#### 3.1. Field Biomass Estimation

Field surveys are important for collecting in situ data for accuracy analysis of the satellite-based estimates. We organized an intensive field campaign during April 2015 to collect the ground truth data. In total, 110 sample plots were established in the study area. The size of a sample plot is 1 ha (100 × 100 m). We measured the diameter at breast height (D_{1.3 m}) and tree height (H).

We used allometric equations for calculating above ground biomass (AGB). The allometric equations were developed by the UN-REDD Vietnam program for the Central Highlands region of Vietnam [

63]. It provides separate equations for calculating the AGB of deciduous forest type (Equation (1)) and evergreen forest type (Equation (2)). The individual tree diameter data at breast height 1.3 m (D) and tree height (H) were used to calculate the above ground biomass.

In Equations (1) and (2), AGB is the aboveground biomass of a tree in kilograms (kg), D is the diameter at breast height (1.3 m) in meters (m), H is the total tree height in meters (m), and WD is the wood density (tons dry matter/m

^{3} fresh volume) (IPCC, 2003) [

64].

The summary of the inventory data is described in

Table 4 and

Table 5 for the training and validation plots, respectively. The distribution of the sample plot positions is shown in

Figure 1.

#### 3.2. Processing of Satellite Data

The digital number (DN) values of the ALOS-2 SAR images in both the HH and HV polarizations were calibrated by calculating the backscattering intensity using Equation (3) [

65]:

In Equation (3), σ° is the sigma naught backscattering intensity and CF is the calibration factor, which is currently set as −83 (JAXA, 2014) [

65].

A three-component scattering model was used to decompose polarimetric SAR images. The covariance matrix approach is used to deal with the non-reflection symmetric scattering case, which describes double bounce (

${\mathsf{\sigma}\xb0}_{\mathrm{tree}}$), and surface (

${\mathsf{\sigma}\xb0}_{\mathrm{ground}}$) and volume scattering (

${\mathsf{\sigma}\xb0}_{\mathrm{vegetation}}$) [

66,

67].

The scattering matrix for the double-bounce scattering is given in Equation (4).

where

$\alpha $ $=\frac{{S}_{HH}-{S}_{VV}}{{S}_{HH}+{S}_{VV}}$ and

$\left[\alpha \right]<1$The scattering matrix for the surface scattering is given in Equation (5).

where

$\beta $=

$\frac{{R}_{h}-{R}_{v}}{{R}_{h}+{R}_{v}}$ gives the Fresnel reflection coefficients for horizontal and vertical polarizations.

The scattering matrix for the volume scattering is given in Equation (6).

The backscattering from vegetation and ground surface is shown in Equation (7) based on Poolla [

22]:

In Equation (7), σ°

_{forest} is backscatter from forest objects, σ°

_{vegetation} is backscatter from vegetation objects, σ°

_{ground} is backscatter from the ground surface, and T

_{tree} is tree transmissivity. Equation (7) can be rewritten as shown in Equation (8) for the ALOS-2 SAR data used in this study:

In Equation (8), σ°_{HV} is the backscattering value from HV polarization and σ°_{HH} is the backscattering value from HH polarization.

The Gray-Level Co-Occurrence Matrix was used to calculate the texture values, which is a function of both the angular relationship and distance between two neighboring pixels [

68]. We used a window size of 5 × 5, and took the average of four directions: horizontal, vertical, and two diagonals. In this study, we used eight texture values from ALOS-2 SAR image including, Contrast, Correlation, Dissimilarity, Entropy, Homogeneity, Mean, Second Moment, and Variance [

68,

69]. The formulas for the texture measurements used in this study are shown in Equations (9)–(16):

In Equations (9)–(16), p (i,j) is the normalized co-occurrence matrix such that sum (i,j = 0, n - 1, p(i,j)) = 1 and µ_{x}, µ_{y}, σ_{x}, σ_{y} are the means and standard deviations of p_{x}, p_{y}.

From the Landsat 8 image, Normalized Difference Vegetation Index (NDVI) was calculated [

70]. Previous studies have shown that the NDVI value has good correlation with the biomass [

50,

71,

72,

73]. In this forest site, plants start sprouting from April, reach their peak in October, and start to fall in November. So the NDVI value of October is taken as the maximum NDVI. The NDVI (Equation (17)) was calculated using the surface reflectance value of the near infrared (

R_{N}) and red (

R_{R}) bands:

#### 3.3. Accuracy Analysis

For each plot, the mean backscattering intensity of HV polarization from ALOS-2 SAR data, mean NDVI values from Landsat 8 data, and mean texture values from SAR and NDVI data were calculated. Out of 110 plots, 55 plots were randomly chosen as the training plots and another 55 plots as the validation plots. The sensitivity of the different parameters to the biomass was statistically analyzed by using simple linear regression and multiple linear regression analysis. The coefficient of determination (R^{2}) in Equation (18) and root mean square error (RMSE) in Equation (19) were used as the metrics for evaluating the relationships. In addition, RMSE% was also calculated by dividing the RMSE value by the mean of the observed biomass values and multiplying by 100.

In Equations (18) and (19), predicted biomass is the biomass value obtained from the model, observed biomass is the biomass value from the inventory data, and N is the number of sample plots used.

The detailed methodology of this study is shown in the flow chart in

Figure 2.