{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:56:54Z","timestamp":1772143014399,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"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>Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel\u20132 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75\u20130.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring.<\/jats:p>","DOI":"10.3390\/rs12030355","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T11:25:59Z","timestamp":1579605959000},"page":"355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-8602","authenticated-orcid":false,"given":"Nam Thang","family":"Ha","sequence":"first","affiliation":[{"name":"Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New Zealand"},{"name":"Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 530000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-0208","authenticated-orcid":false,"given":"Merilyn","family":"Manley-Harris","sequence":"additional","affiliation":[{"name":"Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6422-2847","authenticated-orcid":false,"given":"Tien Dat","family":"Pham","sequence":"additional","affiliation":[{"name":"Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture (VNUA), Trau Quy, Gia Lam, Hanoi 10000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2471-6903","authenticated-orcid":false,"given":"Ian","family":"Hawes","sequence":"additional","affiliation":[{"name":"Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10021-017-0170-8","article-title":"Blue carbon storage in tropical seagrass meadows relates to carbonate stock dynamics, plant\u2013sediment processes, and landscape context: Insights from the Western Indian ocean","volume":"21","author":"Lyimo","year":"2018","journal-title":"Ecosystems"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e0176630","DOI":"10.1371\/journal.pone.0176630","article-title":"Seagrass blue carbon spatial patterns at the meadow-scale","volume":"12","author":"Oreska","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Duarte, C.M., and Krause-Jensen, D. (2017). Export from seagrass meadows contributes to marine carbon sequestration. Front. Mar. Sci., 4.","DOI":"10.3389\/fmars.2017.00013"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12377","DOI":"10.1073\/pnas.0905620106","article-title":"Accelerating loss of seagrasses across the globe threatens coastal ecosystems","volume":"106","author":"Waycott","year":"2009","journal-title":"Proc. Natl. Acad. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"014002","DOI":"10.1088\/1748-9326\/6\/1\/014002","article-title":"Monitoring, reporting and verification for national REDD + programmes: Two proposals","volume":"6","author":"Herold","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Xia, J., Ha, N.T., Bui, D.T., Le, N.N., and Tekeuchi, W. (2019). A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, seagrassesand salt marshes during 2010\u20132018. Sensors, 19.","DOI":"10.3390\/s19081933"},{"key":"ref_7","unstructured":"(2015). ESA Sentinel\u20142 User Handbook, ESA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/01431161.2014.990649","article-title":"The application of remote sensing to seagrass ecosystems: An overview and future research prospects","volume":"36","author":"Hossain","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1002\/aqc.2688","article-title":"A low cost field-survey method for mapping seagrasses and their potential threats: An example from the northern Gulf of Aqaba, Red Sea: Mapping seagrasses and their potential threats in the Gulf of Aqaba","volume":"27","author":"Winters","year":"2017","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/22797254.2018.1544838","article-title":"A review of seagrass detection, mapping and monitoring applications using acoustic systems","volume":"52","author":"Gumusay","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5739","DOI":"10.1080\/01431161.2018.1506951","article-title":"Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment","volume":"39","author":"Wicaksono","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Poursanidis, D., Topouzelis, K., and Chrysoulakis, N. (2018). Mapping coastal marine habitats and delineating the deep limits of the Neptune\u2019s seagrass meadows using very high resolution Earth observation data. Int. J. Remote Sens., 1\u201318.","DOI":"10.1080\/01431161.2018.1490974"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jag.2019.03.012","article-title":"On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band","volume":"80","author":"Poursanidis","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"ref_14","unstructured":"Asmala, A. (2012). Analysis of Maximum Likelihood Classification on Multispectral Data. Appl. Math. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Supervised Classification Techniques. Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Holloway, J., and Mengersen, K. (2018). Statistical machine learning methods and remote sensing for sustainable development goals: A review. Remote Sens., 10.","DOI":"10.3390\/rs10091365"},{"key":"ref_17","unstructured":"Liu, Y. (2017). Python Machine Learning by Example: Easy-to-follow Examples that Get You up and Running with Machine Learning, Packt Publishing."