{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:09:01Z","timestamp":1774498141189,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T00:00:00Z","timestamp":1562716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2016YFB0501404"],"award-info":[{"award-number":["2016YFB0501404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China\u2019s 1982\u20132017 0.05\u00b0 land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.<\/jats:p>","DOI":"10.3390\/rs11141639","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T11:56:51Z","timestamp":1562759811000},"page":"1639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Haoyu","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0155-6735","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7844-593X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4638-3743","authenticated-orcid":false,"given":"Donghai","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Peking University, Beijing 100871, China"}]},{"given":"Xiaozheng","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1126\/science.1118160","article-title":"The Importance of Land-Cover Change in Simulating Future Climates","volume":"310","author":"Feddema","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.rse.2007.07.004","article-title":"Landsat continuity: Issues and opportunities for land cover monitoring","volume":"112","author":"Wulder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.5194\/bg-7-1383-2010","article-title":"Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model","volume":"7","author":"Bathiany","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1146\/annurev.energy.28.050302.105459","article-title":"Dynamics of land-use and land-cover change in tropical regions","volume":"28","author":"Lambin","year":"2003","journal-title":"Annu. Rev. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/00045608.2011.596357","article-title":"A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery","volume":"102","author":"Liu","year":"2012","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","article-title":"Remote sensing technology for mapping and monitoring land-cover and land-use change","volume":"61","author":"Rogan","year":"2004","journal-title":"Prog. Plan."},{"key":"ref_8","first-page":"125","article-title":"Detection of land use and land cover change and land surface temperature in English Bazar urban centre","volume":"20","author":"Pal","year":"2017","journal-title":"Egypt. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ijsbe.2015.02.005","article-title":"Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt","volume":"4","author":"Hegazy","year":"2015","journal-title":"Int. J. Sustain. Built Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1080\/014311697217099","article-title":"The IGBP-DIS global 1km land cover data set, DISCover: First results","volume":"18","author":"Loveland","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","first-page":"650","article-title":"Completion of the 1990s National Land Cover Data Set for the Conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources","volume":"67","author":"Vogelmann","year":"2010","journal-title":"Am. Soc. Photogram. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/j.rse.2013.10.004","article-title":"Development and assessment of a 250 m spatial resolution MODIS annual land cover time series (2000\u20132011) for the forest region of Canada derived from change-based updating","volume":"140","author":"Pouliot","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2013.03.022","article-title":"Consistent classification of image time series with automatic adaptive signature generalization","volume":"134","author":"Gray","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.02.030","article-title":"A new approach for land cover classification and change analysis: Integrating backdating and an object-based method","volume":"177","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/07038992.2015.1089401","article-title":"Large area mapping of annual land cover dynamics using multitemporal change detection and classification of Landsat time series data","volume":"41","author":"Franklin","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/17538947.2013.805262","article-title":"A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies","volume":"6","author":"Liang","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10281","DOI":"10.1002\/2014JD021667","article-title":"Analysis of global land surface albedo climatology and spatial-temporal variation during 1981\u20132010 from multiple satellite products","volume":"119","author":"He","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TGRS.2003.811744","article-title":"Development of an evapotranspiration index from Aqua\/MODIS for monitoring surface moisture status","volume":"41","author":"Nishida","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.1109\/TGRS.2016.2560522","article-title":"Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance","volume":"54","author":"Xiao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2014.07.003","article-title":"Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data","volume":"152","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2015.10.016","article-title":"Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data-based GLASS leaf area index product","volume":"171","author":"Xiao","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1006\/jpdc.1999.1573","article-title":"D-ISODATA: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations","volume":"59","author":"Dhodhi","year":"1999","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_28","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lv, Z., Hu, Y., Zhong, H., Wu, J., Li, B., and Zhao, H. (2010). Parallel k-means clustering of remote sensing images based on mapreduce. International Conference on Web Information Systems and Mining, Springer.","DOI":"10.1007\/978-3-642-16515-3_21"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(98)00088-1","article-title":"Fuzzy neural network classification of global land cover from a 1 AVHRR data set","volume":"67","author":"Gopal","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.rse.2004.01.016","article-title":"Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data","volume":"90","author":"Boles","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"829","DOI":"10.14358\/PERS.70.7.829","article-title":"Development of a 2001 national land-cover database for the United States","volume":"70","author":"Homer","year":"2004","journal-title":"J. Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.rse.2007.07.002","article-title":"Contribution of multispectral and multitemporal information from MODIS images to land cover classification","volume":"112","author":"Caetano","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised deep feature extraction for remote sensing image classification","volume":"54","author":"Romero","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/LGRS.2017.2728698","article-title":"Land cover classification via multi-temporal spatial data by recurrent neural networks","volume":"14","author":"Ienco","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lyu, H., Lu, H., and Mou, L. (2016). Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sens., 8.","DOI":"10.3390\/rs8060506"},{"key":"ref_48","first-page":"551","article-title":"Multi-temporal land cover classification with long short-term memory neural networks","volume":"42","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2015.03.014","article-title":"Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin","volume":"105","author":"Mellor","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","article-title":"A multiple resampling method for learning from imbalanced data sets","volume":"20","author":"Estabrooks","year":"2004","journal-title":"Comput. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_52","unstructured":"Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., and Wu, S. (2018). China\u2019s Multi-Period Land Use Land Cover Remote Sensing Monitoring Data Set (CNLUCC), Resource and Environment Data Cloud Platform."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.rse.2015.03.025","article-title":"Assessment of five global satellite products of fraction of absorbed photosynthetically active radiation: Intercomparison and direct validation against ground-based data","volume":"163","author":"Tao","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"Sulla-Menashe, D., and Friedl, M.A. (2018). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"024002","DOI":"10.1088\/1748-9326\/11\/2\/024002","article-title":"Atmospheric summer teleconnections and Greenland Ice Sheet surface mass variations: Insights from MERRA-2","volume":"11","author":"Lim","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1175\/JCLI-D-16-0570.1","article-title":"Land surface precipitation in MERRA-2","volume":"30","author":"Reichle","year":"2017","journal-title":"J. Clim."},{"key":"ref_57","unstructured":"Bosilovich, M., Lucchesi, R., and Suarez, M. (2019, June 01). MERRA-2: File Specification, Available online: https:\/\/ntrs.nasa.gov\/search.jsp?R=20150019760."},{"key":"ref_58","first-page":"727","article-title":"Mapping city lights with nighttime data from the DMSP Operational Linescan System","volume":"63","author":"Elvidge","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"054011","DOI":"10.1088\/1748-9326\/10\/5\/054011","article-title":"A global map of urban extent from nightlights","volume":"10","author":"Zhou","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2014.03.004","article-title":"A cluster-based method to map urban area from DMSP\/OLS nightlights","volume":"147","author":"Zhou","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0378-1127(99)00272-8","article-title":"Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy","volume":"128","author":"Fahsi","year":"2000","journal-title":"For. Ecol. Manag."},{"key":"ref_62","unstructured":"Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D.B., Oimoen, M.J., Zhang, Z., Danielson, J.J., Krieger, T., Curtis, B., and Haase, J. (2019, June 01). ASTER Global Digital Elevation Model Version 2-Summary of Validation Results, Available online: https:\/\/pubs.er.usgs.gov\/publication\/70005960."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B., and dos Reis Alves, S.F. (2017). Artificial Neural Networks, Springer International Publishing.","DOI":"10.1007\/978-3-319-43162-8"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/aob\/mcg029","article-title":"A flexible sigmoid function of determinate growth","volume":"91","author":"Yin","year":"2003","journal-title":"Ann. Bot."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/01431161.2018.1516313","article-title":"Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series","volume":"40","author":"Sun","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","unstructured":"Graves, A. (2019, June 01). Long Short-Term Memory. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-3-642-24797-2_4."},{"key":"ref_68","unstructured":"Liu, P., Qiu, X., and Huang, X. (2016). Recurrent neural network for text classification with multi-task learning. arXiv."},{"key":"ref_69","first-page":"27","article-title":"An improved algorithm for imbalanced data and small sample size classification","volume":"3","author":"Hu","year":"2015","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhao, H., Chen, X., Nguyen, T., Huang, J.Z., Williams, G., and Chen, H. (2016, January 19). Stratified over-sampling bagging method for random forests on imbalanced data. Proceedings of the 2016 Pacific-Asia Workshop on Intelligence and Security Informatics, Auckland, New Zealand.","DOI":"10.1007\/978-3-319-31863-9_5"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. Proceedings of the European Conference on Information Retrieval, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/isprs-archives-XLII-3-257-2018","article-title":"Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network","volume":"XLII-3","author":"Dang","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_76","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_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S. (2018, January 9\u201312). Privacy risk in machine learning: Analyzing the connection to overfitting. Proceedings of the 2018 IEEE 31st Computer Security Foundations Symposium (CSF), Oxford, UK.","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.procs.2015.07.415","article-title":"NDVI: Vegetation change detection using remote sensing and GIS\u2014A case study of Vellore District","volume":"57","author":"Gandhi","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3189\/S026030550000046X","article-title":"Snow mapping and classification from Landsat Thematic Mapper data","volume":"9","author":"Dozier","year":"1987","journal-title":"Ann. Glaciol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2006.02.010","article-title":"Use of impervious surface in urban land-use classification","volume":"102","author":"Lu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1080\/01431160210154858","article-title":"Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors","volume":"24","author":"Lotsch","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.agwat.2006.02.011","article-title":"Study of reference crop evapotranspiration in IR of Iran","volume":"84","author":"Dinpashoh","year":"2006","journal-title":"Agric. Water Manag."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., and Lursinsap, C. (2009, January 27\u201330). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand.","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1007\/s11430-010-4002-3","article-title":"China\u2019s wetland change (1990\u20132000) determined by remote sensing","volume":"53","author":"Gong","year":"2010","journal-title":"Sci. Chin. Earth Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/BF00045199","article-title":"Wetland classification and inventory: A summary","volume":"118","author":"Finlayson","year":"1995","journal-title":"Vegetatio"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:04:18Z","timestamp":1760187858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,10]]},"references-count":87,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141639"],"URL":"https:\/\/doi.org\/10.3390\/rs11141639","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,10]]}}}