*2.2. Field Investigation*

We conducted field investigations to obtain biomass samples from June to July in 2016. To ensure the availability of samples to build estimation models for the AGB, we collected 127 samples along almost all the accessible tidal creeks to account for the variation in the biomass of all stages of tree growth. The samples were located in the low-, middle-, and high-intertidal zones (Figure 1d). For each sample, we measured the height and diameter at breast height (DBH) of each tree and recorded data for trees within a 10 × 10 m quadrat. Tree height was measured by a handheld laser range finder (precision of 1 m; Trueyard SP-1500H, Trueyard Optical Instruments co.), and DBH higher than 5 cm

was recorded by a breast diameter ruler at 1.3 m above the ground. To match the locations of the samples and overlaid images coordinates, the four vertices and center of each quadrat were recorded by a submeter-accurate GPS. The precise locations of the plots were recorded with the assistance of the GPS and high-resolution images (UAV images with 0.12 m spatial resolution). Details of the locations of the quadrats, such as distance to the shore, were identified in and marked on the images. Using the measured tree heights and DBHs during the field survey, we calculated the AGB of each mangrove tree using allometric equations [49], and computed the sum of the AGB of all trees within a quadrat, to represent the AGB of a sample.

**Figure 1.** Images of the study area. (**a**) Gaofen-2 (GF-2) images (bands 4, 3, 2 false-color combinations); (**b**) HH, HV, and VV color composition of Gaofen-3 (GF-3) images; (**c**) digital surface model (DSM) data derived from Unmanned Aerial Vehicle (UAV) images, and (**d**) spatial distribution of *S. apetala* and field sampling on Qi'ao Island.

#### *2.3. Remote Sensing Data and Preprocessing Procedure*

#### 2.3.1. GF2 Optical Data

Gaofen-2 (GF-2) captures high-resolution images. It was launched by China National Space Administration (CNSA), Beijing, China, in August 2014. It has been applied to land monitoring, urban planning, and resource surveys [50]. GF-2 images have a panchromatic band (1-m resolution) and four multispectral bands (4-m resolution): red (R), green (G), blue (B), and near-infrared (NIR). We obtained GF-2 multispectral images on 15 February 2017 from Land Observation Satellite Data Service Platform (Figure 1a).

The pre-processing of the GF-2 images—including radiation calibration, atmospheric correction, and geometric correction—was carried out using the ENVI 5.4.1 software package. Atmospheric correction of the images was carried out using the fast line-of-sight atmospheric analysis of the spectral hypercubes (FLAASH) model with the ENVI module. The images were also geo-rectified to a 1:10,000 topographic map using ground control points, to ensure that the position error was smaller than 0.5 pixels.

After preprocessing, the images were used to calculate four vegetation indices (VIs)—difference vegetation index (DVI), ratio vegetation index (RVI), normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI)—as input variables to estimate the AGB of the mangrove forest (Table 1).


**Table 1.** List of vegetation indices extracted from GF-2 optical data.

#### 2.3.2. GF3 SAR Data

The Gaofen-3 (GF-3), launched in August 2016, was developed by the China National Space Administration (CNSA), and is the first Chinese satellite that collects multi-polarized C-band SAR data. GF-3 images are the only radar images in the Chinese High-resolution Earth Observation System [55]. They have 12 imaging modes, ranging from single to dual and full polarization, with a resolution of 1 to 500 m and have a revisiting period of 3.5 days at most to the same point on Earth. Such characteristics render the GF-3 suitable for resource monitoring. We obtained fully polarimetric (FP) SAR data (HH, HV, VH, VV) from the Land Observation Satellite Data Service Platform on 5 August 2017, in the Quad-Polarization Stripmap 1 (QPS1) imaging mode at an azimuth resolution of 5.3 m, range resolution of 2.25 m, range of incidence angle of 29.63◦, and in the single-look complex (SLC) format (Figure 1b and Table 2). Based on the original data, the image-related data were preprocessed to preserve phase and amplitude information in the complex images.

**Table 2.** Characteristics of GF-3 synthetic aperture radar (SAR) images.


(1) Preprocessing of GF-3 images

FP SAR images contain speckle noise and geometric distortions that can have a significant negative impact on features of the polarization of the target objects. Thus, a preprocessing sequence consisting of data import, multi-look processing, adaptive filtering, radiometric calibration, and the geometric correction was applied to the GF-3 images using the SARscape 5.4.1 module embedded into the ENVI software. The steps of the preprocessing are as follows:

a) The metadata of the SAR images was imported to obtain the slant-range resolution and angle of incidence. The ground-range resolution was then calculated using the following:

$$R\_{\text{ground}} = \frac{R\_{\text{slant}}}{\sin(\theta)}\tag{1}$$

where *Rground* represents ground-range resolution (4.5480 m), *Rslant* is the slant-range resolution (2.2484 m), and θ is the incidence angle (29.6281◦).