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mohamed, H., Nadaoka, K., and Nakamura, T. (2018). Assessment of machine learning algorithms for automatic benthic cover monitoring and mapping using towed underwater video camera and high-resolution satellite images. Remote Sens., 10.","DOI":"10.3390\/rs10050773"},{"key":"ref_19","first-page":"500","article-title":"Towards visual detection, mapping and quantification of Posidonia Oceanica using a lightweight AUV","volume":"49","author":"Campos","year":"2016","journal-title":"IFAC-Pap."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Traganos, D., and Reinartz, P. (2017). Mapping Mediterranean seagrasses with Sentinel-2 imagery. Mar. Pollut. Bull.","DOI":"10.1016\/j.marpolbul.2017.06.075"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"209","DOI":"10.2112\/SI76-018","article-title":"Eelgrass and macroalgal mapping to develop nutrient criteria in New Hampshire\u2019s estuaries using hyperspectral imagery","volume":"76","author":"Morrison","year":"2016","journal-title":"J. Coast. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1080\/2150704X.2017.1354262","article-title":"Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using sentinel-2 and Landsat OLI imagery","volume":"8","author":"Colkesen","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_23","first-page":"1","article-title":"A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping","volume":"33","author":"Sahin","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"012042","DOI":"10.1088\/1742-6596\/439\/1\/012042","article-title":"Hyperspectral image classification using Support Vector Machine","volume":"439","author":"Moughal","year":"2013","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S. (2019). Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 sulawesi earthquake and tsunami in Palu, Indonesia. Remote Sens., 11.","DOI":"10.3390\/rs11070886"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Liu, W., and Yang, W. (2017). An improved rotation forest for multi-feature remote-sensing imagery classification. Remote Sens., 9.","DOI":"10.3390\/rs9111205"},{"key":"ref_28","unstructured":"Bagnall, A., Bostrom, A., Cawley, G., Flynn, M., Large, J., and Lines, J. (2018). Is rotation forest the best classifier for problems with continuous features?. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, W., Sui, H., Tu, J., Huang, W., Xu, C., and Sun, K. (2018). A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses. Remote Sens., 10.","DOI":"10.3390\/rs10071015"},{"key":"ref_31","unstructured":"Rainforth, T., and Wood, F. (2015). Canonical Correlation Forests. arXiv."},{"key":"ref_32","unstructured":"Park, S.G. (1999). Changes in abundance of seagrass (Zostera spp.) in Tauranga Harbour from 1959\u201396, Environment BOP. Environmental Report 99\/30."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Collier, C.J., Villacorta-Rath, C., van Dijk, K., Takahashi, M., and Waycott, M. (2014). Seagrass proliferation precedes mortality during hypo-salinity events: A stress-induced morphometric response. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094014"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Collier, C.J., Ow, Y.X., Langlois, L., Uthicke, S., Johansson, C.L., O\u2019Brien, K.R., Hrebien, V., and Adams, M.P. (2017). Optimum Temperatures for Net Primary Productivity of Three Tropical Seagrass Species. Front. Plant Sci., 8.","DOI":"10.3389\/fpls.2017.01446"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"York, P.H., Gruber, R.K., Hill, R., Ralph, P.J., Booth, D.J., and Macreadie, P.I. (2013). Physiological and Morphological Responses of the Temperate Seagrass Zostera muelleri to Multiple Stressors: Investigating the Interactive Effects of Light and Temperature. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0076377"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.4319\/lo.2011.56.6.2200","article-title":"Thermal tolerance of two seagrass species at contrasting light levels: Implications for future distribution in the Great Barrier Reef","volume":"56","author":"Collier","year":"2011","journal-title":"Limnol. Oceanogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/00288330709509897","article-title":"Growth and productivity of intertidal Zostera capricorni in New Zealand estuaries","volume":"41","author":"Turner","year":"2007","journal-title":"N. Z. J. Mar. Freshw. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s002270050268","article-title":"Reproduction in the seagrass Zostera novazelandica on intertidal platforms in southern New Zealand","volume":"130","author":"Ramage","year":"1998","journal-title":"Mar. Biol."},{"key":"ref_39","unstructured":"Schwarz, A.-M., and Turner, S. (2006). Management and Conservation of Seagrass in New Zealand: An Introduction."},{"key":"ref_40","unstructured":"Reeve, G., Stephens, S., and Wadhwa, A. (2018). Tauranga Harbour Inundation Modelling, NIWA."},{"key":"ref_41","unstructured":"(2020, January 05). Past Weather for Tauranga Airport. Available online: https:\/\/www.metservice.com\/towns-cities\/locations\/tauranga\/past-weather."},{"key":"ref_42","unstructured":"(2020, January 05). Tauranga Sea Temperature. Available online: https:\/\/www.seatemperature.org\/australia-pacific\/new-zealand\/tauranga.htm."},{"key":"ref_43","unstructured":"Park, S. (2011). Extent of Seagrass in the Bay of Plenty in 2011, Bay of Plenty Reginal Council. Environmental publication."},{"key":"ref_44","unstructured":"(2019, October 12). Glovis, Available online: https:\/\/glovis.usgs.gov."},{"key":"ref_45","unstructured":"(2018, October 01). RBINS Acolite Atmospheric Correction Processor. Available online: https:\/\/odnature.naturalsciences.be\/remsem\/software-and-data\/acolite."