{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:29:46Z","timestamp":1783434586903,"version":"3.54.6"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T00:00:00Z","timestamp":1582934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study investigates the effectiveness 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 coefficient of determination (R2)) 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 R2 = 0.805, RMSE = 28.13 Mg ha\u22121, 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\u22121 (average = 106.93 Mg ha\u22121). 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 different geographical and climatic ecosystems.<\/jats:p>","DOI":"10.3390\/rs12050777","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["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"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6422-2847","authenticated-orcid":false,"given":"Tien Dat","family":"Pham","sequence":"first","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nga Nhu","family":"Le","sequence":"additional","affiliation":[{"name":"Department of Marine Mechanics and Environment, Institute of Mechanics, Vietnam Academy of Science and Technology (VAST), 264 Doi Can street, Ba Dinh district, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-8602","authenticated-orcid":false,"given":"Nam Thang","family":"Ha","sequence":"additional","affiliation":[{"name":"Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 530000, Vietnam"},{"name":"Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6841-1752","authenticated-orcid":false,"given":"Luong Viet","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Remote Sensing Application Department, Space Technology Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet street, Cau Giay district, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7321-4590","authenticated-orcid":false,"given":"Naoto","family":"Yokoya","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tu Trong","family":"To","sequence":"additional","affiliation":[{"name":"Remote Sensing Application Department, Space Technology Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet street, Cau Giay district, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong Xuan","family":"Trinh","sequence":"additional","affiliation":[{"name":"Remote Sensing Application Department, Space Technology Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet street, Cau Giay district, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lap Quoc","family":"Kieu","sequence":"additional","affiliation":[{"name":"Thai Nguyen University of Sciences, Tan Thinh Ward, Thai Nguyen City Thai Nguyen University of Sciences, Tan Thinh Ward, Thai Nguyen City 250000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9138-6601","authenticated-orcid":false,"given":"Wataru","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.4155\/cmt.12.20","article-title":"Carbon sequestration in mangrove forests","volume":"3","author":"Alongi","year":"2012","journal-title":"Carbon Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ecoser.2012.06.003","article-title":"Ecosystem service values for mangroves in Southeast Asia: A meta-analysis and value transfer application","volume":"1","author":"Brander","year":"2012","journal-title":"Ecosyst. 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