{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:13:14Z","timestamp":1774008794782,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,16]],"date-time":"2021-01-16T00:00:00Z","timestamp":1610755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"N\u00facleo de Investiga\u00e7\u00e3o em Intelig\u00eancia Artificial em Agricultura","award":["ALT20-03-0247-FEDER-036981"],"award-info":[{"award-number":["ALT20-03-0247-FEDER-036981"]}]},{"name":"SPARKLE: Sustainable precision agriculture: research and knowledge for learning how to be an agri-entrepreneur","award":["ERASMUS+"],"award-info":[{"award-number":["ERASMUS+"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.<\/jats:p>","DOI":"10.3390\/rs13020300","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:34:25Z","timestamp":1611113665000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6166-2038","authenticated-orcid":false,"given":"Kashyap","family":"Raiyani","sequence":"first","affiliation":[{"name":"Department of Informatics, School of Science and Technology, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1323-0249","authenticated-orcid":false,"given":"Teresa","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Department of Informatics, School of Science and Technology, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4492-7548","authenticated-orcid":false,"given":"Lu\u00eds","family":"Rato","sequence":"additional","affiliation":[{"name":"Department of Informatics, School of Science and Technology, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7614-2951","authenticated-orcid":false,"given":"Pedro","family":"Salgueiro","sequence":"additional","affiliation":[{"name":"Department of Informatics, School of Science and Technology, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-8147","authenticated-orcid":false,"given":"Jos\u00e9 R.","family":"Marques da Silva","sequence":"additional","affiliation":[{"name":"Mediterranean Institute for Agriculture, Environment and Development (MED), Department of Rural Engineering, School of Science and Technology, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"},{"name":"Agroinsider Lda., PITE, R. Circular Norte, NERE, Sala 18, 7005-841 \u00c9vora, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., Krammer, T.A., and Saeedi, P. (2018). Cloud detection algorithm for remote sensing images using fully convolutional neural networks. arXiv.","DOI":"10.1109\/MMSP.2018.8547095"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/19475683.2014.992369","article-title":"Change analysis of land use\/land cover and modelling urban growth in Greater Doha, Qatar","volume":"21","author":"Hashem","year":"2015","journal-title":"Ann. GIS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s12524-011-0165-4","article-title":"Assessment of land use\/land cover change in the North-West District of Delhi using remote sensing and GIS techniques","volume":"40","author":"Rahman","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ecolind.2017.04.055","article-title":"Assessing spatiotemporal eco-environmental vulnerability by Landsat data","volume":"80","author":"Liou","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.mex.2019.03.023","article-title":"Mapping global eco-environment vulnerability due to human and nature disturbances","volume":"6","author":"Nguyen","year":"2019","journal-title":"MethodsX"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.3390\/rs70505077","article-title":"Object-based flood mapping and affected rice field estimation with Landsat 8 OLI and MODIS data","volume":"7","author":"Dao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","first-page":"399","article-title":"An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (Western Antalya, Turkey)","volume":"26","author":"San","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","unstructured":"(2020, February 04). Sentinel-2 Mission. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_9","unstructured":"(2020, June 22). European Copernicus Programme. Available online: https:\/\/www.copernicus.eu\/en."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"Main-Knorn, M., Louis, J., Hagolle, O., M\u00fcller-Wilm, U., and Alonso, K. (2018, January 29\u201331). The Sen2Cor and MAJA cloud masks and classification products. Proceedings of the 2nd Sentinel-2 Validation Team Meeting, ESA-ESRIN, Frascati, Rome, Italy."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u03bcS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"964307","article-title":"MACCS: Multi-Mission Atmospheric Correction and Cloud Screening tool for high-frequency revisit data processing","volume":"Volume 9643","author":"Petrucci","year":"2015","journal-title":"Image and Signal Processing for Remote Sensing XXI"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TGRS.2011.2159726","article-title":"SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images","volume":"50","author":"Moustakidis","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/TGRS.2012.2185504","article-title":"Semisupervised classification of remote sensing images with active queries","volume":"50","author":"Tuia","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_24","unstructured":"Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. (2020, August 04). Maja Algorithm Theoretical Basis Document. Available online: https:\/\/zenodo.org\/record\/1209633#.XpdnZvnQ-Cg."},{"key":"ref_25","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A processor for users. Proceedings of the Living Planet Symposium 2016, Spacebooks Online, Prague, Czech Republic."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and Dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","article-title":"PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval","volume":"145","author":"Zhou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sumbul, G., Charfuelan, M., Demir, B., and Markl, V. (August, January 28). Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900532"},{"key":"ref_34","unstructured":"(2020, February 04). Sentinel-2 MSI\u2014Level 2A Products Algorithm Theoretical Basis Document. Available online: https:\/\/earth.esa.int\/c\/document_library\/get_file?folderId=349490&name=DLFE-4518.pdf."},{"key":"ref_35","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"ref_37","unstructured":"Russell, S.J., and Norvig, P. (2010). Artificial Intelligence\u2014A Modern Approach, Prentice Hall Press. [3rd ed.]."