{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T13:20:09Z","timestamp":1773580809095,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Integrated Infrastructure Operational Programme funded by the ERDF","award":["ITMS2014+ 313011W580"],"award-info":[{"award-number":["ITMS2014+ 313011W580"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adopting a domain generalization approach, e.g., by training models on multiple seasons to improve generalization to new, unseen target years. We utilize a comprehensive five-year Sentinel-2 dataset over different agricultural regions in Slovakia and a diverse crop scheme (eight crop classes). We evaluate the performance of different machine learning classification algorithms, including random forests, support vector machines, quadratic discriminant analysis, and neural networks. Our main findings reveal that the transferability of models across years differs between regions, with the Danubian lowlands demonstrating better performance (overall accuracies ranging from 91.5% in 2022 to 94.3% in 2020) compared to eastern Slovakia (overall accuracies ranging from 85% in 2022 to 91.9% in 2020). Quadratic discriminant analysis, support vector machines, and neural networks consistently demonstrated high performance across diverse transferability scenarios. The random forest algorithm was less reliable in generalizing across different scenarios, particularly when there was a significant deviation in the distribution of unseen domains. This finding underscores the importance of employing a multi-classifier analysis. Rapeseed, grasslands, and sugar beet consistently show stable transferability across seasons. We observe that all periods play a crucial role in the classification process, with July being the most important and August the least important. Acceptable performance can be achieved as early as June, with only slight improvements towards the end of the season. Finally, employing a multi-classifier approach allows for parcel-level confidence determination, enhancing the reliability of crop distribution maps by assuming higher confidence when multiple classifiers yield similar results. To enhance spatiotemporal generalization, our study proposes a two-step approach: (1) determine the optimal spatial domain to accurately represent crop type distribution; and (2) apply interannual training to capture variability across years. This approach helps account for various factors, such as different crop rotation practices, diverse observational quality, and local climate-driven patterns, leading to more accurate and reliable crop classification models for nationwide agricultural monitoring.<\/jats:p>","DOI":"10.3390\/rs15133414","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:41:27Z","timestamp":1688604087000},"page":"3414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3532-633X","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Rus\u0148\u00e1k","sequence":"first","affiliation":[{"name":"Institute of Landscape Ecology, Slovak Academy of Sciences, v.v.i, \u0160tef\u00e1nikova 3, 814 99 Bratislava, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-6537","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Kasanick\u00fd","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, v.v.i, D\u00fabravsk\u00e1 Cesta 9, 845 07 Bratislava, Slovakia"}]},{"given":"Peter","family":"Mal\u00edk","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, v.v.i, D\u00fabravsk\u00e1 Cesta 9, 845 07 Bratislava, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-2271","authenticated-orcid":false,"given":"J\u00e1n","family":"Moj\u017ei\u0161","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, v.v.i, D\u00fabravsk\u00e1 Cesta 9, 845 07 Bratislava, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5852-0429","authenticated-orcid":false,"given":"J\u00e1n","family":"Zelenka","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, v.v.i, D\u00fabravsk\u00e1 Cesta 9, 845 07 Bratislava, Slovakia"}]},{"given":"Michal","family":"Svi\u010dek","sequence":"additional","affiliation":[{"name":"National Agricultural and Food Center (NPPC), Hlohoveck\u00e1 2, 951 41 Lu\u017eianky, Slovakia"}]},{"given":"Dominik","family":"Abrah\u00e1m","sequence":"additional","affiliation":[{"name":"National Agricultural and Food Center (NPPC), Hlohoveck\u00e1 2, 951 41 Lu\u017eianky, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9055-8563","authenticated-orcid":false,"given":"Andrej","family":"Halabuk","sequence":"additional","affiliation":[{"name":"Institute of Landscape Ecology, Slovak Academy of Sciences, v.v.i, \u0160tef\u00e1nikova 3, 814 99 Bratislava, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,5]]},"reference":[{"key":"ref_1","first-page":"122","article-title":"How Much Does Multi-Temporal Sentinel-2 Data Improve Crop Type Classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","article-title":"Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data","volume":"4","author":"Tuia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","first-page":"102264","article-title":"Phenology-Based Sample Generation for Supervised Crop Type Classification","volume":"95","author":"Belgiu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1109\/JSTARS.2023.3237500","article-title":"Towards Scalable within-Season Crop Mapping with Phenology Normalization and Deep Learning","volume":"16","author":"Yang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112994","DOI":"10.1016\/j.rse.2022.112994","article-title":"Early- and in-Season Crop Type Mapping without Current-Year Ground Truth: Generating Labels from Historical Information via a Topology-Based Approach","volume":"274","author":"Lin","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-Term Land Cover Dynamics by Multi-Temporal Classification across the Landsat-5 Record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.08.023","article-title":"Efficient Corn and Soybean Mapping with Temporal Extendability: A Multi-Year Experiment Using Landsat Imagery","volume":"140","author":"Zhong","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qin, R., and Liu, T. (2022). A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images\u2014Analysis Unit, Model Scalability and Transferability. Remote Sens., 14.","DOI":"10.3390\/rs14030646"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zeng, H., Tian, F., Zhang, M., Wu, B., Gilliams, S., Li, S., Li, Y., Lu, Y., and Yang, H. (2022). An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China. Remote Sens., 14.","DOI":"10.3390\/rs14051208"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining Earliest Identifiable Timing of Crops Using All Available Sentinel 1\/2 Imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112576","DOI":"10.1016\/j.rse.2021.112576","article-title":"Pre- and within-Season Crop Type Classification Trained with Archival Land Cover Information","volume":"264","author":"Johnson","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A High-Performance and in-Season Classification System of Field-Level Crop Types Using Time-Series Landsat Data and a Machine Learning Approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","unstructured":"Lapin, M., Faako, P., Melo, M., Stastny, P., and Tomlain, J. (2002). Climatic Regions; 1:1,000,000; 27. Klimaticke Oblasti; 1:1,000,000, Ministry of Environment of the Slovak Republic Bratislava."