{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:53:17Z","timestamp":1774619597766,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T00:00:00Z","timestamp":1663632000000},"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>Nearshore sandbars characterize many sandy coasts, and unravelling their dynamics is crucial to understanding nearshore sediment pathways. Sandbar morphologies exhibit complex patterns that can be classified into distinct states. The tremendous progress in data-driven learning in image recognition has recently led to the first automated classification of single-barred beach states from Argus imagery using a Convolutional Neural Network (CNN). Herein, we extend this method for the classification of beach states in a double-barred system. We used transfer learning to fine-tune the pre-trained network of ResNet50. Our data consisted of labelled single-bar time-averaged images from the beaches of Narrabeen (Australia) and Duck (US), complemented by 9+ years of daily averaged low-tide images of the double-barred beach of the Gold Coast (Australia). We assessed seven different CNNs, of which each model was tested on the test data from the location where its training data came from, the self-tests, and on the test data of alternate, unseen locations, the transfer-tests. When the model trained on the single-barred data of both Duck and Narrabeen was tested on unseen data of the double-barred Gold Coast, we achieved relatively low performances as measured by F1 scores. In contrast, models trained with only the double-barred beach data showed comparable skill in the self-tests with that of the single-barred models. We incrementally added data with labels from the inner or outer bar of the Gold Coast to the training data from both single-barred beaches, and trained models with both single- and double-barred data. The tests with these models showed that which bar the labels used for training the model mattered. The training with the outer bar labels led to overall higher performances, except at the inner bar. Furthermore, only 10% of additional data with the outer bar labels was needed for reasonable transferability, compared to the 20% of additional data needed with the inner bar labels. Additionally, when trained with data from multiple locations, more data from a new location did not always positively affect the model\u2019s performance on other locations. However, the larger diversity of images coming from more locations allowed the transferability of the model to the locations from where new training data were added.<\/jats:p>","DOI":"10.3390\/rs14194686","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:08:09Z","timestamp":1663718889000},"page":"4686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Image-Based Classification of Double-Barred Beach States Using a Convolutional Neural Network and Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Stan C. M.","family":"Oerlemans","sequence":"first","affiliation":[{"name":"Department of Physical Geography, Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2665-0947","authenticated-orcid":false,"given":"Wiebe","family":"Nijland","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands"}]},{"given":"Ashley N.","family":"Ellenson","sequence":"additional","affiliation":[{"name":"College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3664-4417","authenticated-orcid":false,"given":"Timothy D.","family":"Price","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15755","DOI":"10.1029\/1999JC900112","article-title":"A simple model for interannual sandbar behavior","volume":"104","author":"Plant","year":"1999","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Walstra, D.J.R., Wesselman, D.A., Van der Deijl, E.C., and Ruessink, G. (2016). On the intersite variability in inter-annual nearshore sandbar cycles. J. Mar. Sci. Eng., 4.","DOI":"10.3390\/jmse4010015"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.margeo.2004.04.017","article-title":"Quantification of nearshore morphology based on video imaging","volume":"208","author":"Alexander","year":"2004","journal-title":"Mar. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"C01006","DOI":"10.1029\/2005JC002965","article-title":"Rip spacing and persistence on an embayed beach","volume":"111","author":"Holman","year":"2006","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111555","DOI":"10.1016\/j.rse.2019.111555","article-title":"Nearshore sandbars crest position dynamics analysed based on Earth Observation data","volume":"237","author":"Constantin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.geomorph.2015.03.006","article-title":"Impact of the winter 2013\u20132014 series of severe Western Europe storms on a double-barred sandy coast: Beach and dune erosion and megacusp embayments","volume":"238","author":"Castelle","year":"2015","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.csr.2010.02.001","article-title":"Two-and three-dimensional double-sandbar system behaviour under intense wave forcing and a meso\u2013macro tidal range","volume":"30","author":"Almar","year":"2010","journal-title":"Cont. Shelf Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"C10025","DOI":"10.1029\/2004JC002401","article-title":"Surf zone entrainment, along-shore transport, and human health implications of pollution from tidal outlets","volume":"110","author":"Grant","year":"2005","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2016.09.008","article-title":"Rip current types, circulation and hazard","volume":"163","author":"Castelle","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_10","first-page":"141","article-title":"Single and multi-bar beach change models","volume":"15","author":"Short","year":"1993","journal-title":"J. Coast. