{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:43:35Z","timestamp":1770975815917,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19K06313"],"award-info":[{"award-number":["19K06313"]}],"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>Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH\/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.<\/jats:p>","DOI":"10.3390\/rs11101148","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"1148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8330-3730","authenticated-orcid":false,"given":"Rei","family":"Sonobe","sequence":"first","affiliation":[{"name":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"ref_1","unstructured":"(2019, April 01). Ministry of Agriculture, Forestry and Fisheries, Available online: http:\/\/www8.cao.go.jp\/space\/comittee\/dai36\/siryou3-5.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Demarez, V., Helen, F., Marais-Sicre, C., and Baup, F. (2019). In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11020118"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sonobe, R., Yamaya, Y., Tani, H., Wang, X.F., Kobayashi, N., and Mochizuki, K.I. (2018). Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover. Geocarto Int., 1\u201317.","DOI":"10.1080\/10106049.2018.1425739"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1080\/01431161.2017.1323286","article-title":"Mapping crop cover using multi-temporal Landsat 8 OLI imagery","volume":"38","author":"Sonobe","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J.H., Zhu, W.Q., Atzberger, C., Zhao, A.Z., Pan, Y.Z., and Huang, X. (2018). A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10081203"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/JSTARS.2018.2855564","article-title":"Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data","volume":"12","author":"Pan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5541","DOI":"10.1109\/JSTARS.2017.2750325","article-title":"Empirical Modeling of Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations with Sentinel-2 MSI Data: Effects of Spectral Settings, Spatial Resolution, and Crop Canopy Cover","volume":"10","author":"Chemura","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"026019","DOI":"10.1117\/1.JRS.12.026019","article-title":"Crop classification from Sentinel-2-derived vegetation indices using ensemble learning","volume":"12","author":"Sonobe","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.compag.2018.12.031","article-title":"Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2","volume":"157","author":"Ottosen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2018.10.012","article-title":"Estimating canola phenology using synthetic aperture radar","volume":"219","author":"McNairn","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6069","DOI":"10.1080\/01431160902980316","article-title":"Application of the Sahebi model using ALOS\/PALSAR and 66.3 cm long surface profile data","volume":"30","author":"Sonobe","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2018.04.013","article-title":"Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil","volume":"211","author":"Amazirh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"859","DOI":"10.3390\/rs3050859","article-title":"Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution Spotlight TerraSAR-X data","volume":"3","author":"Bargiel","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.pce.2014.11.001","article-title":"Discrimination of crop types with TerraSAR-X-derived information","volume":"83\u201384","author":"Sonobe","year":"2015","journal-title":"Phys. Chem. Earth"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, Y., and Wang, J. (2019). Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11040449"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Zhang, B., and Liu, M. (2019). Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11010053"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Yoo, C., Han, H., and Rhee, J. (2018). Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Stendardi, L., Karlsen, S.R., Niedrist, G., Gerdol, R., Zebisch, M., Rossi, M., and Notarnicola, C. (2019). Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions. Remote Sens., 11.","DOI":"10.3390\/rs11050542"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Morton, R.D., and Rowland, C.S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Santos, C., Lamparelli, R.A.C., Figueiredo, G., Dupuy, S., Boury, J., Luciano, A.C.D., Torres, R.D., and Maire, G. (2019). Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region. Remote Sens., 11.","DOI":"10.3390\/rs11030334"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_23","unstructured":"Ager, T.P., and Bresnahan, P.C. (2009, January 27\u201331). Geometric precision in space radar imaging: Results from TerraSAR-X. Proceedings of the ASPRS Annual Conference, Baltimore, ML, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.asr.2006.02.032","article-title":"Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis","volume":"39","author":"Inoue","year":"2007","journal-title":"Adv. Space Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2003.08.008","article-title":"Retrieving crop parameters based on tandem ERS 1\/2 interferometric coherence images","volume":"88","author":"Blaes","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/TGRS.2007.895883","article-title":"Hybrid-polarity SAR architecture","volume":"45","author":"Raney","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Raney, R.K., Cahill, J.T.S., Patterson, G.W., and Bussey, D.B.J. (2012, January 22\u201327). The m-chi decomposition of hybrid dual-polarimetric radar data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS\u201912), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352465"},{"key":"ref_28","unstructured":"Cloude, S.R. (2007, January 22\u201326). The dual polarization entropy\/alpha decomposition: A PALSAR case study. Proceedings of the 3rd International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, Frascati, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TGRS.2010.2046331","article-title":"A General Characterization for Polarimetric Scattering From Vegetation Canopies","volume":"48","author":"Arii","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/LGRS.2011.2174772","article-title":"Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean","volume":"9","author":"Kim","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1080\/01431161.2013.793861","article-title":"Contemporary and historical classification of crop types in Arizona","volume":"34","author":"Hartfield","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","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":"GISci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"Test"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B-Cybern."