{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T03:09:40Z","timestamp":1773976180601,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Projects of High Resolution Earth Observation Systems of National Science and Technology","award":["05-Y30B01-9001-19\/20-1"],"award-info":[{"award-number":["05-Y30B01-9001-19\/20-1"]}]},{"name":"Major Projects of High Resolution Earth Observation Systems of National Science and Technology","award":["30-Y20A07-9003-17\/18"],"award-info":[{"award-number":["30-Y20A07-9003-17\/18"]}]},{"name":"Key Research and Development Project about Science and Technology Cooperation of Hainan Province","award":["ZDYF2018231"],"award-info":[{"award-number":["ZDYF2018231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.<\/jats:p>","DOI":"10.3390\/s21134328","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T23:22:14Z","timestamp":1624576934000},"page":"4328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data"],"prefix":"10.3390","volume":"21","author":[{"given":"Yupeng","family":"Kang","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"given":"Qingyan","family":"Meng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Miao","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Youfeng","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"given":"Xuemiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"ref_1","first-page":"277","article-title":"Advances of research and application in remote sensing for agriculture","volume":"45","author":"Zhao","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"748","article-title":"Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing","volume":"20","author":"Chen","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.3390\/rs2092305","article-title":"Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution","volume":"2","author":"Thenkabail","year":"2010","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7610","DOI":"10.3390\/rs6087610","article-title":"The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification: A case study for Bole and Manas Counties in Xinjiang, China","volume":"6","author":"Hao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_7","first-page":"255","article-title":"Comparison between GF-1 and Landsat-8 images in land cover classification","volume":"35","author":"Song","year":"2016","journal-title":"Prog. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"245","article-title":"Object oriented land use classification of Dongjiang River Basin based on GF-1 image","volume":"34","author":"Li","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_10","first-page":"140","article-title":"Impact of red edge waveband of RapidEye satellite on estimation accuracy of crop planting area","volume":"32","author":"Liu","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eja.2012.12.001","article-title":"A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems","volume":"46","author":"Delegido","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_12","first-page":"145","article-title":"Extracting oilseed rape growing regions based on variation characteristics of red edge position","volume":"29","author":"She","year":"2013","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s11119-016-9433-1","article-title":"Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields","volume":"17","author":"Kanke","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_14","first-page":"1168","article-title":"Red Edge Characteristics and SPAD Estimation Model Using Hyperspectral Data for Rice in Ningxia Irrigation Zone","volume":"41","author":"Qin","year":"2016","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_15","first-page":"1055","article-title":"Assessments of Sentinel 2 vegetation red-edge spectral bands for improving land cover classification","volume":"42","author":"Qiu","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5583","DOI":"10.1080\/01431161.2012.666812","article-title":"Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data","volume":"33","author":"Schuster","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"41","article-title":"Greening tree species spectrum characteristics analysis in Huhhot based on worldview-\u2161","volume":"35","author":"Liu","year":"2014","journal-title":"J. Inn. Mong. Agric. Univ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_20","first-page":"107","article-title":"Potential Application of GF-6 WFV Data in Forest Types Monitoring","volume":"40","author":"Liu","year":"2019","journal-title":"Spacecr. Recovery Remote Sens."},{"key":"ref_21","first-page":"7046","article-title":"Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data","volume":"35","author":"Kim","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"195","article-title":"Crop Type Classification Using Vegetation Indices of RapidEye Imagery","volume":"40","author":"Ustuner","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","first-page":"3169","article-title":"Study of typical arid crops classification based on machine learning","volume":"38","author":"Hang","year":"2018","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_24","first-page":"194","article-title":"Fine Classification of County Crops Based on Multi-temporal Images of Sentinel-2A","volume":"50","author":"Wu","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1080\/15481603.2017.1351149","article-title":"Assessing the suitability of data from Sentinel-1A and 2A for crop classification","volume":"54","author":"Sonobe","year":"2017","journal-title":"GIScience Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105164","DOI":"10.1016\/j.compag.2019.105164","article-title":"Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning","volume":"169","author":"Maponya","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","first-page":"199","article-title":"Remote sensing estimation of crop planting area based on HJ time-series images","volume":"31","author":"Liu","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e5431","DOI":"10.7717\/peerj.5431","article-title":"Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data","volume":"6","author":"Hao","year":"2018","journal-title":"PeerJ"},{"key":"ref_31","first-page":"283","article-title":"Mode of rotation\/fallow management in typical areas of China and its development strategy","volume":"55","author":"Huang","year":"2018","journal-title":"Acta Pedol. Sin."