b) The multi-look method was applied to de-speckle and re-sample the full-polarization GF-3 images. The GF-3 SAR images had a 1 × 1 multilook (azimuth × range), and were resampled to a regular grid with 5 × 5 m pixels in terms of azimuth and range resolution (5.30 × 2.25 m).

c) After the multi-look processing, the images were preprocessed using adaptive filters to reduce speckle and to enhance the edges and other features. We applied the refined Lee method of adaptive filtering by setting a 5 × 5 m filter window.

d) Radiometric calibration was carried out to convert the intensity values to the calibrated backscattering coe fficient σ*<sup>o</sup>* (dB) of the normalized radar using the following equation [56]:

$$
\sigma^{\diamond} = 10 \times \lg(DN^2) + K \tag{2}
$$

where *DN* represents the pixel values of the complex images, and *K* is the calibration constant. This equation referred to ALOS satellite processing. The radiometric calibration is still being explored to better process GF-3 SAR images. Previous studies used equation 2 to perform radiometric calibration, and demonstrated its utility and obtained satisfactory results [57,58]. Further study for radiometric calibration of Gaofen-3 images is needed.

e) Finally, the corrected backscatter map was generated from the backscattering coe fficients to reduce the negative e ffect of the incidence angle on the radar's backscatter. Geometric correction is then performed to match the positions of the ground control points selected in the GF2 images to corresponding points in a 1:10000 topographic map, with the positional error smaller than 0.5 pixels.

(2) Variables derived from GF-3 images

The preprocessed images were used to acquire HV/HH-, VH/HH-, HV/VV-, and VH/VV-polarized data, by calculating the ratio of the backscattering coe fficients of di fferent polarimetric channels.

The FP SAR data allow us to identify the scattering mechanisms of di fferent types that can significantly improve the depiction of features of the target object. This was achieved by polarimetric decomposition techniques, to separate the received signals of the radar. Such analysis can help repose a simpler object susceptible to an easier physical interpretation as a combination of the scattering [59]. Coherent decomposition is used to measure the scattering matrix by the responses of coherent scatters [60]. The targets of coherent scatter are analyzed based on the Sinclair matrix (S) representing all polarimetric information. With linear horizontal (H) and vertical (V) polarizations, the Sinclair matrix can be expressed as follows:

$$\mathcal{S} = \begin{bmatrix} \begin{array}{cc} \text{s}\_{HH} & \text{s}\_{HV} \\\\ \text{s}\_{VH} & \text{s}\_{VV} \end{array} \end{bmatrix} \tag{3}$$

The Pauli and Krogager decomposition approaches were used to analyze the targets of coherent scatter based on the Sinclair matrix [61]. Both could be applied to a homogeneous distribution of mangrove species in the study area [62]. Pauli decomposition was used to extract features of the polarization of the objects by defining di fferent polarization fundamental matrices representing various types of objects. Pauli's polarimetric parameters were then decomposed into three elementary scattering mechanisms: odd-bounce scattering (P1), even-bounce scattering (P2), and volume scattering (P3). The Krogager decomposition aims to decompose the scattering matrix of a complex symmetric radar target into the physical interpretation of three components: sphere (KS1), diplane (KD3), and helix (KH2) [63].

The radar vegetation index (SAR-RVI), derived from FP SAR images, was used as a measure of the randomness of scattering from vegetation. It models the vegetation canopy as a collection of randomly oriented dipoles [64], and has yielded a good correlation between the SAR-RVI and the AGB. It was calculated by preprocessed FP data as follows [65]:

$$\text{SAR-RVI} = \frac{8 \times \sigma\_{HV}}{2 \times \sigma\_{HV} + \sigma\_{HV} + \sigma\_{VV}} \tag{4}$$

where σ*HH*, σ*HV*, σ*VH*, and σ*VV* represent the backscattering coe fficients of the polarimetric channels HH, HV, VH, and VV, respectively.

In this study, the 11 predictors derived from GF-3 SAR images—the four backscattering coe fficients (HH, HV, VH, and VV) of the FP channels, three Pauli polarimetric parameters (P1, P2, and P3), three Krogager polarimetric parameters (KS1, KD3, and KH2), and the SAR-RVI—were used as input parameters to build and predict the AGB of mangrove forests. They can reflect the di fferent properties of mangrove forests. The backscatter of HH is linked to both trunk and crown biomass, HV and VH return crown biomass, VV is dominated by branch biomass [66,67]. For the ratio of backscattering, they can potentially reduce topographic effects and forest structural effects, thereby increasing estimation accuracy [68]. The SAR-RVI reflects the canopy vegetation characteristics [64,69]. The Pauli decomposition can be used to separate the scattering matrix into simpler scattering responses related to single bouncing (e.g., canopy surface), double bouncing (e.g., trunk) and volume scattering (e.g., crown) [70,71]. The Krogager decomposition is related to surface, two, and three-sided corner reflectors [72]. The partial variables are shown in Figure 2.

**Figure 2.** The partial variables derived from SAR images. (**a**) HV/HH, (**b**) P1 of Pauli decomposition, (**c**) KD3 of Krogager decomposition, and (**d**) radar vegetation index (SAR-RVI).
