{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T21:32:28Z","timestamp":1783287148748,"version":"3.54.6"},"reference-count":85,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T00:00:00Z","timestamp":1593043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of Science and Technology Program of Guangzhou City","award":["201804020016"],"award-info":[{"award-number":["201804020016"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M633023"],"award-info":[{"award-number":["2018M633023"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2017A020217003"],"award-info":[{"award-number":["2017A020217003"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2016A030313261"],"award-info":[{"award-number":["2016A030313261"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, few studies have attended to integrating these factors to build models of the AGB of mangrove plantations. The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations using Chinese satellite optical, SAR, and Unmanned Aerial Vehicle (UAV) data based digital surface models (DSM). This paper chose Qi\u2019ao Island, which forms the largest contiguous area of mangrove plantation in China, as the study area. Several field visits collected 127 AGB samples. The models for AGB estimation were developed using the random forest algorithm and integrating images from multiple sources: optical images from Gaofen-2 (GF-2), synthetic aperture radar (SAR) images from Gaofen-3 (GF-3), and UAV-based digital surface model (DSM) data. The performance of the models was assessed using the root-mean-square error (RMSE) and relative RMSE (RMSEr), based on five-fold cross-validation and stratified random sampling approach. The results showed that images from the GF-2 optical (RMSE = 33.49 t\/ha, RMSEr = 21.55%) or GF-3 SAR (RMSE = 35.32 t\/ha, RMSEr = 22.72%) can be used appropriately to monitor the AGB of the mangrove plantation. The AGB models derived from a combination of the GF-2 and GF-3 datasets yielded a higher accuracy (RMSE = 29.89 t\/ha, RMSEr = 19.23%) than models that used only one of them. The model that used both datasets showed a reduction of 2.32% and 3.49% in RMSEr over the GF-2 and GF-3 models, respectively. On the DSM dataset, the proposed model yielded the highest accuracy of AGB (RMSE = 25.69 t\/ha, RMSEr = 16.53%). The DSM data were identified as the most important variable, due to mitigating the saturation effect observed in the optical and SAR images for a dense AGB estimation of the mangroves. The resulting map, derived from the most accurate model, was consistent with the results of field investigations and the mangrove plantation sequences. Our results indicated that the AGB can be accurately measured by integrating images from the optical, SAR, and DSM datasets to adequately represent canopy-related information, forest structures, and tree heights.<\/jats:p>","DOI":"10.3390\/rs12122039","type":"journal-article","created":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T10:36:54Z","timestamp":1593081414000},"page":"2039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China\u2019s Largest Artificially Planted Mangroves"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0474-5945","authenticated-orcid":false,"given":"Yuanhui","family":"Zhu","sequence":"first","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1829-7557","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soe","family":"W. Myint","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China"},{"name":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6128-4404","authenticated-orcid":false,"given":"Zhenyu","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-2203","authenticated-orcid":false,"given":"Jingjing","family":"Cao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7202-3418","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"},{"name":"Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3173-4739","authenticated-orcid":false,"given":"Zhifeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Province Engineering Technology Research for Geographical Conditions Monitoring and Comprehensive Analysis, School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.ecolecon.2009.11.002","article-title":"Payments for ecosystem services as commodity fetishism","volume":"69","author":"Kosoy","year":"2010","journal-title":"Ecol. Econ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7679","DOI":"10.1080\/01431161.2019.1601289","article-title":"Recent advancement on estimation of blue carbon biomass using satellite-based approach","volume":"40","author":"Sani","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","unstructured":"Laffoley, D., and Grimsditch, G.D. (2009). The Management of Natural Coastal Carbon Sinks, IUCN."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bouillon, S., Borges, A.V., Casta\u00f1eda-Moya, E., Diele, K., Dittmar, T., Duke, N.C., Kristensen, E., Lee, S.Y., Marchand, C., and Middelburg, J.J. (2008). Mangrove production and carbon sinks: A revision of global budget estimates. Glob. Biogeochem. Cycles, 22.","DOI":"10.1029\/2007GB003052"},{"key":"ref_5","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. Remote Sens., 11.","DOI":"10.3390\/rs11172043"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dou, Z., Cui, L., Li, J., Zhu, Y., Gao, C., Pan, X., Lei, Y., Zhang, M., Zhao, X., and Li, W. (2018). Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China. Sustainability, 10.","DOI":"10.3390\/su10041127"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and distribution of mangrove forests of the world using earth observation satellite data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"188","DOI":"10.2747\/1548-1603.45.2.188","article-title":"Identifying Mangrove Species and Their Surrounding Land Use and Land Cover Classes Using an Object-Oriented Approach with a Lacunarity Spatial Measure","volume":"45","author":"Myint","year":"2008","journal-title":"GISci. Remote Sens."