},{"key":"ref_32","first-page":"2012","article-title":"Influence factors and ecological compensation standard of winter wheat-fallow in the ground water funnel area","volume":"32","author":"Xie","year":"2017","journal-title":"J. Nat. Resour."},{"key":"ref_33","first-page":"171","article-title":"On-orbit geometric calibration and accuracy verification of GF-6 WFV camera","volume":"49","author":"Wang","year":"2020","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_34","first-page":"1619","article-title":"Tree species classification based on the new bands of GF-6 remote sensing satellite","volume":"21","author":"Zhang","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_35","first-page":"218","article-title":"A new method of hyperspectral remote sensing image dimensional reduction","volume":"10","author":"Liu","year":"2005","journal-title":"J. Image Graph."},{"key":"ref_36","first-page":"221","article-title":"Hyperspectral adaptive band selection method through nonlinear transform and information adjacency correlation","volume":"46","author":"Zhang","year":"2017","journal-title":"Infrared Laser Eng."},{"key":"ref_37","first-page":"66","article-title":"Hyperspectral band reduction by combining clustering with adaptive band selection","volume":"33","author":"Zhang","year":"2018","journal-title":"Remote Sens. Inf."},{"key":"ref_38","first-page":"358","article-title":"Determination on the optimum band combination of HJ-1A hyperspectral data in the case region of Dongguan based on optimum index factor and J\u2013M distance","volume":"25","author":"Ma","year":"2010","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1109\/36.477187","article-title":"An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection","volume":"33","author":"Bruzzone","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e4834","DOI":"10.7717\/peerj.4834","article-title":"Estimation of different data compositions for early-season crop type classification","volume":"6","author":"Hao","year":"2018","journal-title":"PeerJ"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1109\/TGRS.2014.2363477","article-title":"Model-based fusion of multi-and hyperspectral images using PCA and wavelets","volume":"53","author":"Palsson","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","unstructured":"Zhao, Y.S. (2013). Principles and Methods of Remote Sensing Application Analysis, Science Press. [2nd ed.]."},{"key":"ref_44","first-page":"313","article-title":"Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images","volume":"23","author":"Zhang","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_45","first-page":"1382","article-title":"A comparative study of different red edge indices for remote sensing detection of urban grassland health status","volume":"19","author":"Fang","year":"2017","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_46","unstructured":"Xie, Q.Y. (2017). Research on Leaf Area Index Retrieve Methods Based on The Red Edge Bands from Multi-Platform Remote Sensing Data. [Ph.D. Thesis, University of Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_48","unstructured":"Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., and Moran, M.S. (2000, January 27\u201330). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"L11402","DOI":"10.1029\/2006GL026457","article-title":"Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"MTCI: The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_55","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1080\/01621459.1979.10481030","article-title":"Comparison of Stopping Rules in Forward Stepwise Discriminant Analysis","volume":"74","author":"Costanza","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1080\/10106049.2017.1333533","article-title":"Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier","volume":"33","author":"Zhang","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_58","first-page":"519","article-title":"Identification of main crops based on the univariate feature selection in Subei","volume":"21","author":"Wang","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box--A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s41976-019-00023-9","article-title":"The performance of random forest classification based on phenological metrics derived from Sentinel-2 and Landsat 8 to map crop cover in an irrigated semi-arid region","volume":"2","author":"Htitiou","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_61","first-page":"992","article-title":"Land-cover Classification of Random Forest based on Sentinel-2A Image Feature Optimization","volume":"41","author":"He","year":"2019","journal-title":"Resour. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Vasilakos, C., Kavroudakis, D., and Georganta, A. (2020). Machine learning classification ensemble of multitemporal Sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sens., 12.","DOI":"10.3390\/rs12122005"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s11769-017-0894-6","article-title":"Effects of RapidEye imagery\u2019s red-edge band and vegetation indices on land cover classification in an arid region","volume":"27","author":"Li","year":"2017","journal-title":"Chin. Geogr. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"16091","DOI":"10.3390\/rs71215820","article-title":"Object-based crop classification with Landsat-MODIS enhanced time-series data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/JSTARS.2017.2774807","article-title":"Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1080\/00131911.2017.1330503","article-title":"An introduction to secondary data analysis with IBM SPSS statistics","volume":"70","author":"Homer","year":"2018","journal-title":"Educ. Rev."},{"key":"ref_70","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_71","unstructured":"Raschka, S. (2015). Python Machine Learning, Packt Publishing."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:09Z","timestamp":1760163789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":71,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134328"],"URL":"https:\/\/doi.org\/10.3390\/s21134328","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,24]]}}}