{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:59:21Z","timestamp":1760237961539,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88881.145912\/2017-01"],"award-info":[{"award-number":["88881.145912\/2017-01"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["018\/06918-3, 017\/12646-3, 2016\/26170-8, 2014\/12236-1"],"award-info":[{"award-number":["018\/06918-3, 017\/12646-3, 2016\/26170-8, 2014\/12236-1"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FAPESP-Microsoft Virtual Institute","award":["016\/08085-3, 2015\/02105-0, 2014\/50715-9, 2013\/50169-1, and 2013\/50155-0"],"award-info":[{"award-number":["016\/08085-3, 2015\/02105-0, 2014\/50715-9, 2013\/50169-1, and 2013\/50155-0"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["307560\/2016-3, 132847\/2015-9"],"award-info":[{"award-number":["307560\/2016-3, 132847\/2015-9"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.<\/jats:p>","DOI":"10.3390\/rs12142267","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T10:35:18Z","timestamp":1594809318000},"page":"2267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Soft Computing Approach for Selecting and Combining Spectral Bands"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3997-4422","authenticated-orcid":false,"given":"Juan F. H.","family":"Albarrac\u00edn","sequence":"first","affiliation":[{"name":"Institute of Computing, University of Campinas, Campinas 13000-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6392-2526","authenticated-orcid":false,"given":"Rafael S.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Institute of Biology at University of Campinas, Campinas 13000-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1958-3651","authenticated-orcid":false,"given":"Marina","family":"Hirota","sequence":"additional","affiliation":[{"name":"Institute of Biology at University of Campinas, Campinas 13000-000, Brazil"},{"name":"Department of Physics, Federal University of Santa Catarina, Florian\u00f3polis 88040-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8889-1586","authenticated-orcid":false,"given":"Jefersson A.","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Department of Computer Science at Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9772-263X","authenticated-orcid":false,"given":"Ricardo da S.","family":"Torres","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences at Norwegian University of Science and Technology (NTNU), 6009 \u00c5lesund, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/S0034-4257(02)00151-7","article-title":"Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features","volume":"84","author":"Sims","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2015.05.011","article-title":"Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data","volume":"106","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"318","article-title":"Performance of vegetation indices from Landsat time series in deforestation monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/36.536541","article-title":"Designing optimal spectral indexes for remote sensing applications","volume":"34","author":"Verstraete","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1109\/36.885197","article-title":"Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications","volume":"38","author":"Gobron","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","unstructured":"Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press."},{"key":"ref_11","first-page":"1765","article-title":"A Survey of Genetic Programming and Its Applications","volume":"13","author":"Li","year":"2019","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TSMCC.2009.2033566","article-title":"A Survey on the Application of Genetic Programming to Classification","volume":"40","author":"Espejo","year":"2010","journal-title":"IEEE Trans. Syst. Man, Cybern. Part C (Appl. Rev.)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40747-017-0036-x","article-title":"Genetic programming for production scheduling: A survey with a unified framework","volume":"3","author":"Nguyen","year":"2017","journal-title":"Complex Intell. Syst."},{"key":"ref_14","unstructured":"Khan, A., Qureshi, A.S., Wahab, N., Hussain, M., and Hamza, M.Y. (2019). A Recent Survey on the Applications of Genetic Programming in Image Processing. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lan Woodward, F., Lomas, M., and Lee, S. (2001). Predicting the Future Productivity and Distribution of Global Terrestrial Vegetation. Terr. Glob. Prod., 521\u2013541.","DOI":"10.1016\/B978-012505290-0\/50023-5"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Woodward, S. (2009). Introduction to Biomes, Greenwood Press. Greenwood guides to biomes of the world.","DOI":"10.5040\/9798400671951"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1111\/j.1466-8238.2010.00634.x","article-title":"When is a \u2018forest\u2019 a savanna, and why does it matter?","volume":"20","author":"Ratnam","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/JSTARS.2016.2591004","article-title":"Firefly-Algorithm-Inspired Framework With Band Selection and Extreme Learning Machine for Hyperspectral Image Classification","volume":"10","author":"Su","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/JSTARS.2016.2557584","article-title":"A Hyperheuristic Approach for Unsupervised Land-Cover Classification","volume":"9","author":"Papa","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","first-page":"191","article-title":"Genetic Programming with Dynamic Fitness for a Remote Sensing Application","volume":"Volume 1917","author":"Fonlupt","year":"2000","journal-title":"International Conference on Parallel Problem Solving from Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TGRS.2008.922061","article-title":"A genetic-programming-based method for hyperspectral data information extraction: Agricultural applications","volume":"46","author":"Chion","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"363","DOI":"10.14358\/PERS.77.4.363","article-title":"A Genetic Programming Approach to Estimate Vegetation Cover in the Context of Soil Erosion Assessment","volume":"77","author":"Puente","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","first-page":"115","article-title":"Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery","volume":"52","author":"Gerstmann","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wimberly, M.C., and Dwomoh, F.K. (2017). Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015. Remote Sens., 9.","DOI":"10.3390\/rs9010005"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cost\u0103chioiu, T., and Datcu, M. (2010, January 10\u201312). Land cover dynamics classification using multi-temporal spectral indices from satellite image time series. Proceedings of the 2010 8th International Conference on Communications, Bucharest, Romania.","DOI":"10.1109\/ICCOMM.2010.5509070"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2015.03.004","article-title":"Analysis of uncertainty in multi-temporal object-based classification","volume":"105","author":"Conrad","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2012.08.017","article-title":"The effect of Landsat ETM\/ETM+ image acquisition dates on the detection of agricultural land abandonment in Eastern Europe","volume":"126","author":"Prishchepov","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2015.12.017","article-title":"Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations","volume":"174","author":"Balzarolo","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.asoc.2004.06.003","article-title":"Hyperspectral image analysis using genetic programming","volume":"5","author":"Ross","year":"2005","journal-title":"Appl. Soft Comput."},{"key":"ref_31","unstructured":"Rauss, P.J., Daida, J.M., and Chaudhary, S. (2000). Classification of spectral imagery using genetic programming. Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, Morgan Kaufmann Publishers Inc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LGRS.2017.2719033","article-title":"Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions","volume":"14","author":"Almeida","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/LGRS.2018.2872132","article-title":"A Soft Computing Framework for Image Classification Based on Recurrence Plots","volume":"16","author":"Menini","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1007\/s11042-012-1152-7","article-title":"Multimodal retrieval with relevance feedback based on genetic programming","volume":"69","author":"Calumby","year":"2014","journal-title":"Multimed. Tools Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s13173-012-0087-1","article-title":"Evaluation of parameters for combining multiple textual sources of evidence for Web image retrieval using genetic programming","volume":"19","author":"Saraiva","year":"2013","journal-title":"J. Braz. Comput. Soc."},{"key":"ref_36","unstructured":"Hern\u00e1ndez, J., dos Santos, J.A., and Torres, R.D.S. (2016, January 4\u20137). Learning to Combine Spectral Indices with Genetic Programming. Proceedings of the 29th Conference on Graphics, Patterns and Images (SIBGRAPI), Sao Paulo, Brazil."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Raghavan, P., and Sch\u00fctze, H. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/ele.12537","article-title":"Disturbance maintains alternative biome states","volume":"19","author":"Dantas","year":"2016","journal-title":"Ecol. Lett."},{"key":"ref_39","first-page":"57","article-title":"Virtual laboratory of remote sensing time series: Visualization of MODIS EVI2 data set over South America","volume":"2","author":"Freitas","year":"2011","journal-title":"J. Comput. Interdiscip. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with Erts","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Goddard Space Flight Cent. 3d ERTS-1 Symp."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, Springer New York Inc.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_47","unstructured":"Berndt, D.J., and Clifford, J. (1994). Using Dynamic Time Warping to Find Patterns in Time Series. AAAIWS\u201994, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAI Press."},{"key":"ref_48","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ecoinf.2013.12.011","article-title":"Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation","volume":"19","author":"Alberton","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ecoinf.2013.06.011","article-title":"Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees","volume":"23","author":"Almeida","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1111\/1365-2745.12969","article-title":"The environmental triangle of the Cerrado Domain: Ecological factors driving shifts in tree species composition between forests and savannas","volume":"106","author":"Bueno","year":"2018","journal-title":"J. Ecol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"14681","DOI":"10.1038\/ncomms14681","article-title":"Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks","volume":"8","author":"Zemp","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_54","unstructured":"Hern\u00e1ndez, J., Ferreira, E., dos Santos, J.A., and Torres, R.D.S. (2017, January 23\u201328). Fusion of genetic-programming-based indices in hyperspectral image classification tasks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/TCYB.2015.2404806","article-title":"A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification","volume":"46","author":"Nag","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liddle, T., Johnston, M., and Zhang, M. (2010, January 18\u201323). Multi-objective genetic programming for object detection. Proceedings of the IEEE Congress on Evolutionary Computation, Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586072"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/CEC.2001.934438","article-title":"Multiobjective genetic programming: Reducing bloat using SPEA2","volume":"Volume 1","author":"Bleuler","year":"2001","journal-title":"Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546)"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1109\/TNNLS.2013.2293418","article-title":"Feature learning for image classification via multiobjective genetic programming","volume":"25","author":"Shao","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSMCA.2004.826299","article-title":"\u2019Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming","volume":"34","author":"Fonseca","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.cie.2007.08.008","article-title":"Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems","volume":"54","author":"Tay","year":"2008","journal-title":"Comput. Ind. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.1007\/s00521-017-3253-8","article-title":"Figure-ground image segmentation using feature-based multi-objective genetic programming techniques","volume":"31","author":"Liang","year":"2019","journal-title":"Neural Comput. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2267\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:48Z","timestamp":1760176128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,15]]},"references-count":61,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12142267"],"URL":"https:\/\/doi.org\/10.3390\/rs12142267","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,7,15]]}}}