**1. Introduction**

Mangrove forests are among the most important components of natural ecosystems. They perform a wide range of crucial functions, such as mitigating the e ffects of tropical typhoons and tsunami, reducing coastal erosion, and storing huge amounts of blue carbon [1,2]. Despite their functions and benefits, mangrove forests have been reduced and degraded worldwide, more seriously in South East Asia, where the decimation rate reached its highest level in the last 50 years [3,4]. The driving factors of mangrove deforestation and degradation are conversion to shrimp aquaculture, agriculture (particularly rice and oil palm in West Africa and Southeast Asia), urban development, poor governance, and overexploitation [3,5]. Unfortunately, the loss of mangrove carbon on large spatial scales is little understood. Without this knowledge, we cannot mitigate the global loss of mangrove habitats [6].

Land-cover change is thought to alter the above-ground biomass (AGB) in the tropical areas [7–9]. By mapping the spatial distribution of mangrove AGB and the carbon stocks associated with external factors, we could detect the changes in mangrove ecosystems, better understand the drivers of these changes, and reduce the uncertainty in estimating the loss of mangrove ecosystem services. A precise estimation of mangrove AGB is required for sustainably preserving and protecting mangrove ecosystems from loss and degradation under climate change and accelerated global warming. However, the complex structure of mangrove ecosystems hindered quantitative estimates of mangrove AGB. Especially, the biosphere reserves of mangroves are characterized by multiple species, very high diversity, and large spatial distributions. During the last 30 years, AGB retrieval of mangroves has been investigated worldwide [10–14]. Mangrove AGB can be accurately estimated from field-based measurements or forest inventory data. However, these approaches are disadvantaged by high cost and site-selection biases [15]. Cost-e ffective and accurate retrieval techniques for mangrove AGB in tropical and semi-tropical areas would provide baseline data for the monitoring, reporting, and verification schemes adopted in climate-change mitigation strategies, such as Blue Carbon projects and the United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD+) program in the tropics [16].

In recent years, mangrove AGBs have been increasingly mapped using earth observation (EO) data collected by optical sensors [17–19], synthetic aperture radar (SAR) data [13,20,21], airborne LiDAR [22,23], and LiDAR data acquired form unmanned aerial vehicles (UAV) [24,25]. A few attempts combined the data of multispectral and SAR sensors for mangrove AGB retrieval in tropical regions. Fused data are particularly useful in biosphere reserves comprising multiple mangrove species and rich biodiversity. In such systems, the spatial distribution of the mangrove AGB is di fficult to estimate with su fficient accuracy. By accurately estimating the mangrove AGB in biosphere reserves, we could effectively monitor their mangrove ecosystems and implement sustainable mangrove conservation and management.

Models for estimating AGB range from simple to multi-linear regression approaches [13,21,24] to sophisticated machine learning (ML) methods [17,18,26]. For mapping and estimating forest AGBs, non-parametric approaches using various ML algorithms have proven more e ffective than parametric methods using linear models. Meanwhile, numerous EO datasets have been compiled from optical, SAR, and LiDAR data. These data are commonly retrieved from non-parametric regression techniques such as the random forest regression (RFR) algorithm [17,25,27], artificial neuron networks (ANN) [26], and support vector regression (SVR) [28,29]. Recently, gradient boosting decision trees (GBDT) e ffectively solved regression problems such as evaporation prediction [30] and oil price estimation [31]. The extreme gradient boosting regression (XGBR) algorithm is a particularly potent tool in environmental problems in environmental problems such as urban heat islands [32], algal blooming [33], and energy-supply security issues [34]. However, to our knowledge, the usefulness of the XGBR algorithm in forest AGB estimation, particularly in tropical mangrove habitats, has not been quantified. Especially, the current literature seems to lack a quantitative comparison of state-of-the-art ML techniques for estimating AGBs in di fferent forest ecosystems.

To overcome these challenges, we estimated the mangrove AGB in the Can Gio biosphere reserve (South Vietnam) using an ML model and the fused data of the Sentinel-2 (S2) MSI and ALOS-2 PALSAR-2 sensors. We selected Sentinel-2 MSI because the multispectral bands of S-2 reflect the forest stand structures such as stem volume, whereas the longer wavelengths of the dual polarimetric (HH, HV) mode of the ALOS-2 PALSAR-2 sensor can penetrate mangrove forest canopies. The fused S2 MSI and ALOS-2 PALSAR-2 data were processed by a nonlinear regression model in the XGBR algorithm, providing the first estimation of mangrove AGB in the Can Gio biosphere reserve (CGBRS). Additionally, the performance of the XGBR model was compared with those of other GBDT techniques and several well-known ML algorithms (SVR, GPR, and RFR) on mangrove AGB estimation in the same study area. Incorporating the S-2 MSI and ALOS-2 PALSAR-2 data into the proposed model was found to improve the mangrove AGB estimation in a Vietnamese biosphere reserve and is potentially applicable to mangrove conservation in other biosphere reserves.

#### **2. Materials and Methods**
