values of this study is 155.4 t/ha, which may cause a 2.68% error of AGB—which is within the acceptable range—due to the different date between the field data and the images. Therefore, the inevitable difference of one year between the remote sensing images and in situ measurements can be deemed acceptable.

**Figure 7.** The predicted map of AGB values in 2016. (**<sup>a</sup>**–**f**) represented the partial enlarged detail of predicted mangrove AGB overlaid with the map of mangrove plantation from before 2011.
