*2.5. Topography Data*

The GLAS parameters are related to forest structure and terrain variation, so we used topography data to extrapolate from discrete GLAS data into spatial continuous layers. We selected the CSRTM digital elevation model (DEM) provided by Zhao et al. [55]. The CSRTM is a corrected product from the Shuttle Radar Topography Mission (SRTM), which reduced the vertical errors of SRTM at vegetated areas. To be consistent with other datasets, we resampled the CSRTM DEM into 250-m resolution using a bilinear method for further interpolation. The slope (denoted by tangent values of slope) was calculated from the resampled CSRTM DEM.

#### *2.6. Mangrove AGB Estimation Methods*

As mentioned, we estimated global mangrove AGB using a methodology that had been successfully implemented to estimate forest AGB at both national and global scales [46,47]. We modified the step regarding plot location uncertainty to account specifically for the distribution of mangrove. We did not use a land cover map in the random forest regression analysis as we assumed all areas were mangrove based on our data collection methods previous described. As shown in Figure 1, the estimation of mangrove AGB is generally divided into four major steps.

First, discrete GLAS points were interpolated to create continuous spatial layers using the random forest algorithm. The GLAS points were filtered using a 100-km coastline buffer and aggregated into 250-m pixels using the average value of the GLAS full waveform parameter within each pixel. These pixels were then used as training data to build the random forest model created to extrapolate the GLAS parameters along with other predictor layers (cumulative EVI, DEM, slope, climate surfaces) using the randomForest R package [63].

Second, we generated a circular buffer for each plot measurements with a 500-m radius to reduce uncertainty related to plot location. Since mangrove has a much smaller distribution than other forest types, we could not use the point-radius method suggested by Su et al. to reduce geolocation uncertainty [47]. Using their Monte-Carlo simulation method, generating plot sets with location errors of 1 or 10 km would relocate many mangrove plots into the ocean. To avoid this issue, we used the circular buffer method. Most latitudes and longitudes in our field observation data were accurate to 0.01◦, corresponding to ~1km. We, therefore, adopted a 500-m radius to reduce location uncertainty.

Third, an initial global mangrove AGB map was created using the random forest method. Pixels for each explanatory layer within a plot buffer were averaged and used as explanatory variables to build a regression model from plot measurements. We randomly chose 70% of the plots (239 plots) to train the model and used the remaining 30% (103 plots) to validate the mangrove AGB estimation model. The three extrapolated GLAS parameters and the other nine parameters in Table 1 were used in the regression model to generate the outputs needed to produce this initial mangrove AGB map.

Finally, we used a mangrove extent map from Spalding et al. as a mask for our initial mangrove AGB map, eliminating areas outside of identified mangrove forests. [19]. The final global mangrove AGB map was obtained by setting AGB value in areas outside the mangrove extent to 0 Mg/ha.
