{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T10:48:55Z","timestamp":1784803735606,"version":"3.55.0"},"reference-count":77,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42171330"],"award-info":[{"award-number":["42171330"]}]},{"name":"National Natural Science Foundation of China","award":["BJJWZYJH01201910028032"],"award-info":[{"award-number":["BJJWZYJH01201910028032"]}]},{"name":"Beijing Outstanding Young Scientist Program","award":["42171330"],"award-info":[{"award-number":["42171330"]}]},{"name":"Beijing Outstanding Young Scientist Program","award":["BJJWZYJH01201910028032"],"award-info":[{"award-number":["BJJWZYJH01201910028032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate mapping of winter wheat using remote sensing technology is essential for ensuring food security. Most of the existing studies have failed to fully characterize the phenological features of winter wheat in mapping, resulting in low classification accuracy. To this end, this study developed a new multiple phenological spectral feature (Mpsf) and then used the generated new features as input data for a one-class classifier (One-Class Support Vector Machine, OCSVM) to map winter wheat. The main steps in this work are as follows: (1) Identifying key phenological periods. The spectral indices temporal profiles of winter wheat (after cloud masking) were drawn separately using different spectral indices, and the key phenological periods of winter wheat were identified with a priori knowledge of phenology. (2) Composition for a new feature. Composited the spectral features of winter wheat for each key phenological period to generate a new feature. (3) Training using a one-class classifier. The new feature was put into OCSVM for training, and the final winter wheat mapping result in the Beijing region was obtained. The cost of this new winter wheat mapping method is low and the accuracy is high. To verify the accuracy of this study, we compared the Mpsf map with three kinds of reference data, and all of them got good results. In comparison, with ground truth samples from Sentinel-2, the total accuracy was overall higher than 97.9%. The relative error of the 2019 winter wheat mapping result was only 0.51%, compared with the data from the Beijing Bureau of Statistics. In comparison, with an up-to-date available winter wheat-mapping product for Beijing (spatial resolution: 30 m), the Mpsf map has significantly fewer misclassifications. To our knowledge, this study produced one of the highest accuracy winter wheat-mapping products in Beijing for 2018 and 2019 to date. In general, we hope that this work can promote the development of winter wheat mapping and provide a reference for sustainable agricultural development and governmental decision-making.<\/jats:p>","DOI":"10.3390\/rs14184529","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A New Multiple Phenological Spectral Feature for Mapping Winter Wheat"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2832-8038","authenticated-orcid":false,"given":"Wenxin","family":"Cai","sequence":"first","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyan","family":"Tian","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojuan","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, N.A., Zhang, B.Z., Liu, Y., Peng, Z.G., Zhou, Q.Y., and Wei, Z. (2022). Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data. Atmosphere, 13.","DOI":"10.3390\/atmos13010122"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1038\/s41467-021-22564-8","article-title":"O-linked N-acetylglucosamine transferase is involved in fine regulation of flowering time in winter wheat","volume":"12","author":"Fan","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dong, Q., Chen, X., Chen, J., Zhang, C., Liu, L., Cao, X., Zang, Y., Zhu, X., and Cui, X. (2020). Mapping Winter Wheat in North China Using Sentinel 2A\/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping. Remote Sens., 12.","DOI":"10.3390\/rs12081274"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, C., Chen, W., Wang, Y., Wang, Y., Ma, C., Li, Y., Li, J., and Zhai, W. (2022). Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China. Remote Sens., 14.","DOI":"10.3390\/rs14020284"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s11769-019-1060-0","article-title":"Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data","volume":"29","author":"Useya","year":"2019","journal-title":"Chin. Geogr. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3389\/fenvs.2020.00077","article-title":"Wheat Area Mapping in Afghanistan Based on Optical and SAR Time-Series Images in Google Earth Engine Cloud Environment","volume":"8","author":"Tiwari","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Zhou, T., Pan, J., Zhang, P., Wei, S., and Han, T. (2017). Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors, 17.","DOI":"10.3390\/s17061210"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.06.018","article-title":"An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine","volume":"178","author":"Ni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liang, S., and Wang, J. (2020). Chapter 1\u2014A systematic view of remote sensing. Advanced Remote Sensing, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-815826-5.00001-5"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.isprsjprs.2018.05.024","article-title":"Mapping water-logging damage on winter wheat at parcel level using high spatial resolution satellite data","volume":"142","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.3390\/rs6053611","article-title":"Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image","volume":"6","author":"Yuan","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Y., Tao, B., Ren, W., Zourarakis, D.P., El Masri, B., Sun, Z., and Tian, Q. (2019). An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images. Remote Sens., 11.","DOI":"10.3390\/rs11101191"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/S2095-3119(15)61304-1","article-title":"Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data","volume":"16","author":"Tao","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106049","DOI":"10.1016\/j.compag.2021.106049","article-title":"Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China","volume":"182","author":"Ren","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, F., Ren, J., Wu, S., Zhao, H., and Zhang, N. (2021). Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds. Remote Sens., 13.","DOI":"10.3390\/rs13061162"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.5194\/essd-12-3081-2020","article-title":"Early-season mapping of winter wheat in China based on Landsat and Sentinel images","volume":"12","author":"Dong","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Meng, S., Zhong, Y., Luo, C., Hu, X., Wang, X., and Huang, S. (2020). Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China. Remote Sens., 12.","DOI":"10.3390\/rs12020226"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111410","DOI":"10.1016\/j.rse.2019.111410","article-title":"High resolution wheat yield mapping using Sentinel-2","volume":"233","author":"Hunt","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.isprsjprs.2021.03.015","article-title":"A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages","volume":"175","author":"Qu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, H., Wu, X., Yang, W., Shen, Y., Lu, B., and Wang, J. (2022). AUTS: A Novel Approach to Mapping Winter Wheat by Automatically Updating Training Samples Based on NDVI Time Series. Agriculture, 12.","DOI":"10.3390\/agriculture12060817"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y. (2021). Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7\/8 Images, and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132510"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113060","DOI":"10.1016\/j.rse.2022.113060","article-title":"Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage","volume":"277","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.compag.2016.03.008","article-title":"Mapping interannual variability of maize cover in a large irrigation district using a vegetation index\u2014Phenological index classifier","volume":"123","author":"Jiang","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111916","DOI":"10.1016\/j.rse.2020.111916","article-title":"Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995\u20132018","volume":"247","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/17538947.2016.1187673","article-title":"Mass data processing of time series Landsat imagery: Pixels to data products for forest monitoring","volume":"9","author":"Hermosilla","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111745","DOI":"10.1016\/j.rse.2020.111745","article-title":"Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion","volume":"242","author":"Tian","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"102065","article-title":"Spatial and semantic effects of LUCAS samples on fully automated land use\/land cover classification in high-resolution Sentinel-2 data","volume":"88","author":"Weigand","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Polykretis, C., Grillakis, M., and Alexakis, D. (2020). Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece. Remote Sens., 12.","DOI":"10.3390\/rs12020319"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1016\/j.scitotenv.2018.09.115","article-title":"NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015","volume":"650","author":"Chu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Six, J., Plant, R.E., and Pe\u00f1a, J.M. (2018). Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California. Remote Sens., 10.","DOI":"10.3390\/rs10111745"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00484-016-1236-6","article-title":"Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland","volume":"61","author":"Ren","year":"2016","journal-title":"Int. J. Biometeorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100034","DOI":"10.1016\/j.tfp.2020.100034","article-title":"Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India","volume":"2","author":"Bera","year":"2020","journal-title":"Trees For. People"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_39","first-page":"183","article-title":"Combining UAV Visible Light and Multispectral Vegetation Indices for Estimating SPAD Value of Winter Wheat","volume":"52","author":"Niu","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_40","first-page":"939","article-title":"Estimating Vegetation Coverage of Winter Wheat Based on New Vegetation Index","volume":"36","author":"Chen","year":"2016","journal-title":"J. Triticeae Crops"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Pickens, A., Krylov, A., and Stehman, S.V. (2018). Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan. Remote Sens., 10.","DOI":"10.3390\/rs10040489"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tian, H., Pei, J., Huang, J., Li, X., Wang, J., Zhou, B., Qin, Y., and Wang, L. (2020). Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote Sens., 12.","DOI":"10.3390\/rs12213539"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.isprsjprs.2008.01.001","article-title":"LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements","volume":"63","author":"Darvishzadeh","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/anbo.1997.0544","article-title":"An Algorithm for Estimating Chlorophyll Content in Leaves Using a Video Camera","volume":"81","author":"Kawashima","year":"1998","journal-title":"Ann. Bot."