{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T19:43:43Z","timestamp":1775591023802,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Local Scientific and Technological Development Projects of Qinghai Guided by the Central Government of China","award":["*"],"award-info":[{"award-number":["*"]}]},{"name":"National High Resolution Earth Observation System (The Civil Part) Technology Projects of China","award":["*"],"award-info":[{"award-number":["*"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The northeastern margin of the Qinghai\u2013Tibet Plateau (QTP) is an agricultural protection area in China\u2019s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A\/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices\u2019 time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices\u2014normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)\u2014maintained high mapping potential; (2) under the optimal threshold, &gt;88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.<\/jats:p>","DOI":"10.3390\/rs14020343","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T09:10:36Z","timestamp":1641978636000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai\u2013Tibet Plateau: A Case Study of Minhe, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Fujue","family":"Huang","sequence":"first","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4441-9674","authenticated-orcid":false,"given":"Xingsheng","family":"Xia","sequence":"additional","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongsheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghui","family":"Lv","sequence":"additional","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2307-2715","authenticated-orcid":false,"given":"Yaozhong","family":"Pan","sequence":"additional","affiliation":[{"name":"Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6660-2034","authenticated-orcid":false,"given":"Xiufang","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s12571-013-0263-y","article-title":"Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security","volume":"5","author":"Shiferaw","year":"2013","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1111\/aab.12108","article-title":"Food security: The challenge of increasing wheat yield and the importance of not compromising food safety","volume":"164","author":"Curtis","year":"2014","journal-title":"Ann. Appl. Biol."},{"key":"ref_3","first-page":"263","article-title":"Multi stage wheat yield estimation using different model under semiarid region of India","volume":"XLII-3-W6","author":"Vashisth","year":"2019","journal-title":"Int. Arch. Photogramm."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.fcr.2017.11.008","article-title":"Sensitivity of European wheat to extreme weather","volume":"222","author":"Kaseva","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_5","unstructured":"Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., and Genova, R.C. (2014). Food security and food production systems. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vannoppen, A., Gobin, A., Kotova, L., Top, S., De Cruz, L., V\u012bksna, A., Aniskevich, S., Bobylev, L., Buntemeyer, L., and Caluwaerts, S. (2020). Wheat yield estimation from NDVI and regional climate models in Latvia. Remote Sens., 12.","DOI":"10.3390\/rs12142206"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/0261-2194(93)90001-Y","article-title":"Remote sensing for crop protection","volume":"12","author":"Hatfield","year":"1993","journal-title":"Crop Prot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.2134\/agronj2004.1139","article-title":"Monitoring maize (Zea mays L.) Phenology with remote sensing","volume":"96","author":"Gitelson","year":"2004","journal-title":"Agron J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fang, P., Zhang, X., Wei, P., Wang, Y., Zhang, H., Liu, F., and Zhao, J. (2020). The classification performance and mechanism of machine learning algorithms in winter wheat mapping using Sentinel-2 10 m resolution imagery. Appl. Sci., 10.","DOI":"10.3390\/app10155075"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Feng, L., Du, Q., and Runge, T. (2020). Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens., 12.","DOI":"10.3390\/rs12081232"},{"key":"ref_11","first-page":"596","article-title":"Extracting winter wheat spatial distribution information from GF-2 image","volume":"24","author":"Song","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_12","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_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":"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","volume":"233","author":"Zhong","year":"2019","journal-title":"U.S. Remote Sens. Environ."},{"key":"ref_15","first-page":"508","article-title":"Study on planting areas, extraction of remote sensing and monitoring of crop growth of winter wheat and rice in Jiangsu Province in 2009","volume":"6","author":"Huang","year":"2010","journal-title":"Jiangsu Agric. Sci."