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.csr.2010.12.018","article-title":"State dynamics of a double sandbar system","volume":"31","author":"Price","year":"2011","journal-title":"Cont. Shelf Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/0025-3227(84)90008-2","article-title":"Morphodynamic variability of surf zones and beaches: A synthesis","volume":"56","author":"Wright","year":"1984","journal-title":"Mar. Geol."},{"key":"ref_13","first-page":"C06028","article-title":"Observations of nearshore crescentic sandbars","volume":"109","author":"Ruessink","year":"2004","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_14","unstructured":"Abessolo Ondoa, G. (2020). Response of Sandy Beaches in West Africa, Gulf of Guinea, to Multi-Scale Forcing. [Ph.D. Thesis, Universit\u00e9 Paul Sabatier\u2014Toulouse III]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.1029\/97JC02765","article-title":"Observations of sand bar evolution on a natural beach","volume":"103","author":"Gallagher","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.coastaleng.2007.01.003","article-title":"The history and technical capabilities of Argus","volume":"54","author":"Holman","year":"2007","journal-title":"Coast. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1146\/annurev-marine-121211-172408","article-title":"Remote sensing of the nearshore","volume":"5","author":"Holman","year":"2013","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_18","unstructured":"Aarninkhof, S., and Ruessink, G. (2002, January 6\u201310). Quantification of surf zone bathymetry from video observations of wave breaking. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.margeo.2017.08.014","article-title":"Morphodynamics of slightly oblique nearshore bars and their relationship with the cycle of net offshore migration","volume":"392","author":"Aleman","year":"2017","journal-title":"Mar. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Palmsten, M.L., and Brodie, K.L. (2022). The Coastal Imaging Research Network (CIRN). Remote Sens., 14.","DOI":"10.3390\/rs14030453"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107812","DOI":"10.1016\/j.ecss.2022.107812","article-title":"Beach morphodynamic classification using high-resolution nearshore bathymetry and process-based wave modelling","volume":"268","author":"Jackson","year":"2022","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106615","DOI":"10.1016\/j.enggeo.2022.106615","article-title":"Automatic classification and mapping of the seabed using airborne LiDAR bathymetry","volume":"301","author":"Janowski","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.csr.2011.03.009","article-title":"Neural-network predictability experiments for nearshore sandbar migration","volume":"31","author":"Pape","year":"2011","journal-title":"Cont. Shelf Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0025-3227(00)00056-6","article-title":"Artificial neural network correction of remotely sensed sandbar location","volume":"169","author":"Kingston","year":"2000","journal-title":"Mar. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.neunet.2007.04.007","article-title":"Recurrent neural network modeling of nearshore sandbar behavior","volume":"20","author":"Pape","year":"2007","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Collins, A.M., Geheran, M.P., Hesser, T.J., Bak, A.S., Brodie, K.L., and Farthing, M.W. (2021). Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC. Remote Sens., 13.","DOI":"10.3390\/rs13234907"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_29","unstructured":"Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., and Wu, Y. (2022). Coca: Contrastive captioners are image-text foundation models. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hoshen, Y., Weiss, R.J., and Wilson, K.W. (2015, January 19\u201324). Speech acoustic modeling from raw multichannel waveforms. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178847"},{"key":"ref_32","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., and Zisserman, A. (2015). Deep face recognition. arXiv.","DOI":"10.5244\/C.29.41"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A decade survey of transfer learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_35","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ellenson, A.N., Simmons, J.A., Wilson, G.W., Hesser, T.J., and Splinter, K.D. (2020). Beach State Recognition Using Argus Imagery and Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12233953"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Birkemeier, W.A., DeWall, A.E., Gorbics, C.S., and Miller, H.C. (1981). A User\u2019s Guide to CERC\u2019s Field Research Facility, Coastal Engineering Research Center. Technical Report.","DOI":"10.5962\/bhl.title.48249"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0025-3227(98)00010-3","article-title":"Storm-driven