},{"key":"ref_47","first-page":"979","article-title":"From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research","volume":"23","author":"Wang","year":"2003","journal-title":"Acta Ecol. Sin."},{"key":"ref_48","unstructured":"Chen, Y.Y. (2019). The Main Tree Species in Typital Areas of Beijing Regulate the Air Quality and Ecological Function. [Master\u2019s Thesis, University of Agriculture]."},{"key":"ref_49","first-page":"139","article-title":"Extraction and annual variation dynamic monitoring of winter wheat area based on GF-1 images","volume":"49","author":"Li","year":"2017","journal-title":"Shandong Agric. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1109\/TGRS.2009.2019126","article-title":"Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping","volume":"47","author":"Tolpekin","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qi, Q., and Liu, Y. (2018). Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10081193"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/S0034-4257(02)00196-7","article-title":"Spectral discrimination of vegetation types in a coastal wetland","volume":"85","author":"Schmidt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the Support of a High-Dimensional Distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_55","first-page":"102446","article-title":"AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine","volume":"102","author":"Yang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/TCYB.2014.2340433","article-title":"Parameter Selection of Gaussian Kernel for One-Class SVM","volume":"45","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_58","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_59","unstructured":"Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., Han, W., Zheng, Y., Huang, J., and Yaun, W. (2020). 30 m Winter Wheat Distribution Map of China for Four Years (2016\u20132019). figshare."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"112367","DOI":"10.1016\/j.rse.2021.112367","article-title":"Impacts of ignorance on the accuracy of image classification and thematic mapping","volume":"259","author":"Foody","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/10106049.2012.760004","article-title":"Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain","volume":"29","author":"Lu","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"108153","DOI":"10.1016\/j.agrformet.2020.108153","article-title":"Investigating the urban-induced microclimate effects on winter wheat spring phenology using Sentinel-2 time series","volume":"294","author":"Tian","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Song, Y., and Wang, J. (2019). Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11040449"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.isprsjprs.2016.09.016","article-title":"Winter wheat mapping combining variations before and after estimated heading dates","volume":"123","author":"Qiu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1016\/j.asr.2019.08.042","article-title":"Mapping paddy rice by the object-based random forest method using time series Sentinel-1\/Sentinel-2 data","volume":"64","author":"Cai","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.3389\/fpls.2019.01749","article-title":"Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm","volume":"10","author":"Anderegg","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_67","first-page":"68","article-title":"Extraction model of winter wheat planting information based on unsupervised classification","volume":"8","author":"Wang","year":"2019","journal-title":"Bull. Surv. Mapp."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s42489-020-00060-1","article-title":"Estimation of Forest Canopy Cover and Forest Fragmentation Mapping Using Landsat Satellite Data of Silabati River Basin (India)","volume":"70","author":"Bera","year":"2020","journal-title":"KN-J. Cartogr. Geogr. Inf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2017.11.009","article-title":"Remote sensing of mangrove forest phenology and its environmental drivers","volume":"205","author":"Dash","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2017.05.025","article-title":"Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111611","DOI":"10.1016\/j.rse.2019.111611","article-title":"Mapping smallholder and large-scale cropland dynamics with a flexible classification system and pixel-based composites in an emerging frontier of Mozambique","volume":"239","author":"Bey","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal Composite Landsat TM\/ETM+ Images Using the Medoid (a Multi-Dimensional Median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isprsjprs.2016.07.008","article-title":"Mapping raised bogs with an iterative one-class classification approach","volume":"120","author":"Mack","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"034512","DOI":"10.1117\/1.JRS.13.034512","article-title":"Study on the potential of whitening transformation in improving single crop mapping accuracy","volume":"13","author":"Zhao","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_76","first-page":"1","article-title":"Research Progress of Image Sensing and Deep Learning in Agriculture","volume":"51","author":"Sun","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_77","unstructured":"Cai, W. (2010). Wheat Identification and Area Estimation Based on Mixed Image Element Decomposition of MODIS Remote Sensing Data. [Master\u2019s Thesis, Shandong Normal University]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:29:11Z","timestamp":1760142551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,10]]},"references-count":77,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184529"],"URL":"https:\/\/doi.org\/10.3390\/rs14184529","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,10]]}}}