{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T21:20:46Z","timestamp":1775510446141,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities, Research Funds of Renmin University of China","award":["20XNF022"],"award-info":[{"award-number":["20XNF022"]}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17H06108"],"award-info":[{"award-number":["17H06108"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water\/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.<\/jats:p>","DOI":"10.3390\/rs13112220","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-9425","authenticated-orcid":false,"given":"Yanbing","family":"Bai","sequence":"first","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5343-6508","authenticated-orcid":false,"given":"Wenqi","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}]},{"given":"Zhengxin","family":"Yang","sequence":"additional","affiliation":[{"name":"China Huaneng Group Co., Ltd., Beijing 100031, China"}]},{"given":"Jinze","family":"Yu","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan"}]},{"given":"Bo","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, UK"}]},{"given":"Xing","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan"}]},{"given":"Hanfang","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4861-5739","authenticated-orcid":false,"given":"Erick","family":"Mas","sequence":"additional","affiliation":[{"name":"International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-0639","authenticated-orcid":false,"given":"Shunichi","family":"Koshimura","sequence":"additional","affiliation":[{"name":"International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","unstructured":"IFRC (2021, January 18). World Disaster Report 2020. Available online: https:\/\/media.ifrc.org\/ifrc\/world-disaster-report-2020\/."},{"key":"ref_2","unstructured":"IDMC (2021, January 17). Global Report on Internal Displacement. Available online: https:\/\/www.internal-displacement.org\/sites\/default\/files\/publications\/documents\/2019-IDMC-GRID.pdf."},{"key":"ref_3","unstructured":"Aon (2021, January 18). Weather, Climate & Catastrophe Insight 2019 Annual Report. Available online: http:\/\/thoughtleadership.aon.com\/Documents\/20200122-if-natcat2020.pdf?utm_source=ceros&utm_medium=storypage&utm_campaign=natcat20."},{"key":"ref_4","unstructured":"FAO (2021, January 18). The State of Food Security and Nutrition in the World. Available online: http:\/\/www.fao.org\/3\/I9553EN\/i9553en.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E. (2020, January 14\u201319). Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00113"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TGRS.2009.2029236","article-title":"Flood Detection in Urban Areas Using TerraSAR-X","volume":"48","author":"Mason","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.envsci.2018.03.014","article-title":"A global network for operational flood risk reduction","volume":"84","author":"Alfieri","year":"2018","journal-title":"Environ. Sci. Policy"},{"key":"ref_8","unstructured":"Zajic, B. (2019). How flood mapping from space protects the vulnerable and can save lives. Planet Labs, 17."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3389\/fenvs.2019.00127","article-title":"The value of near real-time earth observations for improved flood disaster response","volume":"7","author":"Oddo","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1175\/WCAS-D-17-0111.1","article-title":"Exploiting the convergence of evidence in satellite data for advanced weather index insurance design","volume":"11","author":"Enenkel","year":"2019","journal-title":"Weather. Clim. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Okada, G., Moya, L., Mas, E., and Koshimura, S. (2021). The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework. Remote Sens., 13.","DOI":"10.3390\/rs13071401"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"303","DOI":"10.5194\/nhess-9-303-2009","article-title":"Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data","volume":"9","author":"Martinis","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mahoney, C., Merchant, M., Boychuk, L., Hopkinson, C., and Brisco, B. (2020). Automated SAR Image Thresholds for Water Mask Production in Alberta\u2019s Boreal Region. Remote Sens., 12.","DOI":"10.3390\/rs12142223"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tiwari, V., Kumar, V., Matin, M.A., Thapa, A., Ellenburg, W.L., Gupta, N., and Thapa, S. (2020). Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0237324"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"Threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9026","DOI":"10.1080\/01431161.2019.1624869","article-title":"Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping","volume":"40","author":"Bioresita","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Conde, F.C., and Munoz, M.D. (2019). Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study. Water, 11.","DOI":"10.3390\/w11122454"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, M.M., and Jin, S.G. (2020). Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data. Remote Sens., 12.","DOI":"10.3390\/rs12132073"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rinp.2018.02.054","article-title":"On the merging of optical and SAR satellite imagery for surface water mapping applications","volume":"9","author":"Markert","year":"2018","journal-title":"Results Phys."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Benoudjit, A., and Guida, R. (2019). A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier. Remote Sens., 11.","DOI":"10.3390\/rs11070779"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111664","DOI":"10.1016\/j.rse.2020.111664","article-title":"Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine","volume":"240","author":"DeVries","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"Rudner, T.G., Ru\u00dfwurm, M., Fil, J., Pelich, R., Bischke, B., Kopa\u010dkov\u00e1, V., and Bili\u0144ski, P. (February, January 27). Multi3Net: Segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_23","first-page":"15","article-title":"Flood detection from multi-temporal SAR data using harmonic analysis and change detection","volume":"38","author":"Schlaffer","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-based flood mapping: A fully automated processing chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","first-page":"77","article-title":"Probabilistic mapping of flood-induced backscatter changes in SAR time series","volume":"56","author":"Schlaffer","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1109\/TGRS.2018.2797536","article-title":"Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images","volume":"56","author":"Amitrano","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Moya, L., Endo, Y., Okada, G., Koshimura, S., and Mas, E. (2019). Drawback in the change detection approach: False detection during the 2018 western Japan floods. Remote Sens., 11.","DOI":"10.3390\/rs11192320"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Moya, L., Mas, E., and Koshimura, S. (2020). Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon. Remote Sens., 12.","DOI":"10.3390\/rs12142244"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2017.2772349","article-title":"A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks","volume":"15","author":"Bai","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bai, Y., Mas, E., and Koshimura, S. (2018). Towards operational satellite-based damage-mapping using u-net convolutional network: A case study of 2011 tohoku earthquake-tsunami. Remote Sens., 10.","DOI":"10.3390\/rs10101626"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kang, W., Xiang, Y., Wang, F., Wan, L., and You, H. (2018). Flood detection in gaofen-3 SAR images via fully convolutional networks. Sensors, 18.","DOI":"10.3390\/s18092915"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.isprsjprs.2019.04.014","article-title":"Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence","volume":"152","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, P., Xing, J., Li, Z., Xing, X., and Yuan, Z. (2020). A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas. Remote Sens., 12.","DOI":"10.3390\/rs12193205"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100008","DOI":"10.1016\/j.srs.2020.100008","article-title":"Mapping of glacial lakes using Sentinel-1 and Sentinel-2 data and a random forest classifier: Strengths and challenges","volume":"2","author":"Wangchuk","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"135563","DOI":"10.1016\/j.scitotenv.2019.135563","article-title":"Seasonal cycles of lakes on the Tibetan Plateau detected by Sentinel-1 SAR data","volume":"703","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_36","unstructured":"Sunkara, V., Purri, M., Saux, B.L., and Adams, J. (2020). Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz, D.F., Mu\u00f1oz, P., Moftakhari, H., and Moradkhani, H. (2021). From Local to Regional Compound Flood Mapping with Deep Learning and Data Fusion Techniques. Sci. Total Environ., 146927.","DOI":"10.1016\/j.scitotenv.2021.146927"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bai, Y., Hu, J., Su, J., Liu, X., Liu, H., He, X., Meng, S., Mas, E., and Koshimura, S. (2020). Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets. Remote Sens., 12.","DOI":"10.3390\/rs12244055"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Su, J., Bai, Y., Wang, X., Lu, D., Zhao, B., Yang, H., Mas, E., and Koshimura, S. (2020). Technical Solution Discussion for Key Challenges of Operational Convolutional Neural Network-Based Building-Damage Assessment from Satellite Imagery: Perspective from Benchmark xBD Dataset. Remote Sens., 12.","DOI":"10.3390\/rs12223808"},{"key":"ref_40","unstructured":"(2020). FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Konapala, G., and Kumar, S. (2021, March 04). Exploring Sentinel-1 and Sentinel-2 Diversity for Flood Inundation Mapping Using Deep Learning. Technical Report. Copernicus Meetings. Available online: https:\/\/doi.org\/10.5194\/egusphere-egu21-10445.","DOI":"10.5194\/egusphere-egu21-10445"},{"key":"ref_42","first-page":"123","article-title":"An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data","volume":"73","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 15\u201320). Basnet: Boundary-aware salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_44","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1162\/089976603321192103","article-title":"Permitted and forbidden sets in symmetric threshold-linear networks","volume":"15","author":"Hahnloser","year":"2003","journal-title":"Neural Comput."},{"key":"ref_46","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_47","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Intell. Mach."},{"key":"ref_49","unstructured":"Goyal, P., and Kaiming, H. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_50","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Luo, W., and Urtasun, R. (2017, January 22\u201329). Deeproadmapper: Extracting road topology from aerial images. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.372"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_53","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_55","unstructured":"Banko, G. (1998). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory, International Institute for Applied Systems Analysis."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111732","DOI":"10.1016\/j.rse.2020.111732","article-title":"Hindcast and forecast of daily inundation extents using satellite SAR and altimetry data with rotated empirical orthogonal function analysis: Case study in Tonle Sap Lake Floodplain","volume":"241","author":"Chang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Qian, Q., Chen, L., Li, H., and Jin, R. (2020, January 14\u201319). DR loss: Improving object detection by distributional ranking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01218"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., Wang, J., and Ma, A. (2020, January 14\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00415"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_61","unstructured":"McKay, J., Gag, I., Monga, V., Raj, R.G., and Ieee (2017). What\u2019s Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2220\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:19Z","timestamp":1760163079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":61,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112220"],"URL":"https:\/\/doi.org\/10.3390\/rs13112220","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,5]]}}}