## 2 Materials and Methods
### Methods  
The AGB was estimated from allometric equations based on the tree height and circumference measured in the field. A collection of allometric equations (Table 1) was used to estimate AGB for each individual tree based on D and H as the dependent variables. The allometric equations were adapted from [PERSON] et al. [15], who studied the biomass distribution of 25 species of perennial woods, including mahagony, acacia, sonokeling, teak, sengon, and others, in a community forest in Nglanggeran Village, Indonesia. In the current study, a separate allometric equation was selected for each species as it has been proven that biomass predicting power is higher for species-specific models than for all species combined [7]. Thereafter, the AGB estimates for each individual tree were added to extract the representative AGB of each plot (in ton/ha). The Sentinel-2 image was first atmospherically corrected with the Sen2 Cor plugin version 2.8 [80] to construct the level 2A surface reflectance product. Sentinel-2 bands inherit various spatial resolutions (at 10, 20, and 60 m), and the spatial resolution of each band was kept unchanged during the atmospheric correction. The bands 1, 9, and 10, acquired at 60 m spatial resolution, were dismissed from further processing as they relate mainly to cloud screening and atmospheric correction. Finally, the bands acquired at 20 m spatial resolution were resampled to 10 m with the nearest neighbor algorithm to retain the native spectral information of the image. In regard to the Sentinel-1 imagery pre-processing, several studies have presented efficient processing workflows for a variety of applications (e.g., [81; 82; 83; 84]). In the current paper, we follow a baseline pre-processing of sequential steps undertaken with the open-source software Sentinel Application Platform (SNAP) version 8.0.0. First, the orbit file was applied based on 3 polynomial degree followed by the thermal noise removal. Then, the sigma nought (\(o^{0}\)) was calculated and the Lee Sigma speckle filter with a window size 7 \(\times\) 7 and sigma = 0.9 was applied on both polarizations to eliminate noise. Thereafter, a terrain correction operation was implemented using the Shuttle Radar Topography Mission (SRTM) 3 sec Digital Elevation Model (DEM) and bilinear interpolation at 10 m spatial resolution, and, finally, the pixel values were converted to dB. The two Sentinel images were then subset to the Girasekar and Jetis FMU extent (Figure 1). The nearest neighbor resampling algorithm was used to preserve the raw pixel values. Thereafter, all Sentinel-1 and Sentinel-2 bands were stacked and reprojected to the WGS84 coordinate system. Correspondingly, the GNSS coordinates recorded at the center of each field plot were converted from Degrees-Minutes-Seconds (DMS) to decimal degrees. Subsequently, the Sentinel-1 and Sentinel-2 band values of the pixels containing the aforementioned coordinates were extracted. The correlation between the spectral indices and the spatially corresponding in situ-collected data was examined through the exhaustive combination of the Ratio Spectral Index (RSI) and the Normalised Difference Spectral Index (NDSI) spectral indices, which are defined in Equations (1) and (2). \[\mathrm{RSI_{i,j}=R_{i}/R_{j}} \tag{1}\]  
\[\mathrm{NDSI_{i,j}=(R_{i}-R_{j})/(R_{i}+R_{j})} \tag{2}\]  
where \(R_{i}\) and \(R_{j}\) are the reflectance values of bands i and j, respectively. The coefficient of determination (R\({}^{2}\)) and the \(p\)-value (\(p\)) at the critical thresholds (i.e., 0.050, 0.010, and 0.001) were used as the metrics to evaluate the performance of the statistical relationship. Cumulative R\({}^{2}\) and \(p\) graphs were constructed to aid the visualization and comparison between each pair of bands as in similar studies [59; 85; 86; 87].