{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T02:29:45Z","timestamp":1773973785098,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T00:00:00Z","timestamp":1610668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Norwegian Space Agency","award":["NIT.05.19.5"],"award-info":[{"award-number":["NIT.05.19.5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen\u2019s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.<\/jats:p>","DOI":"10.3390\/rs13020289","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:34:25Z","timestamp":1611113665000},"page":"289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1452-1395","authenticated-orcid":false,"given":"Misganu","family":"Debella-Gilo","sequence":"first","affiliation":[{"name":"Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 \u00c5s, Norway"}]},{"given":"Arnt Kristian","family":"Gjertsen","sequence":"additional","affiliation":[{"name":"Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 \u00c5s, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.ecolind.2018.04.064","article-title":"Agriculture, climate change and sustainability: The case of EU-28","volume":"105","author":"Agovino","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19691","DOI":"10.1073\/pnas.0701890104","article-title":"Adapting agriculture to climate change","volume":"104","author":"Howden","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.jclepro.2019.02.151","article-title":"The impact of farm size on agricultural sustainability","volume":"220","author":"Ren","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1006\/jema.1997.0162","article-title":"Assessing the sustainability of agriculture at the planning stage","volume":"52","author":"Smith","year":"1998","journal-title":"J. Environ. Manag."},{"key":"ref_5","first-page":"3","article-title":"Land registration and cadastre in the Netherlands, and the role of cadastral boundaries: The application of GPS technology in the survey of cadastral boundaries","volume":"5","author":"Wakker","year":"2003","journal-title":"J. Geospat. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pareeth, S., Karimi, P., Shafiei, M., and De Fraiture, C. (2019). Mapping agricultural landuse patterns from time series of Landsat 8 using random forest based hierarchial approach. Remote Sens., 11.","DOI":"10.3390\/rs11050601"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","first-page":"GC51H-0885","article-title":"Developing a Satellite-Based Remote Sensing System for Observing Maize Crop Growth","volume":"2018","author":"Levitan","year":"2018","journal-title":"AGUFM"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","first-page":"231","article-title":"Remote sensing of crop health for food security in Africa: Potentials and constraints","volume":"8","author":"Mutanga","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_11","unstructured":"Gatti, A., and Bertolini, A. (2020, November 25). Sentinel-2 Products Specification Document. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2-Products-Specification-Document."},{"key":"ref_12","unstructured":"Chollet, F. (2018). Deep Learning with Pyton, Manning Publications, Co."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"332","DOI":"10.7763\/IJCTE.2011.V3.328","article-title":"Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers","volume":"3","author":"Panchal","year":"2011","journal-title":"Int. J. Comput. Theory Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a Convolutional Neural Network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_15","unstructured":"Liang, M., and Hu, X. (2015, January 7\u201312). Recurrent Convolutional Neural Network for Object Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kussul, N., Lavreniuk, M., Shelestov, A., and Yailymov, B. (2016, January 10\u201315). Along the Season Crop Classification in Ukraine Based on Time Series of Optical and Sar Images Using Ensemble of Neural Network Classifiers. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730864"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Masjedi, A., Zhao, J., and Crawford, M.M. (2017, January 23\u201328). Prediction of Sorghum Biomass Based on Image Based Features Derived from Time Series of UAV Images. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128413"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_24","unstructured":"Kuang, D. (2019). A 1d convolutional network for leaf and time series classification. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Webb, G.I., and Petitjean, F. (2019). Temporal convolutional neural network for the classification of satellite image time series. Remote Sens., 11.","DOI":"10.3390\/rs11050523"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/JPROC.2009.2035355","article-title":"FORMOSAT-2 Mission: Current Status and Contributions to Earth Observations","volume":"98","author":"Chen","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s40965-017-0038-z","article-title":"Construction of smooth daily remote sensing time series data: A higher spatiotemporal resolution perspective","volume":"2","author":"Pan","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_29","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013). API design for machine learning software: Experiences from the scikit-learn project. arXiv."},{"key":"ref_30","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A System for Large-Scale Machine Learning. Proceedings of the 12th Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_31","unstructured":"Gao, B., and Pavel, L. (2017). On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26","DOI":"10.9781\/ijimai.2016.415","article-title":"Multilayer Perceptron: Architecture Optimization and Training","volume":"4","author":"Ramchoun","year":"2016","journal-title":"IJIMAI"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A., and Asari, V.K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_34","unstructured":"O\u2019Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., and Invernizzi, L. (2020, December 11). Keras Tuner. Available online: https:\/\/github.com\/keras-team\/keras-tuner."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_37","unstructured":"Neyshabur, B., Bhojanapalli, S., McAllester, D., and Srebro, N. (2017, January 4\u20139). Exploring Generalization in Deep Learning. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_39","unstructured":"Alvarez, S.A. (2002). An Exact Analytical Relation among Recall, Precision, and Classification Accuracy in Information Retrieval, Department of Computer Science, Boston College. Technical Report BCCS-02-01."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","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_43","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR and Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","article-title":"The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances","volume":"31","author":"Bagnall","year":"2017","journal-title":"Data Min. Knowl. Discov."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/289\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:30Z","timestamp":1760159490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,15]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020289"],"URL":"https:\/\/doi.org\/10.3390\/rs13020289","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,15]]}}}