{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T06:54:21Z","timestamp":1725778461880},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program","award":["2019YFC1520800"]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41930110 and 4197139"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Timely and accurate large-scale water body mapping and dynamic monitoring are of great significance for water resource planning, flood control, and disaster reduction applications. Synthetic aperture radar (SAR) systems have the characteristics of strong operability, wide coverage, and all-weather data availability, and play a key role in large-scale water monitoring applications. However, there are still some challenges in the application of highly efficient, high-precision water extraction and dynamic monitoring methods. In this paper, a framework for the automatic extraction and long-term change monitoring of water bodies is proposed. First, a multitemporal water sample dataset is produced based on the bimodal threshold segmentation method. Second, attention block and pyramid module are introduced into the UNet (encoder-decoder) model to construct a robust water extraction network (PA-UNet). Then, GIS modeling is used for the automatic postprocessing of the water extraction results. Finally, the results are mapped and statistically analyzed. The whole process realizes end-to-end input and output. Sentinel-1 data covering Dongting Lake and Poyang Lake are selected for water extraction and dynamic monitoring analysis from 2017 to 2020, and Sentinel-2 images from a similar time frame are selected for verification. The results show that the proposed framework can realize high-precision (the extraction accuracy is higher than 95%), highly efficient automatic water extraction. Multitemporal monitoring results show that Dongting Lake and Poyang Lake fluctuate most in April, July, and November in 2017, 2019, and 2020, and the change trends of the two lakes are the same.<\/jats:p>","DOI":"10.3390\/rs13050865","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T09:36:24Z","timestamp":1614332184000},"page":"865","source":"Crossref","is-referenced-by-count":31,"title":["Multitemporal Water Extraction of Dongting Lake and Poyang Lake Based on an Automatic Water Extraction and Dynamic Monitoring Framework"],"prefix":"10.3390","volume":"13","author":[{"given":"Juanjuan","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4887-923X","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2508-6800","authenticated-orcid":false,"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9280-8378","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"ref_1","first-page":"55","article-title":"Rapid Extraction of Water Area in Poyang Lake Based on Sentinel-1 Satellite Images","volume":"10","author":"Huang","year":"2019","journal-title":"Meteorol. Environ. Res."},{"key":"ref_2","first-page":"104","article-title":"A MODIS-based automated flood monitoring system for southeast asia","volume":"61","author":"Ahamed","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chang, H., Yun, C., Shiqiang, Z., Linyi, L., Kaifang, S., and Rui, L. (2016). Surface Water Mapping from Suomi NPP-VIIRS Imagery at 30 m Resolution via Blending with Landsat Data. Remote Sens., 8.","DOI":"10.3390\/rs8080631"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1080\/2150704X.2014.960606","article-title":"Analysis of Landsat-8 OLI imagery for land surface water mapping","volume":"5","author":"Du","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1080\/01431161.2017.1285503","article-title":"Flood mapping in the lower Mekong River Basin using daily MODIS observations","volume":"38","author":"Fayne","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"794","DOI":"10.3390\/w7020794","article-title":"Target Detection Method for Water Mapping Using Landsat 8 OLI\/TIRS Imagery","volume":"7","author":"Ji","year":"2015","journal-title":"Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.rse.2017.09.032","article-title":"Automatic near real-time flood detection using Suomi-NPP\/VIIRS data","volume":"204","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kobayashi, T., Satake, M., Masuko, H., Manabe, T., and Shimada, M. (1998, January 26\u201328). CRL\/NASDA airborne dual-frequency polarimetric interferometric SAR system. Proceedings of the SPIE\u2014The International Society for Optical Engineering, San Jose, CA, USA.","DOI":"10.1117\/12.331350"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1080\/01431161.2015.1009653","article-title":"An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery","volume":"36","author":"Li","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","first-page":"325","article-title":"Sensitivity Study of ALOS-2 Data to Floodwaters in Joso City in 2015 and its Application","volume":"38","author":"ARII","year":"2018","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1109\/JSTARS.2014.2328012","article-title":"Flood Damage Assessment Through Multitemporal COSMO-SkyMed Data and Hydrodynamic Models: The Albania 2010 Case Study","volume":"7","author":"Pulvirenti","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"687","DOI":"10.3390\/rs5020687","article-title":"Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses","volume":"5","author":"Kuenzer","year":"2013","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Katherine, I., Alexander, B., Georgia, F., Achim, R., and Birgit, W. (2018). Assessing Single-Polarization and Dual-Polarization TerraSAR-X Data for Surface Water Monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10060949"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, S., Qin, J., Ren, J., Zhao, H., and Hong, H. (2020). Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas. Remote Sens., 12.","DOI":"10.3390\/rs12020243"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Y., Niu, Z., Xu, Z., and Yan, X. (2020). Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE. Remote Sens., 12.","DOI":"10.3390\/rs12152413"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07038992.2018.1478723","article-title":"Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification","volume":"44","author":"Possa","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tang, D., Wang, F., Xiang, Y., You, H., and Kang, W. (2018, January 22\u201327). Automatic Water Detection Method in Flooding Area for GF-3 Single-Polarization Data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517886"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bioresita, F., Puissant, A., Stumpf, A., and Malet, J.P. (2018). A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020217"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1111\/jfr3.12303","article-title":"Multi-temporal synthetic aperture radar flood mapping using change detection","volume":"11","author":"Clement","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_21","unstructured":"Lv, W., Yu, Q., and Yu, W. (2010, January 24\u201328). Water extraction in SAR images using GLCM and Support vector Machine. Proceedings of the IEEE International Conference on Signal Processing, Beijing, China."