{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:49:19Z","timestamp":1777603759268,"version":"3.51.4"},"reference-count":72,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images\u2019 time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.<\/jats:p>","DOI":"10.3390\/rs11222673","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T11:24:32Z","timestamp":1573817072000},"page":"2673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series\u2014A Case Study in Zhanjiang, China"],"prefix":"10.3390","volume":"11","author":[{"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China"},{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China"},{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-0412","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China"},{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China"},{"name":"Guangzhou Institute of Geography, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-3943","authenticated-orcid":false,"given":"Wenlong","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China"},{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China"},{"name":"Guangzhou Institute of Geography, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China"},{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Tibetan Plateau Research, CAS, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine","volume":"40","author":"Kolotii","year":"2015","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1615\/JAutomatInfScien.v45.i6.70","article-title":"Winter Wheat Yield Forecasting: A Comparative Analysis of Results of Regression and Biophysical Models","volume":"45","author":"Kussul","year":"2013","journal-title":"J. Autom. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"252","article-title":"Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2","volume":"28","author":"McNairn","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/JSTARS.2013.2238507","article-title":"Data Mining, A Promising Tool for Large-Area Cropland Mapping","volume":"6","author":"Vintrou","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2018.10.008","article-title":"Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST\u2013PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10888","DOI":"10.3390\/rs61110888","article-title":"The potential and uptake of remote sensing in insurance: A review","volume":"6","author":"Vrieling","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"026019","DOI":"10.1117\/1.JRS.12.026019","article-title":"Crop classification from Sentinel-2-derived vegetation indices using ensemble learning","volume":"12","author":"Sonobe","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., R\u00fcdiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study. Remote Sens., 10.","DOI":"10.3390\/rs10091396"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1080\/01431161.2015.1070319","article-title":"A unified framework for crop classification in southern China using fully polarimetric, dual polarimetric, and compact polarimetric SAR data","volume":"36","author":"Xie","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, J.S., and Pottier, E. (2016). Polarimetric Radar Imaging: Basics to Applications, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420054989"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1063\/1.3502550","article-title":"Polarisation: Applications in Remote Sensing","volume":"63","author":"Cloude","year":"2010","journal-title":"Phys. Today"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3390\/s150100769","article-title":"Application of remote sensors in mapping rice area and forecasting its production: A review","volume":"15","author":"Mosleh","year":"2015","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, D., Jing, W., Xu, J., Huang, J., Yang, J., and Chen, S. (2019). Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A\/2 Time Series Data: A Case Study in Zhanjiang City, China. Remote Sens., 11.","DOI":"10.3390\/rs11070861"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(01)00296-6","article-title":"A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery","volume":"80","author":"Rogan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote sensing imagery in vegetation mapping: A review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Potin, P., Rosich, B., Grimont, P., Miranda, N., Shurmer, I., O\u2019Connell, A., Torres, R., and Krassenburg, M. (2016, January 6\u20139). Sentinel-1 mission status. Proceedings of the EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany.","DOI":"10.1109\/IGARSS.2015.7326401"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1080\/01431161.2017.1395969","article-title":"Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data","volume":"39","author":"Onojeghuo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.1117\/12.2325160"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"\u00dcnsalan, C., and Boyer, K.L. (2011). Review on Land Use Classification, Springer.","DOI":"10.1007\/978-0-85729-667-2_5"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Review","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_24","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). IEEE, Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: What it does and how to do it","volume":"78","author":"Werbos","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_26","unstructured":"Giles, C.L., Miller, C.B., Chen, D., Sun, G.-Z., Chen, H.-H., and Lee, Y.-C. (December, January 30). Extracting and learning an unknown grammar with recurrent neural networks. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/69.842255","article-title":"Natural language grammatical inference with recurrent neural networks","volume":"12","author":"Lawrence","year":"2000","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Comput. Sci.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2017, January 22\u201325). Temporal Vegetation Modelling using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.193"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018). Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040129"},{"key":"ref_33","unstructured":"Castro, J., Achanccaray Diaz, P., Sanches, I., Cue La Rosa, L., Nigri Happ, P., and Feitosa, R. (2017). Evaluation of Recurrent Neural Networks for Crop Recognition from Multitemporal Remote Sensing Images. Anais do XXVII Congresso Brasileiro de Cartografia, SBC."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014, January 14\u201318). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_36","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_37","first-page":"36","article-title":"Spatiotemporal Characteristics of Seasonal Meteorological Drought in Leizhou Peninsula during 1984\u20132013","volume":"37","author":"Ren","year":"2017","journal-title":"J. China Hydrol."},{"key":"ref_38","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_39","unstructured":"(2019, November 15). Available online: http:\/\/www.alz.org\/what-is-dementia.asp."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TPAMI.1982.4767223","article-title":"A model for radar images and its application to adaptive digital filtering of multiplicative noise","volume":"4","author":"Frost","year":"1982","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/S0893-6080(03)00104-7","article-title":"Extension neural network and its applications","volume":"16","author":"Wang","year":"2003","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, T., Yu, H., and Wilamowski, B. (2011, January 27\u201330). Comparison between traditional neural networks and radial basis function networks. Proceedings of the 2011 IEEE International Symposium on Industrial Electronics, Gdansk, Poland.","DOI":"10.1109\/ISIE.2011.5984328"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/0045-7949(94)00377-F","article-title":"Multilayer perceptron in damage detection of bridge structures","volume":"54","author":"Pandey","year":"1995","journal-title":"Comput. Struct."},{"key":"ref_44","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Bottou, L., Orr, G.B., and Muller, K.R. (2012). Efficient BackProp. Neural Networks: Tricks of the Trade, This Book Is an Outgrowth of A Nips Workshop, Springer.","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hu, W., Huang, Y., Wei, L., Zhang, F., and Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. J. Sens.","DOI":"10.1155\/2015\/258619"},{"key":"ref_47","unstructured":"Bakker, B. (2002, January 9\u201314). Reinforcement learning with long short-term memory. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_48","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","first-page":"574","article-title":"Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data","volume":"73","author":"Clauss","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","first-page":"587","article-title":"Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines","volume":"33","author":"Son","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_53","unstructured":"Zhang, Z., and Sabuncu, M. (2018, January 3\u20138). Generalized cross entropy loss for training deep neural networks with noisy labels. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","article-title":"Multivariate lstm-fcns for time series classification","volume":"116","author":"Karim","year":"2019","journal-title":"Neural Netw."},{"key":"ref_56","unstructured":"Hatami, N., Gavet, Y., Debayle, J., Hatami, N., Gavet, Y., and Debayle, J. (2017, January 13\u201315). Classification of Time-Series Images Using Deep Convolutional Neural Networks. Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria."},{"key":"ref_57","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X.L., and Krishnaswamy, S. (2015, January 25\u201331). Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_58","unstructured":"Borovykh, A., Bohte, S., and Oosterlee, C.W. (2017). Conditional Time Series Forecasting with Convolutional Neural Networks. arXiv."},{"key":"ref_59","unstructured":"Cui, Z., Chen, W., and Chen, Y. (2016). Multi-scale convolutional neural networks for time series classification. arXiv."},{"key":"ref_60","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the Neural Information Processing Systems Conference, Montreal, QC, Canada."},{"key":"ref_61","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., and Zhao, J.L. (2014, January 16\u201318). Time series classification using multi-channels deep convolutional neural networks. Proceedings of the International Conference on Web-Age Information Management, Macau, China.","DOI":"10.1007\/978-3-319-08010-9_33"},{"key":"ref_63","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_64","unstructured":"Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015, January 6\u201311). An empirical exploration of recurrent network architectures. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compag.2015.05.001","article-title":"Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data","volume":"115","author":"Tatsumi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Goldstein, B.A., Polley, E.C., and Briggs, F.B. (2011). Random forests for genetic association studies. Stat. Appl. Genet. Mol. Biol., 10.","DOI":"10.2202\/1544-6115.1691"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_68","first-page":"223","article-title":"A coefficient of agreement as a measure of thematic classification accuracy","volume":"52","author":"Rosenfield","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_69","unstructured":"Banko, G. (1998). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods including Remote Sensing Data in Forest Inventory, IIASA."},{"key":"ref_70","first-page":"1","article-title":"The truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Garnot, V.S.F., Landrieu, L., Giordano, S., and Chehata, N. (2019). Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series. arXiv.","DOI":"10.1109\/IGARSS.2019.8900517"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2673\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:34:47Z","timestamp":1760189687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,15]]},"references-count":72,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222673"],"URL":"https:\/\/doi.org\/10.3390\/rs11222673","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,15]]}}}