},{"key":"ref_16","first-page":"242","article-title":"Characteristics of Agricultural Soils in the East-Slovak Lowland and the Possibilities of Improving of Their Productive Potential","volume":"47","author":"Michaeli","year":"2013","journal-title":"Zivotn. Prostr."},{"key":"ref_17","unstructured":"Mikl\u00f3s, L., and Izakovi\u010dov\u00e1, Z. (2006). Atlas of Representative Geoecosystems of Slovakia, Slovak Academy of Sciences, Ministry of Environment and Ministry of Education of the Slovak Republik."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","first-page":"2879","article-title":"Convergence Rates of Efficient Global Optimization Algorithms","volume":"12","author":"Bull","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","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_25","first-page":"1","article-title":"All Models Are Wrong, but Many Are Useful: Learning a Variable\u2019s Importance by Studying an Entire Class of Prediction Models Simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"963","DOI":"10.14358\/PERS.70.8.963","article-title":"Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach","volume":"70","author":"Liu","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1109\/JSTARS.2022.3164771","article-title":"A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation with Deep Learning","volume":"15","author":"Sykas","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1109\/JSTARS.2021.3073965","article-title":"TimeSen2Crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop-Type Classification","volume":"14","author":"Weikmann","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal Composite Landsat TM\/ETM+ Images Using the Medoid (a Multi-Dimensional Median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2017.01.002","article-title":"Phenology-Adaptive Pixel-Based Compositing Using Optical Earth Observation Imagery","volume":"190","author":"Frantz","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"110810","DOI":"10.1016\/j.rse.2018.06.038","article-title":"Robust Landsat-Based Crop Time Series Modelling","volume":"238","author":"Roy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1080\/17538947.2022.2036832","article-title":"Crop Classification Based on the Spectrotemporal Signature Derived from Vegetation Indices and Accumulated Temperature","volume":"15","author":"Zhang","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wei, M., Wang, H., Zhang, Y., Li, Q., Du, X., Shi, G., and Ren, Y. (2023). Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles. Remote Sens., 15.","DOI":"10.3390\/rs15030853"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yi, Z., Jia, L., and Chen, Q. (2020). Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-20926"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Saunier, S., Pflug, B., Lobos, I.M., Franch, B., Louis, J., De Los Reyes, R., Debaecker, V., Cadau, E.G., Boccia, V., and Gascon, F. (2022). Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data. Remote Sens., 14.","DOI":"10.3390\/rs14163855"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-Annual Reflectance Composites from Sentinel-2 and Landsat for National-Scale Crop and Land Cover Mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/22797254.2018.1454265","article-title":"Crop Inventory at Regional Scale in Ukraine: Developing in Season and End of Season Crop Maps with Multi-Temporal Optical and SAR Satellite Imagery","volume":"51","author":"Kussul","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"761148","DOI":"10.3389\/fpls.2021.761148","article-title":"Cross-Year Reuse of Historical Samples for Crop Mapping Based on Environmental Similarity","volume":"12","author":"Liu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., Pelletier, C., Zollner, M., Lef\u00e8vre, S., and K\u00f6rner, M. (2020). BreizhCrops: A Time Series Dataset for Crop Type Mapping. arXiv.","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1545-2020"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Aghababaei, M., Ebrahimi, A., Naghipour, A.A., Asadi, E., P\u00e9rez-Suay, A., Morata, M., Garcia, J.L., Rivera Caicedo, J.P., and Verrelst, J. (2022). Introducing ARTMO\u2019s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape. Remote Sens., 14.","DOI":"10.3390\/rs14184452"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"933","DOI":"10.3390\/s8020933","article-title":"Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data","volume":"8","author":"Tang","year":"2008","journal-title":"Sensors"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-Based Crop Identification Using Multiple Vegetation Indices, Textural Features and Crop Phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gilcher, M., and Udelhoven, T. (2021). Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms\u2014A Randomized Approach to Compare Pixel Based and Convolution Based Methods. Remote Sens., 13.","DOI":"10.3390\/rs13040775"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/JSTARS.2020.3036602","article-title":"Self-Supervised Pretraining of Transformers for Satellite Image Time Series Classification","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","first-page":"102313","article-title":"Crop Type Mapping by Using Transfer Learning","volume":"98","author":"Nowakowski","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Krichen, M., Mihoub, A., Alzahrani, M.Y., Adoni, W.Y.H., and Nahhal, T. (2022, January 9\u201311). Are Formal Methods Applicable to Machine Learning and Artificial Intelligence?. Proceedings of the 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMARTTECH54121.2022.00025"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/inst.12434","article-title":"Framework for Formal Verification of Machine Learning Based Complex System-of-Systems","volume":"26","author":"Raman","year":"2023","journal-title":"Insight"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:06:55Z","timestamp":1760126815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,5]]},"references-count":49,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133414"],"URL":"https:\/\/doi.org\/10.3390\/rs15133414","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,5]]}}}