},{"key":"ref_10","first-page":"535","article-title":"Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China","volume":"73","author":"Jia","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ecolecon.2015.10.014","article-title":"Using REDD+ to balance timber production with conservation objectives in a mangrove forest in Malaysia","volume":"120","author":"Dargusch","year":"2015","journal-title":"Ecol. Econ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.3390\/rs5052257","article-title":"Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR","volume":"5","author":"Kankare","year":"2013","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fatoyinbo, T.E., Simard, M., Washington-Allen, R.A., and Shugart, H.H. (2008). Landscape-scale extent, height, biomass, and carbon estimation of Mozambique\u2019s mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci., 113.","DOI":"10.1029\/2007JG000551"},{"key":"ref_14","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_15","unstructured":"Sasmito, S.D., Murdiyarso, D., Wijaya, A., Purbopuspito, J., and Okimoto, Y. (2013, January 1). Remote sensing technique to assess aboveground biomass dynamics of mangrove ecosystems area in Segara Anakan, Central Java, Indonesia. Proceedings of the 34th Asian Conference on Remote Sensing 2013, ACRS 2013, Bali, Indonesia."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Y., and Li, M. (2019). Improving forest aboveground biomass (AGB) estimation by incorporating crown density and using landsat 8 OLI images of a subtropical forest in Western Hunan in Central China. Forests, 10.","DOI":"10.3390\/f10020104"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T., 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_19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2016.04.026","article-title":"Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data","volume":"183","author":"Aslan","year":"2016","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":"379","DOI":"10.1016\/j.rse.2007.01.009","article-title":"Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images","volume":"109","author":"Proisy","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1016\/j.rse.2011.05.017","article-title":"MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass","volume":"115","author":"Marsden","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"6765","DOI":"10.1080\/01431161.2010.512944","article-title":"Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties","volume":"32","author":"Chadwick","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s00468-015-1334-9","article-title":"Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data","volume":"30","author":"Olagoke","year":"2016","journal-title":"Trees"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Omar, H., Misman, M., and Kassim, A. (2017). Synergetic of PALSAR-2 and Sentinel-1A SAR polarimetry for retrieving aboveground biomass in dipterocarp forest of Malaysia. Appl. Sci., 7.","DOI":"10.3390\/app7070675"},{"key":"ref_27","first-page":"693","article-title":"Estimation of mangrove wetland aboveground biomass based on remote sensing data: A review","volume":"44","author":"Wu","year":"2013","journal-title":"J. South Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17097","DOI":"10.3390\/rs71215873","article-title":"Radarsat-2 backscattering for the modeling of biophysical parameters of regenerating mangrove forests","volume":"7","author":"Cougo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.05.004","article-title":"Applications of ALOS PALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicennia germinans) forest","volume":"82","author":"Kovacs","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lucas, R., Lule, A.V., Rodr\u00edguez, M.T., Kamal, M., Thomas, N., Asbridge, E., and Kuenzer, C. (2017). Spatial ecology of mangrove forests: A remote sensing perspective. Mangrove Ecosystems: A Global Biogeographic Perspective, Springer.","DOI":"10.1007\/978-3-319-62206-4_4"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2013.08.012","article-title":"Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass","volume":"139","author":"Tsui","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Haris, M., Ashraf, M., Ahsan, F., Athar, A., and Malik, M. (2018, January 3\u20134). Analysis of SAR images speckle reduction techniques. Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2018.8346335"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Shao, Z., and Zhang, L. (2016). Estimating forest aboveground biomass by combining optical and SAR data: A case study in Genhe, Inner Mongolia, China. Sensors, 16.","DOI":"10.3390\/s16060834"},{"key":"ref_36","first-page":"388","article-title":"L-band saturation level for aboveground biomass of dipterocarp forests in peninsular Malaysia","volume":"27","author":"Hamdan","year":"2015","journal-title":"J. Trop. For. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/01431161.2012.712224","article-title":"Height and biomass of mangroves in Africa from ICESat\/GLAS and SRTM","volume":"34","author":"Fatoyinbo","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2017.11.018","article-title":"Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar","volume":"136","author":"Fedrigo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.010","article-title":"Biomass and InSAR height relationship in a dense tropical forest","volume":"192","author":"Solberg","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.foreco.2017.12.049","article-title":"Managing mangrove forests from the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia","volume":"411","author":"Otero","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Liu, L., Myint, S., Wang, S., Liu, H., and He, Z. (2017). Exploring the potential of worldview-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9101060"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1672\/06-91.1","article-title":"Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning","volume":"28","author":"Liu","year":"2008","journal-title":"Wetlands"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1016\/j.ecoleng.2009.05.008","article-title":"Sonneratia apetala Buch.Ham in the mangrove ecosystems of China: An invasive species or restoration species?","volume":"35","author":"Ren","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11284-007-0393-9","article-title":"Restoration of mangrove plantations and colonisation by native species in Leizhou bay, South China","volume":"23","author":"Ren","year":"2008","journal-title":"Ecol. