{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:12:49Z","timestamp":1780492369645,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Heilongjiang, China","award":["2022ZX01A25"],"award-info":[{"award-number":["2022ZX01A25"]}]},{"name":"Key Research and Development Program of Heilongjiang, China","award":["JD2023GJ01"],"award-info":[{"award-number":["JD2023GJ01"]}]},{"name":"Key Research and Development Program of Heilongjiang, China","award":["42171056"],"award-info":[{"award-number":["42171056"]}]},{"name":"Key Research and Development Program of Heilongjiang, China","award":["42471362"],"award-info":[{"award-number":["42471362"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZX01A25"],"award-info":[{"award-number":["2022ZX01A25"]}]},{"name":"National Natural Science Foundation of China","award":["JD2023GJ01"],"award-info":[{"award-number":["JD2023GJ01"]}]},{"name":"National Natural Science Foundation of China","award":["42171056"],"award-info":[{"award-number":["42171056"]}]},{"name":"National Natural Science Foundation of China","award":["42471362"],"award-info":[{"award-number":["42471362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The early and accurate mapping of winter canola is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Although some methods have been proposed to map the winter canola at the flowering or later stages, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. The objective of this study was to evaluate the potential of using the combination of optical and synthetic aperture radar (SAR) data for mapping winter canola at the early stage. We assessed the contributions of spectral features, backscatter coefficients, and textural features, derived from Sentinel-2 and Sentinel-1 SAR images, for mapping winter canola at early stages. Random forest (RF) and support vector machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then the best model was applied to corresponding satellite data in 2018\u20132022. The following results were obtained: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV (vertical transmission, vertical reception), DVI (Difference vegetation index), and GOSAVI (Green Optimized Soil Adjusted Vegetation Index); (2) based on Sentinel-1 and Sentinel-2 data, winter canola could be mapped as early as 130 days prior to ripening (i.e., early overwinter stage), with the F-score over 0.85 and the OA (Overall Accuracy) over 81%; (3) adding Sentinel-1 could improve the OA by about 2\u20134% and the F-score by about 1\u20132%; and (4) based on the classifier transfer approach, the F-scores of winter canola mapping in 2018\u20132022 varied between 0.75 and 0.97, and the OAs ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could enable the large-scale early mapping of canola and provide valuable information for stakeholders and decision makers.<\/jats:p>","DOI":"10.3390\/rs16173197","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:12:13Z","timestamp":1724929933000},"page":"3197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China"],"prefix":"10.3390","volume":"16","author":[{"given":"Tingting","family":"Liu","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150008, China"},{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-933X","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Forestry, Northeast Forestry University, Harbin 150040, China"},{"name":"Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4756-9934","authenticated-orcid":false,"given":"Ran","family":"Meng","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150008, China"},{"name":"National Key Laboratory of Smart Farming Technologies and Systems, Harbin 150008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1080\/01431161.2015.1047994","article-title":"Spectral indices for yellow canola flowers","volume":"36","author":"Sulik","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.asr.2018.09.018","article-title":"Object-based rice mapping using time-series and phenological data","volume":"63","author":"Zhang","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2015.03.007","article-title":"Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape","volume":"206","author":"Chipanshi","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., and Wu, W. (2017). In-season crop mapping with GF-1\/WFV data by combining object-based image analysis and random forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_6","first-page":"114","article-title":"Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review","volume":"117","author":"Mosleh","year":"2015","journal-title":"Comput. Fluids"},{"key":"ref_7","unstructured":"Jing, Y., Li, G., Chen, J., and Shi, Y. (2013). Determination of Paddy Rice Growth Indicators with MODIS Data and Ground-Based Measurements of LAI, Atlantis Press."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.fecs.2022.100032","article-title":"Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China","volume":"9","author":"Wang","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.isprsjprs.2023.09.009","article-title":"A spectral-temporal constrained deep learning method for tree species mapping of plantation forests using time series Sentinel-2 imagery","volume":"204","author":"Huang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.isprsjprs.2020.03.009","article-title":"Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages","volume":"163","author":"Mercier","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, S.S., Chen, Y.R., Ma, Y.T., Kong, X.X., Zhang, X.Y., and Zhang, D.Y. (2020). Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm. Remote Sens., 12.","DOI":"10.3390\/rs12203400"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2016.06.016","article-title":"Spectral considerations for modeling yield of canola","volume":"184","author":"Sulik","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2019.08.007","article-title":"Automatic canola mapping using time series of sentinel 2 images","volume":"156","author":"Ashourloo","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.cj.2022.04.013","article-title":"Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm (APPA) in Google Earth Engine","volume":"10","author":"Han","year":"2022","journal-title":"Crop. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1016\/S2095-3119(19)62577-3","article-title":"Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China","volume":"18","author":"Tao","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113145","DOI":"10.1016\/j.rse.2022.113145","article-title":"Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring","volume":"280","author":"Zavagli","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113821","DOI":"10.1016\/j.rse.2023.113821","article-title":"Integration of deep learning algorithms with a Bayesian method for improved characterization of tropical deforestation frontiers using Sentinel-1 SAR imagery","volume":"298","author":"Sun","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liao, C.H., Wang, J.F., Xie, Q.H., Baz, A.A., Huang, X.D., Shang, J.L., and He, Y.J. (2020). Synergistic Use of multi-temporal RADARSAT-2 and VEN\u00b5S data for crop classification based on 1D convolutional neural network. Remote Sens., 12.","DOI":"10.3390\/rs12050832"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Hu, L., Xu, N., Liang, J., Li, Z., Chen, L., and Zhao, F. (2020). Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China. Remote Sens., 12.","DOI":"10.3390\/rs12193120"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ga\u0161parovi\u0107, M., and Dobrini\u0107, D. (2020). Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal sentinel-1 imagery. Remote Sens., 12.","DOI":"10.3390\/rs12121952"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Z., Luo, J.H., Yang, J.Z.C., Yu, Q.Y., Zhang, L., Xue, K., and Lu, L.R. (2020). Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12183086"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/LSP.2017.2758203","article-title":"SAR Image Despeckling Using a Convolutional Neural Network","volume":"24","author":"Wang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2023.04.002","article-title":"An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery","volume":"199","author":"Wang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.5194\/gmd-15-7933-2022","article-title":"Bayesian atmospheric correction over land: Sentinel-2\/MSI and Landsat 8\/OLI","volume":"15","author":"Yin","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Porwal, S., and Katiyar, S.K. (2014, January 7\u20139). Performance evaluation of various resampling techniques on IRS imagery. Proceedings of the International Conference on Contemporary Computing, Noida, India.","DOI":"10.1109\/IC3.2014.6897222"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.rse.2018.09.003","article-title":"Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on Sentinel-1","volume":"217","author":"Periasamy","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(84)90197-X","article-title":"Segmentation of a high-resolution urban scene using texture operators","volume":"25","author":"Conners","year":"1984","journal-title":"Comput. Vis. Graph Image Process"},{"key":"ref_35","first-page":"83","article-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"27","author":"Hastie","year":"2004","journal-title":"Math. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"VSURF: Variable Selection Using Random Forests","volume":"31","author":"Genuer","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, N., Yu, L., Zhang, X., Shen, Y., Zeng, L., Hu, Q., and Niyogi, D. (2020). Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform. Remote Sens., 12.","DOI":"10.3390\/rs12182992"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wei, C., Huang, J., Mansaray, L., Li, Z., Liu, W., and Han, J. (2017). Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method. Remote Sens., 9.","DOI":"10.3390\/rs9050488"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2017.03.014","article-title":"Use of time-series L-band UAVSAR data for the classification of agricultural fields in the San Joaquin Valley","volume":"193","author":"Whelen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1080\/01431161.2017.1407046","article-title":"Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat","volume":"39","author":"Liao","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification","volume":"47","author":"Mcnairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2021.12.001","article-title":"Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery","volume":"184","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"026020","DOI":"10.1117\/1.JRS.10.026020","article-title":"Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops","volume":"10","author":"Betbeder","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/36.841996","article-title":"Modeling microwave interactions with crops and comparison with ERS-2 SAR observations","volume":"38","author":"Cookmartin","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4236\/ars.2013.22020","article-title":"Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data\u2014From Temporal Signatures to Crop Parameters Estimation","volume":"2","author":"Fieuzal","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e20125","DOI":"10.1002\/agg2.20125","article-title":"Automated detection of phenological transitions for yellow flowering plants such as Brassicaoilseeds","volume":"3","author":"Sulik","year":"2020","journal-title":"Agrosystems Geosci. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zang, Y., Chen, X., Chen, J., Tian, Y., and Cui, X. (2020). Remote Sensing Index for Mapping Canola Flowers Using MODIS Data. Remote Sens., 12.","DOI":"10.3390\/rs12233912"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1080\/15481603.2022.2104999","article-title":"Early mapping of winter wheat in Henan province of China using time series of Sentinel-2 data","volume":"59","author":"Huang","year":"2022","journal-title":"GIsci Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.rse.2020.111660","article-title":"Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series","volume":"239","author":"Taymans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"224","DOI":"10.3390\/ijgi7060224","article-title":"A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features","volume":"7","author":"Dong","year":"2018","journal-title":"Int. J. Geo-Inf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.jia.2022.10.008","article-title":"Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index","volume":"22","author":"Tao","year":"2023","journal-title":"J. Integr. Agric."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.02.018","article-title":"Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine","volume":"175","author":"Adrian","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1080\/01431161.2019.1569791","article-title":"Crop type classification using a combination of optical and radar remote sensing data: A review","volume":"40","author":"Orynbaikyzy","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Arias, M., Campo-Besc\u00f3s, M.\u00c1., and \u00c1lvarez-Mozos, J. (2020). Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain. Remote Sens., 12.","DOI":"10.3390\/rs12020278"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111411","DOI":"10.1016\/j.rse.2019.111411","article-title":"Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US","volume":"233","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"112822","DOI":"10.1016\/j.rse.2021.112822","article-title":"Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning","volume":"269","author":"Zhao","year":"2022","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:45:17Z","timestamp":1760111117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,29]]},"references-count":59,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173197"],"URL":"https:\/\/doi.org\/10.3390\/rs16173197","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,29]]}}}