{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:45:55Z","timestamp":1784303155447,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"open project of key laboratory of geological processes and mineral resources in Northern Tibet Plateau of Qinghai Province","award":["2019-KZ-0"],"award-info":[{"award-number":["2019-KZ-0"]}]},{"name":"open project of key laboratory of geological processes and mineral resources in Northern Tibet Plateau of Qinghai Province","award":["2019-ZJ-T04"],"award-info":[{"award-number":["2019-ZJ-T04"]}]},{"name":"special project for innovation platform construction of science and technology department of Qinghai Province","award":["2019-KZ-0"],"award-info":[{"award-number":["2019-KZ-0"]}]},{"name":"special project for innovation platform construction of science and technology department of Qinghai Province","award":["2019-ZJ-T04"],"award-info":[{"award-number":["2019-ZJ-T04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning.<\/jats:p>","DOI":"10.3390\/rs14122758","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine"],"prefix":"10.3390","volume":"14","author":[{"given":"Jinxi","family":"Yao","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"},{"name":"Key Laboratory of the Northern Qinghai\u2013Tibet Plateau Geological Processes and Mineral Resources, Xining 810300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengzhi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhong","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, P., Hu, S., Li, W., Zhang, C., and Cheng, P. (2021). Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework. Remote Sens., 13.","DOI":"10.3390\/rs13112146"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.apgeog.2016.09.009","article-title":"Changes in crop type distribution in Zhangye City of the Heihe River Basin, China","volume":"76","author":"Liu","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112599","DOI":"10.1016\/j.rse.2021.112599","article-title":"Towards interpreting multi-temporal deep learning models in crop mapping","volume":"264","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E. (2018). Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11010037"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, M., Lin, H., Wang, G., Sun, H., and Fu, J. (2018). Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China. Remote Sens., 10.","DOI":"10.3390\/rs10111840"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105962","DOI":"10.1016\/j.compag.2020.105962","article-title":"An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China","volume":"182","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.3390\/rs14081875","article-title":"High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine","volume":"14","author":"Rudiyanto","year":"2022","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Hern\u00e1ndez-L\u00f3pez, D., Ballesteros, R., and Moreno, M.A. (2021). Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13020243"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q., and Shao, Y. (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040561"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Awad, M. (2021, January 8\u201310). Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM). Proceedings of the 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET), Beirut, Lebanon.","DOI":"10.1109\/IMCET53404.2021.9665519"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Virnodkar, S., Pachghare, V.K., Patil, V.C., and Sunil, K.J. (2021). Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series. Progress in Advanced Computing and Intelligent Engineering, Springer.","DOI":"10.1007\/978-981-15-6353-9_15"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ponganan, N., Horanont, T., Artlert, K., and Pimpilai, N. (2021, January 26\u201327). Land Cover Classification using Google Earth Engine\u2019s Object-oriented and Machine Learning Classifier. Proceedings of the 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand.","DOI":"10.1109\/IBDAP52511.2021.9552099"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2021.04.015","article-title":"Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil","volume":"176","author":"Pott","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1017\/S0021859617000879","article-title":"Classification of multi-temporal spectral indices for crop type mapping: A case study in Coalville, UK","volume":"156","author":"Palchowdhuri","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Alibabaei, K., Gaspar, P.D., and Lima, T.M. (2021). Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling. Energies, 14.","DOI":"10.3390\/en14113004"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.3390\/rs10081217","article-title":"Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France","volume":"10","author":"Emile","year":"2018","journal-title":"Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s12524-019-01041-2","article-title":"Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images","volume":"47","author":"Kavita","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2021.01.008","article-title":"Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images","volume":"173","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2021.10.014","article-title":"S3ANet: Spectral-spatial-scale attention network for end-to-end precise crop classification based on UAV-borne H2 imagery","volume":"183","author":"Hu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"149831","DOI":"10.1016\/j.scitotenv.2021.149831","article-title":"Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China","volume":"802","author":"Lv","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2012.10.034","article-title":"Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products","volume":"130","author":"Zhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","article-title":"Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications","volume":"6","author":"Carneiro","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","first-page":"102692","article-title":"10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product","volume":"107","author":"Tran","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tuvdendorj, B., Zeng, H., Wu, B., Elnashar, A., Zhang, M., Tian, F., Nabil, M., Nanzad, L., Bulkhbai, A., and Natsagdorj, N. (2022). Performance and the Optimal Integration of Sentinel-1\/2 Time-Series Features for Crop Classification in Northern Mongolia. Remote Sens., 14.","DOI":"10.3390\/rs14081830"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"Singha","year":"2019","journal-title":"Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111521","DOI":"10.1016\/j.rse.2019.111521","article-title":"Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring","volume":"236","author":"Zhou","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s40010-017-0456-4","article-title":"Red Edge Index as an Indicator of Vegetation Growth and Vigor Using Hyperspectral Remote Sensing Data","volume":"87","author":"Bandyopadhyay","year":"2017","journal-title":"Proc. Natl. Acad. Sci. India Sect. A Phys. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.3390\/rs10091396","article-title":"Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study","volume":"10","author":"Mariette","year":"2018","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"136763","DOI":"10.1016\/j.scitotenv.2020.136763","article-title":"Understanding the changes in spatial fairness of urban greenery using time-series remote sensing images: A case study of Guangdong-Hong Kong-Macao Greater Bay","volume":"715","author":"Yang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_43","unstructured":"Zhang, L., Chen, Y., Chen, X., Chen, Y., and Lin, Y. (2006). Object-oriented classification of remote sensing data for change detection. Geoinformatics 2006: Remotely Sensed Data and Information, Proceedings of the Geoinformatics 2006: GNSS and Integrated Geospatial Applications Wuhan, China, 28\u201329 October 2006, SPIE."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, L., Abubakar, G.A., and Huang, J. (2021). High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13061148"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and Feature Extraction for Remote Sensing Images from Urban Areas Based on Morphological Transformations","volume":"41","author":"Atli","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_48","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, Publications Office of the European Union."},{"key":"ref_49","unstructured":"Aptoula, E. (2021, January 9\u201311). Weed and Crop Classification with Domain Adaptation for Precision Agriculture. Proceedings of the 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2020.07.002","article-title":"Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network","volume":"167","author":"Zheng","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2020.10.018","article-title":"From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2","volume":"171","author":"Hamrouni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2758\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:26:24Z","timestamp":1760138784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":51,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122758"],"URL":"https:\/\/doi.org\/10.3390\/rs14122758","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]}}}