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.biosystemseng.2018.09.018","article-title":"Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments","volume":"175","author":"Sonobe","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/07038992.2018.1461555","article-title":"Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance","volume":"44","author":"Sonobe","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for ai","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_41","first-page":"2211","article-title":"Multiple Kernel Learning Algorithms","volume":"12","author":"Gonen","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.patcog.2012.09.002","article-title":"Localized algorithms for multiple kernel learning","volume":"46","author":"Gonen","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2013.09.072","article-title":"Multiple kernel extreme learning machine","volume":"149","author":"Liu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pottier, E., Ferro-Famil, L., Allain, S., Cloude, S.R., Hajnsek, I., Papathanassiou, K., Moreira, A., Williams, M., Minchella, A., and Lavalle, M. (2009, January 12\u201317). Overview of the PolSARpro v4.0: The open source toolbox for polarimetric and interferometric polarimetric SAR data processing. Proceedings of the IGARSS, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417532"},{"key":"ref_45","unstructured":"Gens, R., and Logan, T. (2003). Alaska Satellite Facility Software Tools: Manual, Geophysical Institute, University of Alaska."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Buckreuss, S., Werninghaus, R., and Pitz, W. (2008, January 26\u201330). The German satellite mission TerraSAR-X. Proceedings of the IEEE Radar Conference (RadarCon), Rome, Italy.","DOI":"10.1109\/RADAR.2008.4720788"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1117\/1.JRS.10.026020","article-title":"Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops","volume":"10","author":"Betbeder","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2018.09.025","article-title":"Large area cropland extent mapping with Landsat data and a generalized classifier","volume":"219","author":"Phalke","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"Roever, C., Raabe, N., Luebke, K., Ligges, U., Szepannek, G., and Zentgraf, M. (2018, December 12). Classification and Visualization. Available online: https:\/\/cran.r-project.org\/web\/packages\/klaR\/klaR.pdf."},{"key":"ref_50","unstructured":"Raschka, S. (2019, April 01). Linear Discriminant Analysis Bit by Bit. Available online: https:\/\/sebastianraschka.com\/Articles\/2014_python_lda.html."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_52","unstructured":"R core team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_53","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","article-title":"Introduction to multi-layer feed-forward neural networks","volume":"39","author":"Svozil","year":"1997","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2013.06.003","article-title":"Balancing misclassification errors of land cover classification maps using support vector machines and Landsat imagery in the Maipo river basin (Central Chile, 1975\u20132010)","volume":"137","author":"Puertas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_58","unstructured":"Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M.M.A., and Adams, R.P. (2015, January 6\u201311). Scalable Bayesian optimization using deep neural networks. Proceedings of the 32nd International Conference on Machine Learning (ICML), Paris, France."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1109\/LGRS.2015.2476365","article-title":"Remote Sensing Image Classification Exploiting Multiple Kernel Learning","volume":"12","author":"Cusano","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zamani, F., and Jamzad, M. (2017). A feature fusion based localized multiple kernel learning system for real world image classification. EURASIP J. Image Video Process., 78.","DOI":"10.1186\/s13640-017-0225-y"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2747\/1548-1603.47.4.480","article-title":"Comparison of field-observed and simulated map output from a dynamic floodplain vegetation model using remote sensing and GIS techniques","volume":"47","author":"Benjankar","year":"2010","journal-title":"GISci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2012.10.039","article-title":"Using sensitivity analysis and visualization techniques to open black box data mining models","volume":"225","author":"Cortez","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1080\/2150704X.2013.842285","article-title":"Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin","volume":"4","author":"Fontanelli","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_67","first-page":"128","article-title":"An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data","volume":"32","author":"Sonobe","year":"2017","journal-title":"Geocarto Int."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:48Z","timestamp":1760187108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,14]]},"references-count":67,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101148"],"URL":"https:\/\/doi.org\/10.3390\/rs11101148","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,14]]}}}