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Quang, N., Tuan, V., Le, H., Anh, N., The, D., Dieu, D., Nguyen, M.H., and Hackney, C. (2019). Hydrological\/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin. Water, 12.","DOI":"10.3390\/w12010071"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tsyganskaya, V., Martinis, S., and Marzahn, P. (2019). Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features. Water, 11.","DOI":"10.3390\/w11091938"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, S., Kan, P., Silbernagel, J., and Jin, J. (2020). Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070424"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5440","DOI":"10.3390\/rs70505440","article-title":"Mapping Regional Inundation with Spaceborne L-Band SAR","volume":"7","author":"Chapman","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TGRS.2010.2052816","article-title":"Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs","volume":"49","author":"Martinis","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1415::AID-HYP532>3.0.CO;2-2","article-title":"Assessment of the Mapping Capabilities of ERS-1 SAR Data for Flood Mapping: A Case Study in Germany","volume":"11","author":"Oberstadler","year":"1997","journal-title":"Hydrol. Proc."},{"key":"ref_28","unstructured":"De Roo, A., Van Der Knijff, J., Horritt, M., Schmuck, G., and De Jong, S. (1999, January 16\u201320). Assessing flood damages of the 1997 Oder flood and the 1995 Meuse flood. Proceedings of the Second International ITC Symposium on Operationalization of Remote Sensing, Enschede, The Netherlands."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/JSTARS.2010.2089042","article-title":"Using ALOS\/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal","volume":"3","author":"Evans","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","first-page":"287","article-title":"Geographic object based image analysis using very high spatial and temporal resolution radar and optical imagery in tracking water level fluctuations in a freshwater reservoir","volume":"3","author":"Simon","year":"2014","journal-title":"South-East. Eur. J. Earth Obs. Geomat."},{"key":"ref_31","unstructured":"Schumann, G., Henry, J., Hoffmann, L., Pfister, L., Pappenberger, F., and Matgen, P. (2005, January 6\u20139). Demonstrating the high potential of remote sensing in hydraulic modelling and flood risk management. Proceedings of the Annual Conference of the Remote Sensing and Photogrammetry Society with the NERC Earth Observation Conference, Portsmouth, UK."},{"key":"ref_32","first-page":"247","article-title":"Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management","volume":"9","author":"Matgen","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/01431161.2015.1060647","article-title":"Comparing four operational sar-based water and flood detection approaches","volume":"36","author":"Martinis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","first-page":"150","article-title":"Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering","volume":"28","author":"Mason","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TGRS.2015.2482001","article-title":"Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence","volume":"54","author":"Pulvirenti","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R., and Chokmani, K. (2018). Flood Mapping in a Complex Environment Using Bistatic TanDEM-X\/TerraSAR-X InSAR Coherence. Remote Sens., 10.","DOI":"10.3390\/rs10121873"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., and Matgen, P. (2019). Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case. Remote Sens., 11.","DOI":"10.3390\/rs11020107"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.1109\/JSTARS.2017.2711960","article-title":"Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields","volume":"10","author":"Pierdicca","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.rse.2019.111582","article-title":"Flood mapping under vegetation using single SAR acquisitions","volume":"237","author":"Grimaldi","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, P., Chen, L., Li, Z., Xing, J., Xing, X., and Yuan, Z. (2019). Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network. Sensors, 19.","DOI":"10.3390\/s19163576"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, L., Cui, X., Li, Z., Zhihui, Y., Xing, J., Xing, X., and Jia, Z. (2019). A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration. Sensors, 19.","DOI":"10.3390\/s19112479"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., and Feng, M. (2019). Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series\u2014A Case Study in Zhanjiang, China. Remote Sens., 11.","DOI":"10.3390\/rs11222673"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/22797254.2019.1694447","article-title":"A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network","volume":"52","author":"Zhang","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_45","first-page":"1","article-title":"Change Detection in SAR Images Based on Deep Learning","volume":"21","author":"Magdy","year":"2019","journal-title":"Int. J. Aeronaut. Space Sci."},{"key":"ref_46","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_47","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_48","doi-asserted-by":"crossref","unstructured":"Nemni, E., Bullock, J., Belabbes, S., and Bromley, L. (2020). Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162532"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention, Springer. MICCAI 2015. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_50","first-page":"453","article-title":"XNet: A convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets","volume":"Volume 10953","author":"Gimi","year":"2019","journal-title":"Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, P., Xing, J., Li, Z., Xing, X., and Zhihui, Y. (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_52","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cao, H., Zhang, H., Wang, C., and Zhang, b. (2019). Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas. Water, 11.","DOI":"10.3390\/w11040786"},{"key":"ref_55","unstructured":"(2017). User Manual of Gaofen-3 Satellite Products, China Centre for Resources Satellite Data and Application."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Hanqiu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1177\/0013164405285548","article-title":"Including Omission Mistakes in the Calculation of Cohen\u2019s Kappa and an Analysis of the Coefficient\u2019s Paradox Features","volume":"66","author":"Simon","year":"2006","journal-title":"Educ. Psychol. Meas."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mizuochi, H., Iijima, Y., Nagano, H., Kotani, A., and Hiyama, T. (2021). Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks. Remote Sens., 13.","DOI":"10.3390\/rs13020175"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/865\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T19:55:57Z","timestamp":1720468557000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,25]]},"references-count":59,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050865"],"URL":"https:\/\/doi.org\/10.3390\/rs13050865","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,25]]}}}