{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T08:53:12Z","timestamp":1775206392704,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Foundation Flanders (FWO)","award":["S006421N"],"award-info":[{"award-number":["S006421N"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detailed information on forest tree species is crucial to inform management and policy and support environmental and ecological research. Sentinel-2 imagery is useful for obtaining spatially explicit and frequent information on forest tree species due to its suitable spatial, spectral, and temporal resolutions. However, classification workflows often do not generalise well to time periods that are not seen by the model during the calibration phase. This study investigates the temporal transferability of dominant tree species classification. To this end, the Random Forest, Support Vector Machine, and Multilayer Perceptron algorithms were used to classify five tree species in Flanders (Belgium) with regularly spaced Sentinel-2 time series from 2018 to 2022. Cross-year single-year input scenarios were compared with same-year single-year input scenarios to quantify the temporal transferability of the five evaluated years. This resulted in a decrease in overall accuracy between 2.30 and 14.92 percentage points depending on the algorithm and evaluated year. Moreover, our results indicate that the cross-year classification performance could be improved by using multi-year training data, reducing the drop in overall accuracy. In some cases, gains in overall accuracy were even observed. This study highlights the importance of including interannual spectral variability during the training stage of tree species classification models to improve their ability to generalise in time.<\/jats:p>","DOI":"10.3390\/rs16142653","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T12:20:38Z","timestamp":1721650838000},"page":"2653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series"],"prefix":"10.3390","volume":"16","author":[{"given":"Margot","family":"Verhulst","sequence":"first","affiliation":[{"name":"Division of Forest, Nature and Landscape (FNL), Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5356-1093","authenticated-orcid":false,"given":"Stien","family":"Heremans","sequence":"additional","affiliation":[{"name":"Division of Forest, Nature and Landscape (FNL), Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium"},{"name":"Research Institute for Nature and Forest (INBO), Havenlaan 88, 1000 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-181X","authenticated-orcid":false,"given":"Matthew B.","family":"Blaschko","sequence":"additional","affiliation":[{"name":"Center for Processing Speech and Images (PSI), Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7875-107X","authenticated-orcid":false,"given":"Ben","family":"Somers","sequence":"additional","affiliation":[{"name":"Division of Forest, Nature and Landscape (FNL), Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium"},{"name":"KU Leuven Plant Institute (LPI), KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s10531-017-1453-2","article-title":"Forest Biodiversity, Ecosystem Functioning and the Provision of Ecosystem Services","volume":"26","author":"Brockerhoff","year":"2017","journal-title":"Biodivers. Conserv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1038\/ncomms2328","article-title":"Higher Levels of Multiple Ecosystem Services Are Found in Forests with More Tree Species","volume":"4","author":"Gamfeldt","year":"2013","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wohlgemuth, T., Jentsch, A., and Seidl, R. (2022). Disturbance Ecology, Springer.","DOI":"10.1007\/978-3-030-98756-5"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Boisvenue, C., and White, J. (2019). Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science. Remote Sens., 11.","DOI":"10.3390\/rs11040463"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.geodrs.2015.01.001","article-title":"The Importance of Tree Species and Soil Taxonomy to Modeling Forest Soil Carbon Stocks in Canada","volume":"4","author":"Shaw","year":"2015","journal-title":"Geoderma Reg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of Studies on Tree Species Classification from Remotely Sensed Data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9812624","DOI":"10.34133\/2021\/9812624","article-title":"Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective","volume":"2021","author":"Pu","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8060445"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2012.2216272","article-title":"Tree Species Classification in Boreal Forests with Hyperspectral Data","volume":"51","author":"Dalponte","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Micha\u0142owska, M., and Rapi\u0144ski, J. (2021). A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers. Remote Sens., 13.","DOI":"10.3390\/rs13030353"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_13","first-page":"1","article-title":"Use of Remote Sensing for Mapping of Non-Native Conifer Species","volume":"33","author":"Hauglin","year":"2016","journal-title":"Ina Fagrapp."