},{"key":"ref_38","unstructured":"Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2012). Foundations of Machine Learning. Adaptive Computation and Machine Learning, MIT Press."},{"key":"ref_39","unstructured":"(2020, August 04). Creodias Platfrom. Available online: https:\/\/creodias.eu\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2016). Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images. Remote Sens., 8.","DOI":"10.3390\/rs8080666"},{"key":"ref_41","unstructured":"ESA, S.O. (2020, August 04). Resolution and Swath. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2\/instrument-payload\/resolution-and-swath."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/234313.234346","article-title":"Learning decision tree classifiers","volume":"28","author":"Quinlan","year":"1996","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Classification trees and rule-based models. Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3_14"},{"key":"ref_44","unstructured":"(2020, June 22). Decision Tree. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.tree.DecisionTreeClassifier.html."},{"key":"ref_45","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.procs.2015.05.157","article-title":"A fuzzy decision tree for processing satellite images and landsat data","volume":"52","author":"Belacel","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.rse.2006.10.019","article-title":"Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data","volume":"107","author":"Wright","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"(2020, June 22). Ensemble Methods. Available online: https:\/\/scikit-learn.org\/stable\/modules\/ensemble.html."},{"key":"ref_50","unstructured":"(2020, June 22). Random Forest. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_52","unstructured":"(2020, June 22). Extra Tress. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.ExtraTreesClassifier.html."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhong, Y., Fei, F., Zhu, Q., and Qin, Q. (2018). Scene classification based on a deep random-scale stretched convolutional neural network. Remote Sens., 10.","DOI":"10.3390\/rs10030444"},{"key":"ref_55","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_56","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_58","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Abdel-Hamid, O., Deng, L., and Yu, D. (2013, January 25\u201329). Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech recognition. Proceedings of the Interspeech 2013, Lyon, France.","DOI":"10.21437\/Interspeech.2013-744"},{"key":"ref_60","unstructured":"(2020, June 03). How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition. Available online: https:\/\/machinelearningmastery.com\/cnn-models-for-human-activity-recognition-time-series-classification\/."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kramer, O. (2016). Scikit-learn. Machine Learning for Evolution Strategies, Springer.","DOI":"10.1007\/978-3-319-33383-0_5"},{"key":"ref_62","unstructured":"(2020, February 04). Feature Selection. Available online: https:\/\/scikit-learn.org\/stable\/modules\/classes.html."},{"key":"ref_63","unstructured":"(2020, February 04). Feature Selection chi2. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.chi2.html."},{"key":"ref_64","unstructured":"(2020, February 04). Feature Selection mutual_info_classif. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.mutual_info_classif.html."},{"key":"ref_65","unstructured":"(2020, February 04). Feature Selection f_classif. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.f_classif.html."},{"key":"ref_66","unstructured":"(2020, February 04). Feature Selection f_regression. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.f_regression.html."},{"key":"ref_67","unstructured":"(2020, June 03). F1 Score. Available online: https:\/\/en.wikipedia.org\/wiki\/F1_score."},{"key":"ref_68","unstructured":"(2020, June 22). RandomizedSearchCV. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RandomizedSearchCV.html."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/01621459.1948.10483284","article-title":"A test for symmetry in contingency tables","volume":"43","author":"Bowker","year":"1948","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lancaster, H.O., and Seneta, E. (2005). Chi-square distribution. Encyclopedia of Biostatistics, American Cancer Society. [2nd ed.].","DOI":"10.1002\/0470011815.b2a15018"},{"key":"ref_73","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_74","unstructured":"Henrich, V., G\u00f6tze, E., Jung, A., Sandow, C., Th\u00fcrkow, D., and Gl\u00e4\u00dfer, C. (2009, January 16\u201318). Development of an online indices database: Motivation, concept and implementation. Proceedings of the 6th EARSeL Imaging Spectroscopy SIG Workshop Innovative Tool for Scientific and Commercial Environment Applications, Tel Aviv, Israel."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A Physically-Based Transformation of Thematic Mapper Data-The TM Tasseled Cap","volume":"22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1046\/j.0028-646X.2001.00289.x","article-title":"An evaluation of noninvasive methods to estimate foliar chlorophyll content","volume":"153","author":"Richardson","year":"2002","journal-title":"New Phytol."},{"key":"ref_77","unstructured":"Alkhaier, F. (2021, January 16). Soil Salinity Detection Using Satellite Remote Sensing. Available online: https:\/\/webapps.itc.utwente.nl\/librarywww\/papers_2003\/msc\/wrem\/khaier.pdf."},{"key":"ref_78","unstructured":"Gabrani, M., and Tretiak, O.J. (1996, January 3\u20136). Elastic transformations. Proceedings of the Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Raiyani, K. (2021, January 14). Ready to Use Machine Learning Approach towards Sentinel-2 Image Scene Classification. Available online: https:\/\/github.com\/kraiyani\/Sentinel-2-Image-Scene-Classification-A-Comparison-between-Sen2Cor-and-a-Machine-Learning-Approach.","DOI":"10.3390\/rs13020300"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"GDAL\/OGR contributors (2020). GDAL\/OGR Geospatial Data Abstraction Software Library, Open Source Geospatial Foundation.","DOI":"10.22224\/gistbok\/2020.4.1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:55Z","timestamp":1760159515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,16]]},"references-count":80,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020300"],"URL":"https:\/\/doi.org\/10.3390\/rs13020300","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,16]]}}}