{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T05:29:29Z","timestamp":1785821369657,"version":"3.56.0"},"reference-count":76,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Education and Research (BMBF)"},{"name":"Ministry of Science, Education"},{"name":"Culture of the state of Mecklenburg-Vorpommern (MV)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting).<\/jats:p>","DOI":"10.3390\/rs16091493","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T12:20:30Z","timestamp":1713874830000},"page":"1493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2190-6640","authenticated-orcid":false,"given":"Hauke","family":"Hoppe","sequence":"first","affiliation":[{"name":"Fraunhofer Institute for Computer Graphics Research (IGD), Joachim-Jungius-Str. 11, D-18059 Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2699-2354","authenticated-orcid":false,"given":"Peter","family":"Dietrich","sequence":"additional","affiliation":[{"name":"Environmental and Engineering Geophysics, Eberhard Karls University T\u00fcbingen, Schnarrenbergstr. 94-96, D-72076 T\u00fcbingen, Germany"},{"name":"Department of Monitoring and Exploration Technologies, Helmholtz Center for Environmental Research, D-04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6294-120X","authenticated-orcid":false,"given":"Philip","family":"Marzahn","sequence":"additional","affiliation":[{"name":"Geodesy and Geoinformatics, University of Rostock, D-18059 Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5278-8379","authenticated-orcid":false,"given":"Thomas","family":"Wei\u00df","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Computer Graphics Research (IGD), Joachim-Jungius-Str. 11, D-18059 Rostock, Germany"},{"name":"Geodesy and Geoinformatics, University of Rostock, D-18059 Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Nitzsche","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Computer Graphics Research (IGD), Joachim-Jungius-Str. 11, D-18059 Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1659-4678","authenticated-orcid":false,"given":"Uwe","family":"Freiherr von Lukas","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Computer Graphics Research (IGD), Joachim-Jungius-Str. 11, D-18059 Rostock, Germany"},{"name":"Institute for Visual and Analytic Computing, University of Rostock, D-18059 Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1056-3512","authenticated-orcid":false,"given":"Thomas","family":"Wengerek","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Hochschule Stralsund, University of Applied Sciences, D-18435 Stralsund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8288-8426","authenticated-orcid":false,"given":"Erik","family":"Borg","sequence":"additional","affiliation":[{"name":"German Aerospace Center, German Remote Sensing Data Center, National Ground Segment, D-17235 Neustrelitz, Germany"},{"name":"Geoinformatics and Geodesy, Neubrandenburg University of Applied Sciences, D-17033 Neubrandenburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2017). World Population Prospects 2017: Data Booklet, United Nations, Department of Economic and Social Affairs."},{"key":"ref_2","first-page":"21","article-title":"Effects of environmental change on agriculture, nutrition and health: A framework with a focus on fruits and vegetables","volume":"2","author":"Tuomisto","year":"2017","journal-title":"Wellcome Open Res."},{"key":"ref_3","first-page":"37480","article-title":"Intensive Farming: It\u2019s Effect on the Environment","volume":"12","author":"Shankar","year":"2021","journal-title":"Int. Bimon."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100878","DOI":"10.1016\/j.aeolia.2023.100878","article-title":"Landscape structure model based estimation of the wind erosion risk in Brandenburg, Germany","volume":"62","author":"Funk","year":"2023","journal-title":"Aeolian Res."},{"key":"ref_5","unstructured":"SUHET (2013). Sentinel-2, User Handbook, European Space Agency (ESA). [1st ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112831","DOI":"10.1016\/j.rse.2021.112795","article-title":"Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany","volume":"269","author":"Schwieder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"55","article-title":"Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany","volume":"61","author":"Waldhoff","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N.H., and Islam, N. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14091990"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Santaga, F.S., Benincasa, P., Toscano, P., Antognelli, S., Ranieri, E., and Vizzari, M. (2021). Simplified and Advanced Sentinel-2-Based Precision Nitrogen Management of Wheat. Agronomy, 11.","DOI":"10.3390\/agronomy11061156"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2011.07.023","article-title":"ESA\u2019s sentinel missions in support of Earth system science","volume":"120","author":"Berger","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","unstructured":"Thompson, N.C., Ge, S., and Manso, G.F. (2022, January 5\u20139). The Importance of (Exponentially More) Computing Power. Proceedings of the Academy of Management Annual Meeting Proceedings, Seattle, DC, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Molas, G., and Nowak, E. (2021). Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence. Appl. Sci., 11.","DOI":"10.3390\/app112311254"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"225134","DOI":"10.1109\/ACCESS.2020.3039858","article-title":"Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead","volume":"8","author":"Capra","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1196634","DOI":"10.3389\/fpls.2023.1196634","article-title":"Crop classification in high-resolution remote sensing images based on multi-scale feature fusion semantic segmentation model","volume":"14","author":"Lu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Q., Tian, J., and Tian, Q. (2023). Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images. Agriculture, 13.","DOI":"10.3390\/agriculture13040906"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, J., Zhang, H., Yang, H., and Zhang, L. (2018, January 6\u20139). Support Vector Machine Classification of Crop Lands Using Sentinel-2 Imagery. Proceedings of the 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Hangzhou, China.","DOI":"10.1109\/Agro-Geoinformatics.2018.8476101"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5721\/EuJRS20124535","article-title":"Evaluation of random forest method for agricultural crop classification","volume":"45","author":"Ok","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_20","first-page":"683","article-title":"Crop Classification on Single Date SENTINEL-2 Imagery Using Random Forest and Suppor Vector Machine","volume":"425","author":"Saini","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., and Wu, W. (2017). In-Season Crop Mapping with GF-1\/WFV Data by Combining Object-Based Image Analysis and Random Forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSTARS.2011.2106198","article-title":"Crop Classification Using Short-Revisit Multitemporal SAR Data","volume":"4","author":"Skriver","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Orynbaikyzy, A., Gessner, U., and Conrad, C. (2022). Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2. Remote Sens., 14.","DOI":"10.3390\/rs14061493"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rus\u0148\u00e1k, T., Kasanick\u00fd, T., Mal\u00edk, P., Moj\u017ei\u0161, J., Zelenka, J., Svi\u010dek, M., Abrah\u00e1m, D., and Halabuk, A. (2023). Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning. Remote Sens., 15.","DOI":"10.3390\/rs15133414"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Z., Zhang, L., Han, J., Cao, J., and Zhang, J. (2022). Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data. Remote Sens., 14.","DOI":"10.3390\/rs14081809"},{"key":"ref_27","first-page":"102451","article-title":"Transferable deep learning model based on the phenological matching principle for mapping crop extent","volume":"102","author":"Ge","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1111\/j.2041-210X.2011.00170.x","article-title":"Assessing transferability of ecological models: An underappreciated aspect of statistical validation","volume":"3","author":"Wenger","year":"2012","journal-title":"Methods Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2016","journal-title":"Ecography"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.08.007","article-title":"Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines","volume":"85","author":"Michel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Bundesamt, S. (2020). Statistisches Jahrbuch, Amt f\u00fcr Statistik Berlin-Brandenburg."},{"key":"ref_33","unstructured":"(2023, September 28). Klima & Gjennomsnittsv\u00e6r i Cottbus, Brandenburg, Tyskland. Available online: https:\/\/www.timeanddate.no\/vaer\/tyskland\/cottbus\/klima."},{"key":"ref_34","unstructured":"Wendel, M. (2007). Das Klima Norddeutschlands, VerlagGRIN Verlag."},{"key":"ref_35","unstructured":"Gerstengarbe, F., Badeck, F.W., Hattermann, F., Krysanova, V., Lahmer, W., Lasch, P., Stock, M., Suckow, F., Wechsung, F., and Werner, P. (2003). Studie zur Klimatischen Entwicklung im Land Brandenburg bis 2055 und 55 deren Auswirkungen auf den Wasserhaushalt, Die Forst- und Landwirtschaft Sowie Die Ableitung Erster Perspektive, Ministerium f\u00fcr Landwirtschaft, Umwelt und Klimaschutz des Landes Brandenburg."},{"key":"ref_36","unstructured":"Stackebrandt, W. (2010). Atlas zur Geologie von Brandenburg: Im Ma\u00dfstab 1:1,000,000, Landesamt f\u00fcr Bergbau, Geologie und Rohstoffe Brandenburg."},{"key":"ref_37","unstructured":"Remy, T.H.D. (2011). Jahrestagung der Floristisch-Soziologischen Arbeitsgemeinschaft (FlorSoz) in Potsdam 2011, Tuexenia."},{"key":"ref_38","unstructured":"Hofmann, G., and Pommer, U. (2005). Potentielle Nat\u00fcrliche Vegetation von Brandenburg und Berlin: Mit Karte im Ma\u00dfstab 1:200,000, Ministry of Rural Development, Environment and Consumer Protection. Eberswalder Forstliche Schriftenreihe, Ministerium f\u00fcr L\u00e4ndliche Entwicklung, Umwelt und Verbraucherschutz des Landes Brandenburg, Referat Presse- und \u00d6ffentlichkeitsarbeit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1111\/j.1502-3885.2011.00240.x","article-title":"Younger Dryas cold stage vegetation patterns of central Europe\u2014Climate, soil and relief controls","volume":"41","author":"Theuerkauf","year":"2012","journal-title":"Boreas"},{"key":"ref_40","unstructured":"Hoppe, H. (2021). Crop Type Classification in the Federal State Brandenburg Using Machine Learning Models and Multi-Temporal, Multispectral Sentinel-2 Imagery. [Master\u2019s Thesis, Stralsund University of Applied Sciences, Faculty of Economics]."