{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:55:27Z","timestamp":1780451727451,"version":"3.54.1"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Natural Science Foundation of Shandong","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"Natural Science Foundation of Shandong","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Natural Science Foundation of Shandong","award":["41871253"],"award-info":[{"award-number":["41871253"]}]},{"name":"Natural Science Foundation of Shandong","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"Natural Science Foundation of China","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Natural Science Foundation of China","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"Natural Science Foundation of China","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Natural Science Foundation of China","award":["41871253"],"award-info":[{"award-number":["41871253"]}]},{"name":"Natural Science Foundation of China","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["41871253"],"award-info":[{"award-number":["41871253"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.<\/jats:p>","DOI":"10.3390\/rs14102341","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region"],"prefix":"10.3390","volume":"14","author":[{"given":"Xue","family":"Wang","sequence":"first","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Xun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenjiang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malak","family":"Henchiri","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5972-5474","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sha","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaishuai","family":"Li","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7387-1603","authenticated-orcid":false,"given":"Xiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105940","DOI":"10.1016\/j.compag.2020.105940","article-title":"Mapping Cotton Cultivated Area Combining Remote Sensing with a Fused Representation-Based Classification Algorithm","volume":"181","author":"Xun","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2021.08.021","article-title":"A Novel Cotton Mapping Index Combining Sentinel-1 SAR and Sentinel-2 Multispectral Imagery","volume":"181","author":"Xun","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wu, Z., Zhang, J., Deng, F., Zhang, S., Zhang, D., Xun, L., Ji, M., and Feng, Q. (2021). Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images. Remote Sens., 13.","DOI":"10.3390\/rs13204067"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"157513","DOI":"10.1109\/ACCESS.2019.2949799","article-title":"Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China","volume":"7","author":"Xun","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Felegari, S., Sharifi, A., Moravej, K., Amin, M., Golchin, A., Muzirafuti, A., Tariq, A., and Zhao, N. (2021). Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping. Appl. Sci., 11.","DOI":"10.3390\/app112110104"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Valero, S., Arnaud, L., Planells, M., and Ceschia, E. (2021). Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13234891"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.envsoft.2013.10.021","article-title":"Image Time Series Processing for Agriculture Monitoring","volume":"53","author":"Eerens","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2015.05.011","article-title":"Mapping Paddy Rice Planting Areas through Time Series Analysis of MODIS Land Surface Temperature and Vegetation Index Data","volume":"106","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, S., Feng, L., Zhang, J., and Deng, F. (2020). Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain. Appl. Sci., 10.","DOI":"10.3390\/app10082667"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved Maize Cultivated Area Estimation over a Large Scale Combining MODIS-EVI Time Series Data and Crop Phenological Information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, Z., Zhang, J., Deng, F., Zhang, S., Zhang, D., Xun, L., Javed, T., Liu, G., Liu, D., and Ji, M. (2021). Fusion of Gf and Modis Data for Regional-Scale Grassland Community Classification with Evi2 Time-Series and Phenological Features. Remote Sens., 13.","DOI":"10.3390\/rs13050835"},{"key":"ref_12","first-page":"91","article-title":"A Simple Smoother Based on Continuous Wavelet Transform: Comparative Evaluation Based on the Fidelity, Smoothness and Efficiency in Phenological Estimation","volume":"47","author":"Qiu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.isprsjprs.2010.04.004","article-title":"Early-Season Crop Area Estimates for Winter Crops in NE Australia Using MODIS Satellite Imagery","volume":"65","author":"Potgieter","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2008.08.015","article-title":"Phenologically-Tuned MODIS NDVI-Based Production Anomaly Estimates for Zimbabwe","volume":"113","author":"Funk","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality Extraction by Function Fitting to Time-Series of Satellite Sensor Data","volume":"40","author":"Eklundh","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A Program for Analyzing Time-Series of Satellite Sensor Data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2005.09.007","article-title":"Spatio-Temporal Distribution of Rice Phenology and Cropping Systems in the Mekong Delta with Special Reference to the Seasonal Water Flow of the Mekong and Bassac Rivers","volume":"100","author":"Sakamoto","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.rse.2007.05.017","article-title":"Wavelet Analysis of MODIS Time Series to Detect Expansion and Intensification of Row-Crop Agriculture in Brazil","volume":"112","author":"Galford","year":"2008","journal-title":"Remote Sens. Environ."},{"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":"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-Golay Filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1080\/01431169408954355","article-title":"Fourier Series for Analysis of Temporal Sequences of Satellite Sensor Imagery","volume":"15","author":"Olsson","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/01431169608949001","article-title":"Cover a Colour Composite of NOAA-AVHRR-NDVI Based on Time Series Analysis (1981\u20131992)","volume":"17","author":"Verhoef","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, T., Xie, C., Liu, Q., Guan, S., and Liu, G. (2019). Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-Scale Winter Wheat Identification. Remote Sens., 11.","DOI":"10.3390\/rs11141665"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Guidici, D., and Clark, M.L. (2017). One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California. Remote Sens., 9.","DOI":"10.3390\/rs9060629"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kumar, S., Shukla, S., Sharma, K.K., Singh, K.K., and Akbari, A.S. (2021). Classification of Land Cover and Land Use Using Deep Learning. Machine Vision and Augmented Intelligence\u2014Theory and Applications, Springer.","DOI":"10.1007\/978-981-16-5078-9_28"},{"key":"ref_28","first-page":"800","article-title":"Evaluation of Recurrent Neural Networks for Crop Recognition from Multitemporal Remote Sensing Images","volume":"2017","author":"Achanccaray","year":"2017","journal-title":"An. Do Congr. Bras. De Cartogr. E XXVI Expo."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, S.W., and Tao, C.S. (2017, January 18\u201321). Multi-Temporal PolSAR Crops Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network. Proceedings of the RSIP 2017\u2014International Workshop on Remote Sensing with Intelligent Processing, Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958818"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning Spectral-Spatialoral 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_32","first-page":"199","article-title":"Random forests, machine learning 45","volume":"2","author":"Breiman","year":"2001","journal-title":"J. Clin. Microbiol."},{"key":"ref_33","unstructured":"Mitchell, T.M. (2003). Machine Learning, McGraw-Hill."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7777","DOI":"10.1080\/01431161.2010.527397","article-title":"A Phenology-Based Approach to Map Crop Types in the San Joaquin Valley, California","volume":"32","author":"Zhong","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","first-page":"27","article-title":"Discussion on multispectral remote sensing image classification integrating object-oriented image analysis and KNN algorithm","volume":"11","author":"Lu","year":"2019","journal-title":"Sci. Technol. Innov. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"9","article-title":"Classification of rice phenotypic omics entities based on stacking integrated learning","volume":"50","author":"Juan","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.08.007","article-title":"Impact of Feature Selection on the Accuracy and Spatial Uncertainty of Per-Field Crop Classification Using Support Vector Machines","volume":"85","author":"Michel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","unstructured":"LeCun, Y., and Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, MIT Press."},{"key":"ref_40","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_41","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_42","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1016\/j.rse.2010.03.008","article-title":"Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data","volume":"114","author":"Tuanmu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"074003","DOI":"10.1088\/1748-9326\/ab80f0","article-title":"Identifying the Spatiotemporal Changes of Annual Harvesting Areas for Three Staple Crops in China by Integrating Multi-Data Sources","volume":"15","author":"Luo","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A Tutorial on Support Vector Regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1038\/nmeth.4370","article-title":"Classification and Regression Trees","volume":"14","author":"Krzywinski","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2017.04.021","article-title":"A Land Cover Change Detection and Classification Protocol for Updating Alaska NLCD 2001 to 2011","volume":"195","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TSMC.1976.5409182","article-title":"A Generalization of the K-NNRule","volume":"SMC-6","author":"Tomek","year":"1976","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4097","DOI":"10.1016\/j.ins.2009.08.025","article-title":"Troika\u2014An Improved Stacking Schema for Classification Tasks","volume":"179","author":"Menahem","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_49","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","first-page":"345","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"299","author":"Srivastava","year":"2014","journal-title":"Phys. Lett. B"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gadiraju, K.K., Ramachandra, B., Chen, Z., and Vatsavai, R.R. (2020, January 6\u201310). Multimodal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403375"},{"key":"ref_52","unstructured":"Powers, D.M.W. (2020). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. arXiv, Available online: https:\/\/arxiv.org\/abs\/2010.16061."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"MODISphenology-derived, multi-year distribution of conterminous US crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1080\/01431161.2018.1492181","article-title":"Identifying crop planting areas using Fourier-transformed feature of time series MODIS leaf area index and sparse-representation-based classification in the North China Plain","volume":"40","author":"Xun","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","first-page":"190","article-title":"Remote sensing monitoring of winter wheat sowing area changes in the North China Plain from 2001 to 2011","volume":"31","author":"Wang","year":"2015","journal-title":"J. Agric. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","article-title":"On Early Stopping in Gradient Descent Learning","volume":"26","author":"Yao","year":"2007","journal-title":"Constr. Approx."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2341\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:48Z","timestamp":1760137788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,12]]},"references-count":56,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102341"],"URL":"https:\/\/doi.org\/10.3390\/rs14102341","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,12]]}}}