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2011.08.028","article-title":"The European Earth Monitoring (GMES) Programme: Status and Perspectives","volume":"120","author":"Aschbacher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1109\/JSTARS.2014.2329390","article-title":"Comparison of Feature Reduction Algorithms for Classifying Tree Species with Hyperspectral Data on Three Central European Test Sites","volume":"7","author":"Fassnacht","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree Species Classification in the Southern Alps Based on the Fusion of Very High Geometrical Resolution Multispectral\/Hyperspectral Images and LiDAR Data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2009.2032239","article-title":"Simulated Multispectral Imagery for Tree Species Classification Using Support Vector Machines","volume":"48","author":"Heikkinen","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"172","DOI":"10.25518\/1780-4507.16524","article-title":"Forest Mapping and Species Composition Using Supervised per Pixel Classification of Sentinel-2 Imagery","volume":"22","author":"Bolyn","year":"2018","journal-title":"BASE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1139\/cjfr-2020-0170","article-title":"National Mapping and Estimation of Forest Area by Dominant Tree Species Using Sentinel-2 Data","volume":"51","author":"Breidenbach","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112103","DOI":"10.1016\/j.rse.2020.112103","article-title":"Evaluation of Machine Learning Algorithms for Forest Stand Species Mapping Using Sentinel-2 Imagery and Environmental Data in the Polish Carpathians","volume":"251","author":"Grabska","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112743","DOI":"10.1016\/j.rse.2021.112743","article-title":"Mapping Temperate Forest Tree Species Using Dense Sentinel-2 Time Series","volume":"267","author":"Hemmerling","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Neuwirth, M., B\u00f6ck, S., Brenner, H., Vuolo, F., and Atzberger, C. (2019). Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11222599"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"469","DOI":"10.5194\/isprs-annals-V-3-2020-469-2020","article-title":"Optimal dates for deciduous tree species mapping using full years Sentinel-2 time series in south west France","volume":"3","author":"Karasiak","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree Species Classification with Multi-Temporal Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_30","first-page":"102208","article-title":"Exploring the Potential of Land Surface Phenology and Seasonal Cloud Free Composites of One Year of Sentinel-2 Imagery for Tree Species Mapping in a Mountainous Region","volume":"94","author":"Kollert","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., and Kycko, M. (2021). Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkono\u0161e\/Karkonosze Transboundary Biosphere Reserve. Remote Sens., 13.","DOI":"10.3390\/rs13132581"},{"key":"ref_32","first-page":"267","article-title":"Sentinel-2 Time Series: A Promising Tool in Monitoring Temperate Species Spring Phenology","volume":"97","year":"2023","journal-title":"For. An Int. J. For. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1654-109X.2009.01053.x","article-title":"Mapping Tree Species in Temperate Deciduous Woodland Using Time-Series Multi-Spectral Data","volume":"13","author":"Hill","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sheeren, D., Fauvel, M., Josipov\u00edc, V., Lopes, M., Planque, C., Willm, J., and Dejoux, J.F. (2016). Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8090734"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gray, P.C., Chamorro, D.F., Ridge, J.T., Kerner, H.R., Ury, E.A., and Johnston, D.W. (2021). Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time. Remote Sens., 13.","DOI":"10.3390\/rs13193953"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.isprsjprs.2024.05.020","article-title":"Evaluating the Spatial\u2013Temporal Transferability of Models for Agricultural Land Cover Mapping Using Landsat Archive","volume":"213","author":"Wijesingha","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kyere, I., Astor, T., Gra\u00df, R., and Wachendorf, M. (2019). Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data. Agronomy, 9.","DOI":"10.3390\/agronomy9060309"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Momm, H.G., ElKadiri, R., and Porter, W. (2020). Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach. Remote Sens., 12.","DOI":"10.3390\/rs12030449"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jin, S., Su, Y., Gao, S., Hu, T., Liu, J., and Guo, Q. (2018). The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data. Remote Sens., 10.","DOI":"10.3390\/rs10081183"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Domingo, D., Alonso, R., Lamelas, M.T., Montealegre, A.L., Rodr\u00edguez, F., and de la Riva, J. (2019). Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data. Remote Sens., 11.","DOI":"10.3390\/rs11030261"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1139\/cjfr-2014-0405","article-title":"Temporal Transferability of LiDAR-Based Imputation of Forest Inventory Attributes","volume":"45","author":"Fekety","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_42","first-page":"10272","article-title":"Reuse of Field Data in Als-Assisted Forest Inventory","volume":"54","author":"Gobakken","year":"2020","journal-title":"Silva Fenn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","article-title":"Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances","volume":"4","author":"Tuia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3354\/cr032253","article-title":"Responses of Leaf Colouring in Four Deciduous Tree Species to Climate and Weather in Germany","volume":"32","author":"Estrella","year":"2006","journal-title":"Clim. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.agrformet.2017.12.259","article-title":"Predicting Autumn Phenology: How Deciduous Tree Species Respond to Weather Stressors","volume":"250\u2013251","author":"Xie","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108485","DOI":"10.1016\/j.agrformet.2021.108485","article-title":"Phenological Shifts Induced by Climate Change Amplify Drought for Broad-Leaved Trees at Low Elevations in Switzerland","volume":"307","author":"Meier","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Duan, S., He, H.S., and Spetich, M. (2018). Effects of Growing-Season Drought on Phenology and Productivity in Thewest Region of Central Hardwood Forests, USA. Forests, 9.","DOI":"10.3390\/f9070377"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"13585","DOI":"10.1073\/pnas.1509991112","article-title":"Deciduous Forest Responses to Temperature, Precipitation, and Drought Imply Complex Climate Change Impacts","volume":"112","author":"Xie","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_49","unstructured":"Govaere, L., and Leyman, A. (2023, December 08). Vlaamse Bosinventarisatie Agentschap Natuur En Bos (VBI1: 1997-1999; VBI2: 2009\u20132018; VBI3: 2019\u20132021). Available online: https:\/\/www.natuurenbos.be\/vlaamse-bosinventaris\/Website_BosAreaal.html."},{"key":"ref_50","unstructured":"Forest Europe (2020). State of Europe\u2019s Forests 2020, Forest Europe."},{"key":"ref_51","unstructured":"Schneiders, A., Alaerts, K., Michels, H., Stevens, M., Van Gossum, P., Van Reeth, W., and Vught, I. (2020). Natuurrapport 2020: Feiten En Cijfers Voor Een Nieuw Biodiversiteitsbeleid, Research Institute Nature and Forest."},{"key":"ref_52","unstructured":"Vandekerkhove, K. (2013). Integration of Nature Protection in Forest Policy in Flanders (Belgium), European Forest Institute."},{"key":"ref_53","first-page":"1","article-title":"Een Blik Op de Kenmerken van Bos in Vlaanderen\u2013Eerste Resultaten van Twee Opeenvolgende Vlaamse Bosinventarisaties","volume":"83","author":"Govaere","year":"2020","journal-title":"Bosrevue"},{"key":"ref_54","unstructured":"Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., and Fritz, S. ESA WorldCover 10 m 2021 V200 2022, Zenodo."},{"key":"ref_55","unstructured":"(2023, November 13). ANF Bosinventaris. Available online: https:\/\/www.natuurenbos.be\/beleid-wetgeving\/natuurbeheer\/bosinventaris."},{"key":"ref_56","unstructured":"Govaere, L., Van de Kerckhove, P., Roelandt, B., Sannen, P., and Schrey, L. (2009). Handleiding Tweede Bosinventarisatie Vlaams Gewest, Agency for Nature and Forests."