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s11104-009-0053-7","article-title":"Biomass accumulation and carbon storage of four different aged Sonneratia apetala plantations in Southern China","volume":"327","author":"Ren","year":"2010","journal-title":"Plant Soil"},{"key":"ref_49","first-page":"391","article-title":"Biomass and net productivity of Sonneratia apetala, S.caseolaris mangrove man-made forest","volume":"19","author":"Zan","year":"2001","journal-title":"J. Wuhan Bot. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/2150704X.2016.1142682","article-title":"Using GF-2 imagery and the conditional random field model for urban forest cover mapping","volume":"7","author":"Wang","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_51","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_52","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_53","unstructured":"Rouse, J.W., Haas, R., Schell, J., and Deering, D. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite\u20141 Symposium: NASA SP-351, Washington, DC, USA."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., and Fu, Q. (2017, January 19\u201322). Classification of Chinese GaoFen-3 fully-polarimetric SAR images: Initial results. Proceedings of the 2017 Progress in Electromagnetics Research Symposium\u2014Fall (PIERS\u2014FALL), Singapore.","DOI":"10.1109\/PIERS-FALL.2017.8293225"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","unstructured":"Li, X.-M., Zhang, T., Huang, B., and Jia, T. (2018). Capabilities of Chinese Gaofen-3 synthetic aperture radar in selected topics for coastal and ocean observations. Remote Sens., 10.","DOI":"10.3390\/rs10121929"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/JSTARS.2019.2911922","article-title":"Calibration of the copolarized backscattering measurements from Gaofen-3 synthetic aperture radar wave mode imagery","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TGRS.2013.2258675","article-title":"Integrating Color Features in Polarimetric SAR Image Classification","volume":"52","author":"Uhlmann","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/PROC.1965.4072","article-title":"Measurement of the target scattering matrix","volume":"53","author":"Huynen","year":"1965","journal-title":"Proc. IEEE"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1049\/el:19900979","article-title":"New decomposition of the radar target scattering matrix","volume":"26","author":"Krogager","year":"1990","journal-title":"Electron. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1109\/TGRS.2009.2014944","article-title":"A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data","volume":"47","author":"Kim","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1109\/JSTARS.2014.2322311","article-title":"RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring","volume":"7","author":"Wiseman","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1080\/01431169408954284","article-title":"Retrieval of forest biomass from SAR data","volume":"15","author":"Beaudoin","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/36.295053","article-title":"Mapping biomass of a northern forest using multifrequency SAR data","volume":"32","author":"Ranson","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1109\/TGRS.2012.2219872","article-title":"Forest biomass estimation using texture measurements of high-resolution dual-polarization C-band SAR data","volume":"51","author":"Sarker","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, C., Wang, G., Zhu, J., Tang, Y., Shen, P., and Zhu, Z. (2018). A crop classification method integrating GF-3 PolSAR and Sentinel-2A optical data in the Dongting Lake Basin. Sensors, 18.","DOI":"10.3390\/s18093139"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2014.01.024","article-title":"Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests","volume":"145","author":"Tanase","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7978","DOI":"10.1080\/2150704X.2014.978952","article-title":"Polarimetric SAR image classification by boosted multiple-kernel extreme learning machines with polarimetric and spatial features","volume":"35","author":"Du","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s12524-015-0525-6","article-title":"Comparison of various polarimetric decomposition techniques for crop classification","volume":"44","author":"Srikanth","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2019.01.021","article-title":"Integrating UAV optical imagery and LiDAR data for assessing the spatial relationship between mangrove and inundation across a subtropical estuarine wetland","volume":"149","author":"Zhu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Wang","year":"2016","journal-title":"Crop J."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jfoodeng.2014.01.007","article-title":"Modelling the relationship between peel colour and the quality of fresh mango fruit using Random Forests","volume":"131","author":"Fukuda","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ecolind.2016.10.001","article-title":"Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation","volume":"73","author":"Luo","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"313","DOI":"10.3934\/agrfood.2018.3.313","article-title":"Estimating tree height and biomass of a poplar plantation with image-based UAV technology","volume":"3","author":"Pena","year":"2018","journal-title":"AIMS Agric. Food"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.rse.2014.04.019","article-title":"Exploring the effects of biophysical parameters on the spatial pattern of rare cold damage to mangrove forests","volume":"150","author":"Liu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1109\/JSTARS.2020.2989500","article-title":"Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models","volume":"13","author":"Zhu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2039\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:42:38Z","timestamp":1760175758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,25]]},"references-count":85,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12122039"],"URL":"https:\/\/doi.org\/10.3390\/rs12122039","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,25]]}}}