variability of the beach-nearshore profile at Duck, North Carolina, USA, 1981\u20131991","volume":"148","author":"Lee","year":"1998","journal-title":"Mar. Geol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Stauble, D.K., and Cialone, M.A. (1996, January 2\u20136). Sediment dynamics and profile interactions: Duck94. Proceedings of the Coastal Engineering 1996, Orlando, FL, USA.","DOI":"10.1061\/9780784402429.303"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"160024","DOI":"10.1038\/sdata.2016.24","article-title":"A multi-decade dataset of monthly beach profile surveys and inshore wave forcing at Narrabeen, Australia","volume":"3","author":"Turner","year":"2016","journal-title":"Sci. Data"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Splinter, K.D., Harley, M.D., and Turner, I.L. (2018). Remote sensing is changing our view of the coast: Insights from 40 years of monitoring at Narrabeen-Collaroy, Australia. Remote Sens., 10.","DOI":"10.3390\/rs10111744"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"F04033","DOI":"10.1029\/2011JF001989","article-title":"A reevaluation of coastal embayment rotation: The dominance of cross-shore versus alongshore sediment transport processes, Collaroy-Narrabeen Beach, southeast Australia","volume":"116","author":"Harley","year":"2011","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Harley, M. (2017). Coastal storm definition. In Coastal Storms: Processes and Impacts, Wiley\u2013Blackwell.","DOI":"10.1002\/9781118937099.ch1"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1071\/MF9920765","article-title":"Wave climate of the Sydney region, an energetic and highly variable ocean wave regime","volume":"43","author":"Short","year":"1992","journal-title":"Mar. Freshw. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"312","DOI":"10.2112\/JCR-SI50-061.1","article-title":"Comparison of two wave models for Gold Coast, Australia","volume":"50","author":"Strauss","year":"2007","journal-title":"J. Coast. Res."},{"key":"ref_46","unstructured":"Allen, M., and Callaghan, J. (2000). Extreme Wave Conditions for the South Queensland Coastal Region."},{"key":"ref_47","unstructured":"Jackson, L.A., Tomlinson, R., and Nature, P. (2017, January 21\u201323). 50 years of seawall and nourishment strategy evolution on the gold coast. Proceedings of the Australasian Coasts & Ports conference, Cairns, Australia."},{"key":"ref_48","unstructured":"Jackson, L.A., Tomlinson, R., Turner, I., Corbett, B., d\u2019Agata, M., and McGrath, J. (2005, January 12\u201314). Narrowneck artificial reef; results of 4 yrs monitoring and modifications. Proceedings of the 4th International Surfing Reef Symposium, Manhattan Beach, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.margeo.2006.10.029","article-title":"Observations of rip spacing, persistence and mobility at a long, straight coastline","volume":"236","author":"Turner","year":"2007","journal-title":"Mar. Geol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_53","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A survey on deep transfer learning. Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_60","first-page":"1100612","article-title":"Super-convergence: Very fast training of neural networks using large learning rates","volume":"Volume 11006","author":"Smith","year":"2019","journal-title":"Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1214\/009053605000000255","article-title":"Boosting with early stopping: Convergence and consistency","volume":"33","author":"Zhang","year":"2005","journal-title":"Ann. Stat."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, J., and Yang, G. (2018). Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer. Algorithms, 11.","DOI":"10.3390\/a11030028"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10618-012-0295-5","article-title":"Training and assessing classification rules with imbalanced data","volume":"28","author":"Menardi","year":"2014","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2019, January 15\u201319). Autoaugment: Learning augmentation strategies from data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1002\/2014JC010329","article-title":"Shoreline variability from days to decades: Results of long-term video imaging","volume":"120","author":"Pianca","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_67","unstructured":"Lei, S., Zhang, H., Wang, K., and Su, Z. (2019, January 6\u20139). How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification. Proceedings of the International Conference on Learning Representations (ICLR 2019), New Orleans, LA, USA."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 8\u201314). Acquisition of localization confidence for accurate object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_69","unstructured":"Bashir, F., and Porikli, F. (2006, January 18). Performance evaluation of object detection and tracking systems. Proceedings of the 9th IEEE International Workshop on PETS, New York, NY, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:34:52Z","timestamp":1760142892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,20]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194686"],"URL":"https:\/\/doi.org\/10.3390\/rs14194686","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,20]]}}}