},{"key":"ref_41","unstructured":"Amt f\u00fcr Statistik Berlin-Brandenburg (2019). Statistischer Bericht C II 7\u2013 j\/18\u2014Besondere Ernte- und Qualit\u00e4tsermittlung im Land Brandenburg 2018, Statistik Berlin Brandenburg."},{"key":"ref_42","first-page":"1","article-title":"SENTINEL-2 SEN2COR: L2A Processor for Users","volume":"Volume SP-740","author":"Ouwehand","year":"2016","journal-title":"Proceedings of the Proceedings Living Planet Symposium 2016"},{"key":"ref_43","unstructured":"Oehmichen, G. (2004). Auf Satellitendaten basierende Ableitungen von Parametern zur Beschreibung Terrestrischer \u00d6kosysteme: Methodische Untersuchung zur Ableitung der Chlorophyll(a+b)- Konzentration und des Blattfl\u00e4chenindexes aus Fernerkundungsdaten. [UFO Dissertation, UFO, Atelier f\u00fcr Gestaltung und Verlag]."},{"key":"ref_44","unstructured":"Witt, H. (1998). Die Spektralen und R\u00e4umlichen Eigenschaften von Fernerkundungssensoren bei der Ableitung von Landoberfl\u00e4chenparametern, Institut f\u00fcr Weltraumsensorik. Institut f\u00fcr Planetenforschung."},{"key":"ref_45","unstructured":"Ministerium f\u00fcr Landwirtschaft, Umwelt und Klimaschutz des Landes Brandenburg (2022, June 06). GEOBROKER der Internetshop der LGB. Available online: https:\/\/geobroker.geobasis-bb.de\/gbss.php?MODE=GetProductInformation&PRODUCTID=996f8fd1-c662-4975-b680-3b611fcb5d1f."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","unstructured":"Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., and Harlan, J.C. (1973, January 27). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor]. Proceedings of the Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor], College Station, TX, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kang, Y., Hu, X., Meng, Q., Zou, Y., Zhang, L., Liu, M., and Zhao, M. (2021). Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data. Remote Sens., 13.","DOI":"10.3390\/rs13224522"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Orynbaikyzy, A., Gessner, U., Mack, B., and Conrad, C. (2020). Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sens., 12.","DOI":"10.3390\/rs12172779"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1016\/j.cj.2022.01.009","article-title":"Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification","volume":"10","author":"Wang","year":"2022","journal-title":"Crop J."},{"key":"ref_55","unstructured":"OpenCV (2022, June 28). OpenCV: Clustering. Available online: https:\/\/docs.opencv.org\/4.x\/d5\/d38\/group__core__cluster.html#ga9a34dc06c6ec9460e90860f15bcd2f88."},{"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","first-page":"1189","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_59","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_60","unstructured":"von der Malsburg, C. (1986). Brain Theory, Springer."},{"key":"ref_61","unstructured":"Anaconda Inc (2020). Anaconda Software Distribution, Anaconda, Inc."},{"key":"ref_62","unstructured":"QGIS Development Team (2009). QGIS Geographic Information System, Open Source Geospatial Foundation."},{"key":"ref_63","unstructured":"Microsoft Corporation Microsoft Excel, Microsoft Corporation."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_67","unstructured":"The Pandas Development Team pandas-dev\/pandas: Pandas 2020."},{"key":"ref_68","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_70","unstructured":"K\u00fchn, D., Auriegel, A., M\u00fcller, H., and Rosskopf, N. (2015). Charakterisierung der B\u00f6den Brandenburgs Hinsichtlich Ihrer Verbreitung, Eigenschaften und Potenziale mit Einer Pr\u00e4sentation Gemittelter Analytischer Untersuchungsergebnisse Einschlie\u00dflich von Hintergrundwerten (Korngr\u00f6\u00dfenzusammensetzung, Bodenphysik, Bodenchemie), Brandenburger Geowissenschaftliche Beitr\u00e4ge."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Proceedings of the Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation, 04, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1080\/10106049.2019.1700556","article-title":"Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery","volume":"36","author":"Saini","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wijesingha, J., Dzene, I., and Wachendorf, M. (2023, January 24\u201328). Spatial-temporal transferability assessment of remote sensing data models for mapping agricultural land use. Proceedings of the Spatial-Temporal Transferability Assessment of Remote Sensing Data Models for Mapping Agricultural Land Use, 04, Vienna, Austria.","DOI":"10.5194\/egusphere-egu23-14716"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Arias, M., Campo-Besc\u00f3s, M.A., and \u00c1lvarez Mozos, J. (2020). Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain. Remote Sens., 12.","DOI":"10.3390\/rs12020278"},{"key":"ref_76","unstructured":"Agency, E.S. (2022, June 06). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:32:54Z","timestamp":1760106774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":76,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091493"],"URL":"https:\/\/doi.org\/10.3390\/rs16091493","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]}}}