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2019.03.010","article-title":"Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives","volume":"225","author":"Vanhellemont","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/08920759609362279","article-title":"A review of remote sensing for the assessment and management of tropical coastal resources","volume":"24","author":"Green","year":"1996","journal-title":"Coast. Manag."},{"key":"ref_48","unstructured":"Frouin, R.J., Ebuchi, N., Pan, D., and Saino, T. (2012). Seagrass mapping using ALOS AVNIR-2 data in Lap An Lagoon, Thua Thien Hue, Vietnam, SPIE."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13157","DOI":"10.3390\/rs71013157","article-title":"A method to analyze the potential of optical remote sensing for benthic habitat mapping","volume":"7","author":"Garcia","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","unstructured":"Green, E.P., and Edwards, A.J. (2000). Remote Sensing Handbook for Tropical Coastal Management, Unesco Pub."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, Q., Yu, R., Hao, Y., Wu, L., Zhang, W., Zhang, Q., and Bu, X. (2018). A new method for mapping aquatic vegetation especially underwater vegetation in lake Ulansuhai using GF-1 satellite data. Remote Sens., 10.","DOI":"10.3390\/rs10081279"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1080\/01431160903154341","article-title":"Using bottom surface reflectance to map coastal marine areas: A new application method for Lyzenga\u2019s model","volume":"31","author":"Sagawa","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2006.872909","article-title":"Multispectral bathymetry using a simple physically based algorithm","volume":"44","author":"Lyzenga","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"623","DOI":"10.5721\/EuJRS20134637","article-title":"Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing","volume":"46","author":"Hogland","year":"2013","journal-title":"Eur. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation Forest: A New Classifier Ensemble Method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Koedsin, W., Intararuang, W., Ritchie, R., and Huete, A. (2016). An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sens., 8.","DOI":"10.3390\/rs8040292"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1080\/2150704X.2018.1468101","article-title":"Seagrass habitat mapping: How do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3 perform?","volume":"9","author":"Kovacs","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1007\/s10661-011-2028-4","article-title":"Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA","volume":"184","author":"Meyer","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5843","DOI":"10.1080\/01431161.2016.1249300","article-title":"Damage to seagrass and seaweed beds in Matsushima Bay, Japan, caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011","volume":"37","author":"Tsujimoto","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","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_62","unstructured":"Joshua, L. (2019, February 13). Rotation Forest 2016. Available online: https:\/\/github.com\/joshloyal\/RotationForest."},{"key":"ref_63","unstructured":"Albanese, D., Visintainer, R., Merler, S., Riccadonna, S., Jurman, G., and Furlanello, C. (2012). Mlpy: Machine Learning Python. arXiv."},{"key":"ref_64","unstructured":"Davide, A. (2019, February 15). Non Linear Methods for Classification: Maximum Likelihood Classifier. Available online: http:\/\/mlpy.sourceforge.net\/docs\/3.5\/nonlin_class.html#maximum-likelihood-classifier."},{"key":"ref_65","unstructured":"Rainforth, T. (2019, February 17). Canonical Correlation Forests 2018. Available online: https:\/\/github.com\/twgr\/ccfs."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"638","DOI":"10.21105\/joss.00638","article-title":"MLxtend: Providing machine learning and data science utilities and extensions to Python\u2019s scientific computing stack","volume":"3","author":"Raschka","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Traganos, D., and Reinartz, P. (2018). Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series. Front. Plant Sci., 9.","DOI":"10.3389\/fpls.2018.00096"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Xia, J., Baier, G., Le, N.N., and Yokoya, N. Mangrove Species Mapping Using Sentinel-1 and Sentinel-2 Data in North Vietnam. Proceedings of the IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.","DOI":"10.1109\/IGARSS.2019.8898987"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Lemenkova, P. (2019). Processing oceanographic data by python libraries Numpy, Scipy, and Pandas. Aquat. Res., 73\u201391.","DOI":"10.3153\/AR19009"},{"key":"ref_70","unstructured":"Raschka, S., and Mirjalili, V. (2017). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow, Packt Publishing. [2nd ed.]."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.future.2017.02.044","article-title":"A cloud-based remote sensing data production system","volume":"86","author":"Yan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, G., Xia, J., Ben, J., Cao, Q., Zhao, L., Ma, Y., Zhang, L., and Zhu, D. (2019). Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12010062"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:29:59Z","timestamp":1760362199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,21]]},"references-count":72,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030355"],"URL":"https:\/\/doi.org\/10.3390\/rs12030355","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,21]]}}}