},{"key":"ref_57","unstructured":"Govaere, L. (2019). Protocol En Handleiding Derde Bosinventarisatie Vlaams Gewest, Agency for Nature and Forests."},{"key":"ref_58","unstructured":"Dumortier, M., Van Gossum, P., Van Calster, H., Adriaens, D., Adriaenssens, V., Alaerts, K., Brys, R., Cools, N., De Knijf, G., and Denys, L. (2022). Voorstel Voor Een Meetnet Biodiversiteit Agrarisch Gebied, Research Institute Nature and Forest. Nr. INBO.A.4387; Adviezen van Het Instituut Voor Natuur-En Bosonderzoek."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Schramm, M., Pebesma, E., Milenkovi\u0107, M., Foresta, L., Dries, J., Jacob, A., Wagner, W., Mohr, M., Neteler, M., and Kadunc, M. (2021). The Openeo Api\u2013Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities. Remote Sens., 13.","DOI":"10.3390\/rs13061125"},{"key":"ref_60","unstructured":"Dries, J., and Lippens, S. (2024, July 15). openeo-python-client (Version 0.22.0). Available online: https:\/\/github.com\/Open-EO\/openeo-python-client."},{"key":"ref_61","unstructured":"(2023, September 14). Terrascope Terrascope. Available online: https:\/\/terrascope.be\/en."},{"key":"ref_62","unstructured":"Swinnen, E., and De Keukelaere, L. (2020). Terrascope Sentinel-2-Quality Assessment Report, Flemish Institute for Technological Research (VITO)."},{"key":"ref_63","unstructured":"Richter, R., Louis, J., and M\u00fcller-Wilm, U. (2012). Sentinel-2 MSI\u2013Level 2A Products Algorithm Theoretical Basis Document, Telespazio VEGA Deutschland GmbH. S2PAD-ATBD-0001, Issue 2.0."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"113276","DOI":"10.1016\/j.rse.2022.113276","article-title":"Mapping the Presence and Distribution of Tree Species in Canada\u2019s Forested Ecosystems","volume":"282","author":"Hermosilla","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Stas, M., Van Orshoven, J., Dong, Q., Heremans, S., and Zhang, B. (2016, January 18\u201320). A Comparison of Machine Learning Algorithms for Regional Wheat Yield Prediction Using NDVI Time Series of SPOT-VGT. Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577625"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_68","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_69","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_70","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/IJCNN.2017.7966039","article-title":"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline","volume":"Volume 2017-May","author":"Wang","year":"2017","journal-title":"Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN)"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep Learning Based Multi-Temporal Crop Classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Akbani, R., Kwek, S., and Japkowicz, N. (2004). Applying Support Vector Machines to Imbalanced Datasets. European Conference on Machine Learning, Springer.","DOI":"10.1007\/978-3-540-30115-8_7"},{"key":"ref_73","unstructured":"Chen, C., Liaw, A., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, Department of Statistics, University of California."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","unstructured":"Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., and Li, X. (2023). A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition. Remote Sens., 15.","DOI":"10.3390\/rs15030827"},{"key":"ref_76","unstructured":"Yang, S., Xiao, W., Zhang, M., Guo, S., Zhao, J., and Shen, F. (2022). Image Data Augmentation for Deep Learning: A Survey. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Iwana, B.K., and Uchida, S. (2021). An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254841"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"10123","DOI":"10.1007\/s00521-023-08459-3","article-title":"Data Augmentation Techniques in Time Series Domain: A Survey and Taxonomy","volume":"35","author":"Iglesias","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_79","unstructured":"Westra, T., Verschelde, P., Van Calster, H., Lommelen, E., Onkelinx, T., Quataert, P., and Govaere, L. (2015). Opmaak van Een Analysestramien Voor de Gegevens van de Vlaamse Bosinventarisatie. Rapporten van het Instituu voor Natuur- en Bosonderzoek 2015, Research Institute for Nature and Forest."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2653\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:20:10Z","timestamp":1760109610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,20]]},"references-count":79,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142653"],"URL":"https:\/\/doi.org\/10.3390\/rs16142653","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,20]]}}}