{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:00:13Z","timestamp":1783180813665,"version":"3.54.6"},"reference-count":91,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"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 proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R2) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R2 = 0.683, RMSE = 25.08 Mg\u00b7ha\u22121) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg\u00b7ha\u22121 to 142 Mg\u00b7ha\u22121 (with an average of 72.47 Mg\u00b7ha\u22121). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.<\/jats:p>","DOI":"10.3390\/rs12081334","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T10:46:22Z","timestamp":1587638782000},"page":"1334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":133,"title":["Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta 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"}]},{"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":"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\/0000-0002-4661-8602","authenticated-orcid":false,"given":"Nam Thang","family":"Ha","sequence":"additional","affiliation":[{"name":"Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 53000, Vietnam"},{"name":"Environmental Research Institute, School of Science, University of Waikato, Hamilton 3216, New Zealand"}],"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"}]},{"given":"Thi Thu Trang","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Chemistry, VNU University of Science, Vietnam National University, Hanoi, 19 Le Thanh Tong, Hoan Kiem, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thi Huong","family":"Dao","sequence":"additional","affiliation":[{"name":"Faculty of Chemistry, VNU University of Science, Vietnam National University, Hanoi, 19 Le Thanh Tong, Hoan Kiem, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thuy Thi Phuong","family":"Vu","sequence":"additional","affiliation":[{"name":"Forest Inventory and Planning Institute (FIPI), Ministry of Agriculture and Rural Development (MARD), Vinh Quynh, Thanh Tri, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9087-7417","authenticated-orcid":false,"given":"Tien Duc","family":"Pham","sequence":"additional","affiliation":[{"name":"Faculty of Chemistry, VNU University of Science, Vietnam National University, Hanoi, 19 Le Thanh Tong, Hoan Kiem, Hanoi 100000, 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":[[2020,4,23]]},"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":"726","DOI":"10.1111\/geb.12155","article-title":"Ecological role and services of tropical mangrove ecosystems: A reassessment","volume":"23","author":"Lee","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1146\/annurev-environ-101718-033302","article-title":"The State of the World\u2019s Mangrove Forests: Past, Present, and Future","volume":"44","author":"Friess","year":"2019","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1073\/pnas.1510272113","article-title":"Rates and drivers of mangrove deforestation in Southeast Asia, 2000\u20132012","volume":"113","author":"Richards","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s41558-018-0090-4","article-title":"Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012","volume":"8","author":"Hamilton","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.marpol.2016.01.011","article-title":"Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD+ a solution?","volume":"66","author":"Ahmed","year":"2016","journal-title":"Mar. Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7761","DOI":"10.1080\/01431161.2018.1471544","article-title":"Estimating Aboveground Biomass of a Mangrove Plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data","volume":"39","author":"Pham","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","unstructured":"Kauffman, J.B., and Donato, D.C. (2012). Protocols for the Measurement, Monitoring and Reporting of Structure, Biomass, and Carbon Stocks in Mangrove Forests, CIFOR."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Maeda, Y., Fukushima, A., Imai, Y., Tanahashi, Y., Nakama, E., Ohta, S., Kawazoe, K., and Akune, N. (2016). Estimating carbon stock changes of mangrove forests using satellite imagery and airborne lidar data in the south sumatra state, indonesia. ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 705\u2013709.","DOI":"10.5194\/isprsarchives-XLI-B8-705-2016"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Navarro, J.A., Algeet, N., Fern\u00e1ndez-Landa, A., Esteban, J., Rodr\u00edguez-Noriega, P., and Guill\u00e9n-Climent, M.L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal. Remote Sens., 11.","DOI":"10.3390\/rs11010077"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"025012","DOI":"10.1088\/1748-9326\/aa9f03","article-title":"Estimating mangrove aboveground biomass from airborne LiDAR data: A case study from the Zambezi River delta","volume":"13","author":"Fatoyinbo","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Makowski, C., and Finkl, C.W. (2018). Remote Sensing of Mangrove Forests: Current Techniques and Existing Databases. Threats to Mangrove Forests: Hazards, Vulnerability, and Management, Springer International Publishing.","DOI":"10.1007\/978-3-319-73016-5"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Xia, J., Ha, N.T., Bui, D.T., Le, N.N., and Takeuchi, W. (2019). A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010\u20132018. Sensors, 19.","DOI":"10.3390\/s19081933"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1126\/science.aaw0809","article-title":"The value of small mangrove