{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:11:10Z","timestamp":1774627870873,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The research leading to these results has received funding from the Norway Grants 2014-2021 via the National Center for Research and Development.","award":["NOR\/POLNOR\/InCoNaDa\/0050\/2019-00"],"award-info":[{"award-number":["NOR\/POLNOR\/InCoNaDa\/0050\/2019-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring of land cover plays an important role in effective environmental management, assessment of natural resources, environmental protection, urban planning and sustainable development. Increasing demand for accurate and repeatable information on land cover and land cover changes causes rapid development of the advanced, machine learning algorithms dedicated to land cover mapping using satellite images. Free and open access to Sentinel-2 data, characterized with high spatial and temporal resolution, increased the potential to map and to monitor land surface with high accuracy and frequency. Despite a considerable number of approaches towards land cover classification based on satellite data, there is still a challenge to clearly separate complex land cover classes, for example grasslands, arable land and wetlands. The aim of this study is to examine, whether a hierarchal classification of Sentinel-2 data can improve the accuracy of land cover mapping and delineation of complex land cover classes. The study is conducted in the Lodz Province, in central Poland. The pixel-based land cover classification is carried out using the machine learning Random Forest (RF) algorithm, based on a time series of Sentinel-2 imagery acquired in 2020. The following nine land cover classes are mapped: sealed surfaces, woodland broadleaved, woodland coniferous, shrubs, permanent herbaceous (grassy cover), periodically herbaceous (i.e., arable land), mosses, non-vegetated (bare soil) and water bodies. The land cover classification is conducted following two approaches: (1) flat, where all land cover classes are classified together, and (2) hierarchical, where the stratification is applied to first separate the most stable land cover classes and then classifying the most problematic once. The national databases served as the source of the reference sampling plots for the classification process. The process of selection and verification of the reference sampling plots is performed automatically. To assess the stability of the classification models the classification processes are performed iteratively. The results of this study confirmed that the hierarchical approach gave more accurate results compared to the commonly used flat approach. The median of the overall accuracy (OA) of the hierarchical classification was higher by 3\u20139 percentage points compared to the flat one. Of interest, the OA of the hierarchical classification reached 0.93\u20130.99, whereas the flat approach reached 0.90. Individual classes are also better classified in the hierarchical approach.<\/jats:p>","DOI":"10.3390\/rs14040989","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T20:26:41Z","timestamp":1645129601000},"page":"989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Can a Hierarchical Classification of Sentinel-2 Data Improve Land Cover Mapping?"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7174-4590","authenticated-orcid":false,"given":"Adam","family":"Wa\u015bniewski","sequence":"first","affiliation":[{"name":"Centre of Applied Geomatics, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"},{"name":"Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmie\u015bcie 30, 00-927 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3304-2445","authenticated-orcid":false,"given":"Agata","family":"Ho\u015bci\u0142o","sequence":"additional","affiliation":[{"name":"Centre of Applied Geomatics, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}]},{"given":"Milena","family":"Chmielewska","sequence":"additional","affiliation":[{"name":"Centre of Applied Geomatics, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29900","DOI":"10.1007\/s11356-020-09091-7","article-title":"Survey on Land Use\/Land Cover (LU\/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges","volume":"27","author":"Mohanrajan","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Khaliq, A., and Chiaberge, M. (2020). Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Appl. Sci., 10.","DOI":"10.3390\/app10010238"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Borges, J., Higginbottom, T.P., Symeonakis, E., and Jones, M. (2020). Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons. Remote Sens., 12.","DOI":"10.3390\/rs12233862"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brovelli, M.A., Sun, Y., and Yordanov, V. (2020). Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100580"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"H\u00e4me, T., Sirro, L., Kilpi, J., Seitsonen, L., Andersson, K., and Melkas, T. (2020). A Hierarchical Clustering Method for Land Cover Change Detection and Identification. Remote Sens., 12.","DOI":"10.3390\/rs12111751"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.5194\/isprs-archives-XLI-B8-1055-2016","article-title":"Assessment of classification accuracies of SENTINEL-2 and LANDSAT-8 data for land cover\/use mapping","volume":"41","author":"Topaloglu","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1080\/22797254.2017.1365570","article-title":"Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery","volume":"50","author":"Olariu","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/gcb.13388","article-title":"Allometric equations for integrating remote sensing imagery into forest monitoring programmes","volume":"23","author":"Jucker","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Randazzo, G., Cascio, M., Fontana, M., Gregorio, F., Lanza, S., and Muzirafuti, A. (2021). Mapping of Sicilian Pocket Beaches Land Use\/Land Cover with Sentinel-2 Imagery: A Case Study of Messina Province. Land, 10.","DOI":"10.3390\/land10070678"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ye, J., Hu, Y., Zhen, L., Wang, H., and Zhang, Y. (2021). Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000\u20132020 Using the Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13245134"},{"key":"ref_12","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Da Ponte, E., Mack, B., Wohlfart, C., Rodas, O., Fleckenstein, M., Oppelt, N., Dech, S., and Kuenzer, C. (2017). Assessing Forest Cover Dynamics and Forest Perception in the Atlantic Forest of Paraguay, Combining Remote Sensing and Household Level Data. Forests, 8.","DOI":"10.3390\/f8100389"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wa\u015bniewski, A., Ho\u015bci\u0142o, A., Zagajewski, B., and Mouk\u00e9tou-Tarazewicz, D. (2020). Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon. Forests, 11.","DOI":"10.3390\/f11090941"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ho\u015bci\u0142o, A., and Lewandowska, A. (2019). Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5660","DOI":"10.3390\/rs70505660","article-title":"Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery","volume":"7","author":"Fagan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1080\/01431160903571791","article-title":"Comparison of pixel- and object-based classification in land cover change mapping","volume":"32","author":"Robertson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","unstructured":"ESA (2022, January 09). Sentinel-2 Missions-Sentinel Online; ESA: Paris, France. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_25","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2021). ESA WorldCover 10 m 2020 v100, European Space Agency."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Malinowski, R., Lewi\u0144ski, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupi\u0144ski, M., Nowakowski, A., Wojtkowski, C., Krupi\u0144ski, M., and Kr\u00e4tzschmar, E. (2020). Automated Production of a Land Cover\/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12213523"},{"key":"ref_28","unstructured":"Olsen, J.B. (2020). Technical Specifications for Implementation of a New Land-Monitoring Concept Based on EAGLE. Public Consultation Document for CLC+ Core, European Environment Agency."},{"key":"ref_29","unstructured":"Europe\u2019s Eyes on Earth, and Land Monitoring Service (2021, November 07). CLC+. Available online: https:\/\/land.copernicus.eu\/pan-european\/clc-plus."},{"key":"ref_30","first-page":"595","article-title":"Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions","volume":"73","author":"Steinhausen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_32","unstructured":"Aghdam, H.H., and Heravi, E.J. (2017). Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification, Springer Publishing Company, Incorporated."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1007\/s42452-019-1527-8","article-title":"Evaluation and comparison of eight machine learning models in land use\/land cover mapping using Landsat 8 OLI: A case study of the northern region of Iran","volume":"1","author":"Jamali","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/20964471.2019.1690404","article-title":"A generalized supervised classification scheme to produce provincial wetland inventory maps: An application of Google Earth Engine for big geo data processing","volume":"3","author":"Amani","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A.H., Tard\u00e0, A., Pineda, L., and Corbera, J. (2021). Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13040777"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T.T., Doan, T.M., Tomppo, E., and McRoberts, R.E. (2020). Land Use\/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods\u2014A Case Study from Dak Nong, Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12091367"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.scitotenv.2018.07.353","article-title":"Development of a spatially complete floodplain map of the conterminous United States using random forest","volume":"647","author":"Woznicki","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2017.08.028","article-title":"Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping","volume":"200","author":"Clark","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.envsoft.2018.01.023","article-title":"A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms","volume":"104","author":"Whyte","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_45","first-page":"15","article-title":"Hierarchical Object-Based Mapping of Urban Land Cover Using Sentinel-2 Data: A Case Study of Six Cities in Central Europe","volume":"89","year":"2021","journal-title":"PFG\u2013J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.5937\/22-16620","article-title":"A hierarchical approach of hybrid image classification for land use and land cover mapping","volume":"22","author":"Rahdari","year":"2018","journal-title":"Geogr. Pannonica"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s11263-013-0636-x","article-title":"Image Classification with the Fisher Vector: Theory and Practice","volume":"105","author":"Perronnin","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","unstructured":"Avci, M. (2004). A Hierarchical classification of Landsat TM Imagery For Landcover Mapping. Int. Soc. Photogramm. Remote Sens., IV."},{"key":"ref_49","first-page":"026524","article-title":"Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the Corine system","volume":"14","author":"Demirkan","year":"2020","journal-title":"J. App. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5019","DOI":"10.3390\/rs6065019","article-title":"Object-Based Image Classification of Summer Crops with Machine Learning Methods","volume":"6","author":"Six","year":"2014","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Haest, B., Vanden Borre, J., Spanhove, T., Thoonen, G., Delalieux, S., Kooistra, L., M\u00fccher, C., Paelinckx, D., Scheunders, P., and Kempeneers, P. (2017). Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9030266"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1139\/cjfr-2014-0562","article-title":"Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance","volume":"46","author":"Freeman","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_53","unstructured":"Kleeschulte, S., Banko, G., Smith, G., Arnold, S., Scholz, J., Kosztra, B., and Maucha, G. (2017). Maucha Technical Specifications for Implementation of a New Land-Monitoring Concept Based on EAGLE. D3: Draft Design Concept and CLC-Backbone, CLC-Core Technical Specifications, Including Requirements Review, European Environment Agency."},{"key":"ref_54","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Lesiv, M., Li, L., and Tsendbazar, N.E. (2020). ESA WorldCover 10 m 2020 v1. Product User Manual, European Space Agency."},{"key":"ref_55","unstructured":"BDL (2021, September 03). Bank Danych o Lasach, Available online: https:\/\/www.bdl.lasy.gov.pl\/portal\/opis-bdl."},{"key":"ref_56","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_57","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_58","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jiao, L., Sun, W., Yang, G., Ren, G., and Liu, Y. (2019). A Hierarchical Classification Framework of Satellite Multispectral\/Hyperspectral Images for Mapping Coastal Wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11192238"},{"key":"ref_60","unstructured":"Europe\u2019s Eyes on Earth (2021, December 13). High Resolution Layers. Available online: https:\/\/land.copernicus.eu\/pan-european\/high-resolution-layers."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/989\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:21:58Z","timestamp":1760134918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,17]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14040989"],"URL":"https:\/\/doi.org\/10.3390\/rs14040989","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,17]]}}}