**Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam**

**Tien Dat Pham 1, Nga Nhu Le 2,\*, Nam Thang Ha 3,4, Luong Viet Nguyen 5, Junshi Xia 1, Naoto Yokoya 1, Tu Trong To 5, Hong Xuan Trinh 5, Lap Quoc Kieu 6 and Wataru Takeuchi 7**


Received: 22 January 2020; Accepted: 25 February 2020; Published: 29 February 2020

**Abstract:** This study investigates the e ffectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coe fficient of determination ( *R*2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with *R*<sup>2</sup> = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−<sup>1</sup> (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in di fferent geographical and climatic ecosystems.

**Keywords:** Sentinel-2; ALOS-2 PALSAR-2; mangrove; above-ground biomass; extreme gradient boosting; Can Gio biosphere reserve; Vietnam

 and