patches","volume":"363","author":"Curnick","year":"2019","journal-title":"Science"},{"key":"ref_16","first-page":"101986","article-title":"Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery","volume":"85","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1007\/s10531-019-01698-8","article-title":"Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data","volume":"28","author":"Pandey","year":"2019","journal-title":"Biodivers. Conserv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"026010","DOI":"10.1117\/1.JRS.11.026010","article-title":"Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam","volume":"11","author":"Pham","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2014.04.029","article-title":"L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia","volume":"155","author":"Hamdan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.apgeog.2013.09.024","article-title":"Mangrove biomass estimation in Southwest Thailand using machine learning","volume":"45","author":"Jachowski","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/15481603.2016.1269869","article-title":"Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks","volume":"54","author":"Pham","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.03.013","article-title":"Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms","volume":"128","author":"Pham","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"921","DOI":"10.14358\/PERS.74.7.921","article-title":"Neural Network Classification of Mangrove Species from Multi-seasonal Ikonos Imagery","volume":"74","author":"Wang","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/LGRS.2009.2014398","article-title":"Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery","volume":"6","author":"Huang","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1177\/0309133310385371","article-title":"Satellite remote sensing of mangrove forests: Recent advances and future opportunities","volume":"35","author":"Heumann","year":"2011","journal-title":"Prog. Phys. Geogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"035010","DOI":"10.1117\/1.JRS.10.035010","article-title":"Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery","volume":"10","author":"Wu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/07038992.2016.1217485","article-title":"A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation","volume":"42","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2019.02.022","article-title":"Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau","volume":"225","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2018.04.026","article-title":"Adaptive neural network based on segmented particle swarm optimization for remote-sensing estimations of vegetation biomass","volume":"211","author":"Gao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Song, Y., Jiao, X., Yang, S., Zhang, S., Qiao, Y., Liu, Z., and Zhang, L. (2019). Combining Multiple Factors of LightGBM and XGBoost Algorithms to Predict the Morbidity of Double-High Disease. International Conference of Pioneering Computer Scientists, Engineers and Educators, Springer.","DOI":"10.1007\/978-981-15-0121-0_50"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, P., and Zhang, J.-S. (2018). A New Hybrid Method for China\u2019s Energy Supply Security Forecasting Based on ARIMA and XGBoost. Energies, 11.","DOI":"10.3390\/en11071687"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/B978-0-12-818597-1.50019-9","article-title":"Gradient Boosted Decision Trees for Lithology Classification","volume":"Volume 47","author":"Laird","year":"2019","journal-title":"Computer Aided Chemical Engineering"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ocecoaman.2018.03.022","article-title":"Bringing social and cultural considerations into environmental management for vulnerable coastal communities: Responses to environmental change in Xuan Thuy National Park, Nam Dinh Province, Vietnam","volume":"158","author":"Leslie","year":"2018","journal-title":"Ocean Coast. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.quaint.2005.05.008","article-title":"Climate change and human impact on the Song Hong (Red River) Delta, Vietnam, during the Holocene","volume":"144","author":"Li","year":"2006","journal-title":"Quat. Int."},{"key":"ref_37","unstructured":"Hong, P.N., and San, H.T. (1993). Mangroves of Vietnam, IUCN."},{"key":"ref_38","unstructured":"Hong, P.N. (2004). Mangrove Ecosystem in the Red River Coastal Zone: Biodiversity, Ecology, Socio-Economic, Management and Education, Agricultural Publishing House."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/0378-1127(89)90034-0","article-title":"Allometric relationships for estimating above-ground biomass in six mangrove species","volume":"27","author":"Clough","year":"1989","journal-title":"For. Ecol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1017\/S0266467405002476","article-title":"Common allometric equations for estimating the tree weight of mangroves","volume":"21","author":"Komiyama","year":"2005","journal-title":"J. Trop. Ecol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1016\/j.proenv.2011.09.343","article-title":"Estimation of aboveground biomass of different mangrove trees based on canopy diameter and tree height","volume":"10","author":"Fu","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3915","DOI":"10.1109\/TGRS.2009.2023909","article-title":"PALSAR Radiometric and Geometric Calibration","volume":"47","author":"Shimada","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 sen2cor: L2a processor for users. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18.","DOI":"10.3390\/ECRS-3-06201"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s13157-015-0660-4","article-title":"Estimation of Mangrove Carbon Stocks by Applying Remote Sensing and GIS Techniques","volume":"35","author":"Patil","year":"2015","journal-title":"Wetlands"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Tien Bui, D. (2018). Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vaglio Laurin, G., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., and Papale, D. (2017). Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates. Remote Sens., 9.","DOI":"10.3390\/rs9010018"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"083638","DOI":"10.1117\/1.JRS.8.083638","article-title":"Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data","volume":"8","author":"Manna","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and limits of vegetation indices for LAI and APAR assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Le, N.N., Ha, N.T., Nguyen, L.V., Xia, J., Yokoya, N., To, T.T., Trinh, H.X., Kieu, L.Q., and Takeuchi, W. (2020). 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. Remote Sens., 12.","DOI":"10.3390\/rs12050777"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_63","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","unstructured":"Sun, X., Liu, M., and Sima, Z. (2018). A novel cryptocurrency price trend forecasting model based on LightGBM. Financ. Res. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.elerap.2018.08.002","article-title":"Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning","volume":"31","author":"Ma","year":"2018","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Ann. Stat., 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.asoc.2018.10.036","article-title":"Feature selection based on artificial bee colony and gradient boosting decision tree","volume":"74","author":"Rao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_69","unstructured":"Nielsen, D. (2016). Tree Boosting with XGBoost-Why Does XGBoost Win \u201cEvery\u201d Machine Learning Competition?. [Master\u2019s Thesis, NTNU]."},{"key":"ref_70","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media."},{"key":"ref_71","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv."},{"key":"ref_72","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased boosting with categorical features. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_73","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_75","first-page":"175","article-title":"Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment","volume":"78","author":"Silveira","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_76","unstructured":"Jones, E., Oliphant, T., and Peterson, P. (2001). SciPy: Open Source Scientific Tools for Python, Available online: https:\/\/www.scienceopen.com\/document?vid=ab12905a-8a5b-43d8-a2bb-defc771410b9."},{"key":"ref_77","unstructured":"Davis, L. (1991). Handbook of Genetic Algorithms, Van Nostrand Reinhold."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1002\/ep.12888","article-title":"Applications of python to evaluate the performance of decision tree-based boosting algorithms","volume":"37","author":"Kadiyala","year":"2018","journal-title":"Environ. Prog. Sustain. Energy"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"044519","DOI":"10.1117\/1.JRS.13.044519","article-title":"Development of aboveground mangrove forests\u2019 biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic","volume":"13","author":"Darmawan","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s10310-013-0402-5","article-title":"Estimation of aboveground biomass in mangrove forests using high-resolution satellite data","volume":"19","author":"Hirata","year":"2014","journal-title":"J. For. Res."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Yokoya, N., Bui, D.T., Yoshino, K., and Friess, D.A. (2019). Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges. Remote Sens., 11.","DOI":"10.3390\/rs11030230"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s11273-015-9479-2","article-title":"Carbon stocks in artificially and naturally regenerated mangrove ecosystems in the Mekong Delta","volume":"24","author":"Nam","year":"2016","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/S0034-4257(99)00064-4","article-title":"Interpretation of Polarimetric Radar Signatures of Mangrove Forests","volume":"71","author":"Proisy","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1080\/17538947.2017.1301581","article-title":"Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon","volume":"10","author":"Feng","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_86","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1590\/S0044-59672005000200015","article-title":"Exploring TM image texture and its relationships with biomass estimation in Rond\u00f4nia, Brazilian Amazon","volume":"35","author":"Lu","year":"2005","journal-title":"Acta Amazonica"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10584-004-3155-5","article-title":"Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets","volume":"67","author":"Quegan","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Zuo, Z., Wang, R., and Wu, X. (2019). Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling. Remote Sens., 11.","DOI":"10.3390\/rs11182156"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jenvman.2013.11.037","article-title":"Carbon stocks and potential carbon storage in the mangrove forests of China","volume":"133","author":"Liu","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Jia, M., Wang, Z., Wang, C., Mao, D., and Zhang, Y. (2019). A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery. 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