{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T23:36:03Z","timestamp":1775000163423,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Monitoring and Forecasting of Crop Growth and Productivity Based on Satellite Remote Sensing Data","award":["2016YFD0300603"],"award-info":[{"award-number":["2016YFD0300603"]}]},{"name":"the National Natural Science Foundation of China","award":["41921001"],"award-info":[{"award-number":["41921001"]}]},{"name":"the Fundamental Research Funds for Central Non-profit Scientific Institution","award":["1610132021021"],"award-info":[{"award-number":["1610132021021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always needed, which will increase the workload and the risk of error transmission, especially for imagery with high spatial resolution. The development of optical imagery with high temporal and spatial resolution and the emergence of deep learning algorithms provide solutions to this problem. Although the one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) models have been used to classify crop types in previous studies, their ability to identify crop types using optical TSD with missing information needs to be further explored due to their different mechanisms for handling invalid values in TSD. In this research, we designed two groups of experiments to explore the performances and characteristics of the 1D CNN, LSTM, GRU, LSTM-CNN, and GRU-CNN models for crop type mapping using unfilled Sentinel-2 (Sentinel-2) TSD and to discover the differences between unfilled and filled Sentinel-2 TSD based on the same algorithm. A case study was conducted in Hengshui City, China, of which 70.3% is farmland. The results showed that the 1D CNN, LSTM-CNN, and GRU-CNN models achieved acceptable classification accuracies (above 85%) using unfilled TSD, even though the total missing rate of the sample values was 43.5%; these accuracies were higher and more stable than those obtained using filled TSD. Furthermore, the models recalled more samples on crop types with small parcels when using unfilled TSD. Although LSTM and GRU models did not attain accuracies as high as the other three models using unfilled TSD, their results were almost close to those with filled TSD. This research showed that crop types could be identified by deep learning features in Sentinel-2 dense time series images with missing information due to clouds or cloud shadows randomly, which avoided spending a lot of time on missing information reconstruction.<\/jats:p>","DOI":"10.3390\/rs13142790","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T23:28:30Z","timestamp":1626391710000},"page":"2790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural, Institute of Agricultural Resources and Regional Planning Remote Sensing, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"given":"Sibo","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural, Institute of Agricultural Resources and Regional Planning Remote Sensing, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural, Institute of Agricultural Resources and Regional Planning Remote Sensing, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-8909","authenticated-orcid":false,"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural, Institute of Agricultural Resources and Regional Planning Remote Sensing, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8022-9977","authenticated-orcid":false,"given":"Louis","family":"Reymondin","sequence":"additional","affiliation":[{"name":"International Center for Tropical Agriculture (CIAT), Hanoi 100000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","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 bio-physical models","volume":"45","author":"Kogan","year":"2013","journal-title":"J. Autom. Inf. Sci."},{"key":"ref_2","first-page":"39","article-title":"Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine","volume":"XL-7\/W3","author":"Kolotii","year":"2015","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250m vegetation index data for crop classification in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"111946","DOI":"10.1016\/j.rse.2020.111946","article-title":"Deep Crop Mapping: A multi-temporal deep learning approach with improved spa-tial generalizability for dynamic corn and soybean mapping","volume":"247","author":"Xu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1109\/TGRS.2017.2670021","article-title":"An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images With Missing Data","volume":"55","author":"Ng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cageo.2017.04.007","article-title":"Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data","volume":"105","author":"Sun","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_10","first-page":"102319","article-title":"A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics","volume":"99","author":"Tang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"195","article-title":"Crop Type Classification Using Vegetation Indices of RapidEye Imagery","volume":"XL-7","author":"Ustuner","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gloplacha.2014.03.009","article-title":"Do aerosols impact ground observation of total cloud cover over the North China Plain?","volume":"117","author":"Sun","year":"2014","journal-title":"Glob. Planet. Chang."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Werbos, P. (1990). Backpropagation through Time: What It Does and How to Do It, Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/5.58337"},{"key":"ref_18","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_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":"Li, F., Li, G., Hwang, S., Yao, B., and Zhang, Z. (2014). Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks, Springer. Web-Age Information Management.","DOI":"10.1007\/978-3-319-08010-9"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder\u2013Decoder Approaches, Association for Computational Linguistics (ACL).","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_24","unstructured":"Parveen, S., and Green, P. (2001, January 3\u20138). Speech Recognition with Missing Data using Recurrent Neural Nets. Proceedings of the Neural In-formation Processing Systems, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.neucom.2018.08.067","article-title":"LSTM-based traffic flow prediction with missing data","volume":"318","author":"Tian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_26","first-page":"1508","article-title":"CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data","volume":"14","author":"Cao","year":"2018","journal-title":"J. Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M., and Burgard, W. (2015). Multimodal deep learning for robust RGB-D object recognition. 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2015.7353446"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Khaliq, A., and Chiaberge, M. (2019). Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Appl. Sci., 10.","DOI":"10.3390\/app10010238"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2017, January 21\u201326). 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.193"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neunet.2018.05.019","article-title":"Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks","volume":"105","author":"Sharma","year":"2018","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1007\/s12665-015-4225-x","article-title":"Occurrence and formation of high fluoride groundwater in the Hengshui area of the North China Plain","volume":"74","author":"Liu","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","unstructured":"Dai, Z., and Heckel, R. (2020). Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_38","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. Neural Evol. Comput."},{"key":"ref_39","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"37","author":"Ioffe","year":"2015","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_40","unstructured":"Boureau, Y.L., Ponce, J., and LeCun, Y. (2010, January 21\u201324). A Theoretical Analysis of Feature Pooling in Visual Recognition. Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel."},{"key":"ref_41","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Zeiler, M.D., and Fergus, R. (2013). Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. Learning."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.5194\/bg-10-4055-2013","article-title":"A comparison of methods for smoothing and gap filling time series of remote sensing observations\u2014Application to MODIS LAI products","volume":"10","author":"Kandasamy","year":"2013","journal-title":"Biogeosciences."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_45","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","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_47","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_48","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_49","doi-asserted-by":"crossref","unstructured":"Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., and Feng, M. (2019). Evaluation of Three Deep Learning Models for Early Crop Classifi-cation Using Sentinel-1A Imagery Time Series\u2014A Case Study in Zhanjiang, China. Remote Sens., 11.","DOI":"10.3390\/rs11222673"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1137\/19M1274067","article-title":"DeepXDE: A Deep Learning Library for Solving Differential Equations","volume":"63","author":"Lu","year":"2021","journal-title":"SIAM Rev."},{"key":"ref_51","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_52","first-page":"1","article-title":"The truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Du, Z., Yang, J., Ou, C., and Zhang, T. (2019). Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method. Remote Sens., 11.","DOI":"10.3390\/rs11070888"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e5431","DOI":"10.7717\/peerj.5431","article-title":"Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data","volume":"6","author":"Hao","year":"2018","journal-title":"PeerJ"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2790\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:31:19Z","timestamp":1760164279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,15]]},"references-count":55,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142790"],"URL":"https:\/\/doi.org\/10.3390\/rs13142790","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,15]]}}}