{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:16:51Z","timestamp":1774365411496,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-2016-StG-714087"],"award-info":[{"award-number":["ERC-2016-StG-714087"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","award":["VH-NG-1018"],"award-info":[{"award-number":["VH-NG-1018"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007306","name":"Bayerische Akademie der Wissenschaften","doi-asserted-by":"publisher","award":["framework of Junges Kolleg"],"award-info":[{"award-number":["framework of Junges Kolleg"]}],"id":[{"id":"10.13039\/501100007306","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each K\u00f6ppen climate zone.<\/jats:p>","DOI":"10.3390\/rs12040602","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T11:45:30Z","timestamp":1581421530000},"page":"602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine"],"prefix":"10.3390","volume":"12","author":[{"given":"Qingyu","family":"Li","sequence":"first","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunping","family":"Qiu","sequence":"additional","affiliation":[{"name":"Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-2362","authenticated-orcid":false,"given":"Michael","family":"Schmitt","sequence":"additional","affiliation":[{"name":"Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5530-3613","authenticated-orcid":false,"given":"Xiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"25","article-title":"The most detailed portrait of Earth","volume":"136","author":"Arino","year":"2008","journal-title":"Eur. Space Agency"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1080\/014311600210209","article-title":"Global land cover classification at 1 km spatial resolution using a classification tree approach","volume":"21","author":"Hansen","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","unstructured":"Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., and Kirches, G. (2013, January 9\u201313). Consistent global land cover maps for climate modelling communities: Current achievements of the ESA\u2019s land cover CCI. Proceedings of the ESA Living Planet Symposium, Edinburgh, UK."},{"key":"ref_5","unstructured":"Latham, J., Cumani, R., Rosati, I., and Bloise, M. (2014). Global Land Cover Share (GLC-SHARE) Database Beta-Release Version 1.0-2014, FAO."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/17538941003777521","article-title":"Production of global land cover data\u2013GLCNMO","volume":"4","author":"Tateishi","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_7","unstructured":"Copernicus Global Land Service (2018, June 12). Providing Bio-Geophysical Products of Global Land Surface. Available online: https:\/\/land.copernicus.eu\/global\/index.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/22797254.2018.1451782","article-title":"Using Google Earth Engine to detect land cover change: Singapore as a use case","volume":"51","author":"Sidhu","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Qiu, C., Schmitt, M., Geiss, C., Chen, T.K., and Zhu, X.X. (2020). A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks. arXiv.","DOI":"10.1016\/j.isprsjprs.2020.01.028"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Steinmann, G. (1989). Population, Resources, and Limits to Growth. Development Economics: Theory, Practice, and Prospects, Springer.","DOI":"10.1007\/978-94-009-1077-5_4"},{"key":"ref_17","first-page":"2","article-title":"Land resource stresses and desertification in Africa","volume":"2","author":"Reich","year":"2001","journal-title":"Agro-Science"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Midekisa, A., Holl, F., Savory, D.J., Andrade-Pacheco, R., Gething, P.W., Bennett, A., and Sturrock, H.J. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0184926"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2016.2561021","article-title":"Data fusion and remote sensing: An ever-growing relationship","volume":"4","author":"Schmitt","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.uclim.2018.11.001","article-title":"Global transferability of local climate zone models","volume":"27","author":"Demuzere","year":"2019","journal-title":"Urban Clim."},{"key":"ref_21","unstructured":"Gatti, A., and Bertolini, A. (2015, February 23). Sentinel-2 Products Specification Document. Available online: https:\/\/earth.esa.int\/documents\/247904\/685211\/Sentinel-2+Products+Specification+Document."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia-Pac. Adv. Netw."},{"key":"ref_24","unstructured":"Pesaresi, M., Ehrilch, D., Florczyk, A.J., Freire, S., Julea, A., Kemper, T., Soille, P., and Syrris, V. (2020, January 10). (In Luxembourg)."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_26","unstructured":"Wan, Z., Hook, S., and Hulley, G. (2019, June 23). MYD11A2 MODIS\/Aqua Land Surface Temperature\/Emissivity 8-Day L3 Global 1 km SIN Grid V006. 2015, Distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MYD11A2.006."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.rse.2015.08.006","article-title":"Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems","volume":"169","author":"Braaten","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS."},{"key":"ref_31","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":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.proenv.2015.03.028","article-title":"Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries","volume":"24","author":"Jhonnerie","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1175\/BAMS-D-11-00019.1","article-title":"Local climate zones for urban temperature studies","volume":"93","author":"Stewart","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1002\/joc.3746","article-title":"Evaluation of the \u2018local climate zone\u2019scheme using temperature observations and model simulations","volume":"34","author":"Stewart","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_35","first-page":"439","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"4","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"ref_36","unstructured":"Rikimaru, A. (1997, January 20\u201324). Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow. Proceedings of the 18th ACRS, Kuala Lumpur, Malaysia."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.rse.2012.06.006","article-title":"Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach","volume":"124","author":"Schneider","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Hu, Y. (2019). Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11050554"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"109","DOI":"10.14358\/PERS.83.2.109","article-title":"Integrating multiple textural features for remote sensing image change detection","volume":"83","author":"Li","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","first-page":"87","article-title":"A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments","volume":"49","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020051"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Melchiorri, M., Florczyk, A., Freire, S., Schiavina, M., Pesaresi, M., and Kemper, T. (2018). Unveiling 25 years of planetary urbanization with remote sensing: Perspectives from the global human settlement layer. Remote Sens., 10.","DOI":"10.3390\/rs10050768"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/LGRS.2016.2615318","article-title":"Assessing and improving the accuracy of GlobeLand30 data for urban area delineation by combining multisource remote sensing data","volume":"13","author":"Huang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"833","DOI":"10.14358\/PERS.76.7.833","article-title":"Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data","volume":"76","author":"Na","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1023\/A:1013051420309","article-title":"Effects of land cover conversion on surface climate","volume":"52","author":"Bounoua","year":"2002","journal-title":"Clim. Chang."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Antos, S.E., Lall, S.V., and Lozano-Gracia, N. (2016). The Morphology of African Cities, The World Bank.","DOI":"10.1596\/1813-9450-7911"},{"key":"ref_52","first-page":"12","article-title":"Challenges Affecting the Development and Optimal Use of Tall Buildings in Nigeria","volume":"3","author":"Ede","year":"2014","journal-title":"Int. J. Eng. Sci. (IJES)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lall, S.V., Henderson, J.V., and Venables, A.J. (2017). Africa\u2019s Cities: Opening Doors to the World, The World Bank.","DOI":"10.1596\/978-1-4648-1044-2"},{"key":"ref_54","unstructured":"Hass, A., and Kopanyi, M. (2017). Taxation of Vacant Urban Land: From Theory to Practice, International Growth Center, London School of Economic and Political Science."},{"key":"ref_55","unstructured":"Abdulazeez, A. (2015). A Description of the Physical and Human Geographies of the Niger Republic Capital City, Niamey, Bayero University."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2019.05.004","article-title":"Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network","volume":"154","author":"Qiu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.5194\/bg-8-2027-2011","article-title":"Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction","volume":"8","author":"Poulter","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1007\/s12517-013-0916-3","article-title":"Monitoring land use\/land cover change using multi-temporal Landsat satellite images in an arid environment: A case study of El-Arish, Egypt","volume":"7","author":"Badreldin","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Huang, C., Yang, J., and Jiang, P. (2018). Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10101569"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.landurbplan.2017.08.009","article-title":"Evaluating urban heat island in the critical local climate zones of an Indian city","volume":"169","author":"Kotharkar","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.5194\/isprs-archives-XLI-B8-1371-2016","article-title":"Towards consistent mapping of urban structure-global human settlement layer and local climate zones","volume":"41","author":"Bechtel","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/602\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:57Z","timestamp":1760173017000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,11]]},"references-count":63,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12040602"],"URL":"https:\/\/doi.org\/10.3390\/rs12040602","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,11]]}}}