},{"key":"ref_16","first-page":"47","article-title":"Application of multi-source and multi-temporal remote sensing data in winter wheat identification","volume":"26","author":"Li","year":"2010","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_17","first-page":"118","article-title":"Winter wheat yield estimation based on high and moderate resolution remote sensing data at county level","volume":"25","author":"Qin","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Engin."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.12677\/JOGT.2020.422013","article-title":"A review of application of machine learning in wireline logging formation evaluation","volume":"42","author":"Yang","year":"2020","journal-title":"J. Oil Gas Technol."},{"key":"ref_19","first-page":"2596","article-title":"Estimation of winter wheat biomass using visible spectral and BP based artificial neural networks","volume":"35","author":"Cui","year":"2015","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_20","first-page":"103","article-title":"Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data","volume":"25","author":"Feng","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Engin."},{"key":"ref_21","first-page":"1132","article-title":"Research on extraction of winter wheat based on random forest","volume":"33","author":"He","year":"2018","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_22","first-page":"21","article-title":"Review of time series prediction methods","volume":"46","author":"Yang","year":"2019","journal-title":"Comput. Sci."},{"key":"ref_23","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_24","first-page":"206","article-title":"Research on typical crop classification based on HJ-1A hyperspectral data in the Huangshui River Basin","volume":"32","author":"Shi","year":"2017","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.apsusc.2016.12.161","article-title":"The support vector machine method for RS images\u2019 classification in northwest arid area","volume":"42","author":"Zhang","year":"2017","journal-title":"Sci. Surv. Mapp."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mutanga, O., and Kumar, L. (2019). Google Earth Engine applications. Remote Sens., 11.","DOI":"10.3390\/rs11050591"},{"key":"ref_27","first-page":"804","article-title":"Big data methods for environmental data","volume":"33","author":"Wu","year":"2018","journal-title":"Bull. Chin. Acad. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xiao, W., Xu, S., and He, T. (2021). Mapping paddy rice with Sentinel-1\/2 and phenology-, object-based algorithm\u2014An implementation in Hangjiahu Plain in China using GEE platform. Remote Sens., 13.","DOI":"10.3390\/rs13050990"},{"key":"ref_29","first-page":"752","article-title":"Extraction of summer crop in Jiangsu based on Google Earth Engine","volume":"21","author":"He","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, A., Zhong, B., and Wu, J. (2019, January 5\u20137). Monitoring winter wheat in ShanDong province using Sentinel data and Google Earth Engine platform. Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE, Shanghai, China.","DOI":"10.1109\/Multi-Temp.2019.8866975"},{"key":"ref_31","first-page":"18","article-title":"Regional drought risk of winter wheat in Minhe County Qinghai Province","volume":"3","author":"Zhao","year":"2018","journal-title":"Sci. Technol. Qinghai Agri. Forest."},{"key":"ref_32","first-page":"6","article-title":"Effects of climate change on winter wheat in Minhe County","volume":"112","author":"Li","year":"2018","journal-title":"Sci. Technol. Qinghai Agri. Forest."},{"key":"ref_33","unstructured":"National Catalogue Service for Geographic Information (2021, January 11). 1:1,000,000 National Basic Geographic Database. Available online: https:\/\/www.webmap.cn\/commres.do?method=result100W."},{"key":"ref_34","first-page":"10","article-title":"Advances in study on vegetation indices","volume":"13","author":"Tian","year":"1998","journal-title":"Adv. Earth Sci."},{"key":"ref_35","first-page":"387","article-title":"Dynamic monitoring of vegetation fraction change in Jilin Province based on MODIS NDVI","volume":"25","author":"Miao","year":"2010","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_36","first-page":"705","article-title":"Spatiotemporal variation of vegetation coverage in Qinling-Daba Mountains in relation to environmental factors","volume":"70","author":"Liu","year":"2015","journal-title":"Acta Geogr. Sinica"},{"key":"ref_37","first-page":"807","article-title":"Variation of normalized difference vegetation index and its response to extreme climate in coastal China during 1982\u20132014","volume":"38","author":"Wang","year":"2019","journal-title":"Geogr. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105334","DOI":"10.1016\/j.compag.2020.105334","article-title":"A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms","volume":"172","author":"Costa","year":"2020","journal-title":"Comp. Elect. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dong, C., Zhao, G., Qin, Y., and Wan, H. (2019). Area extraction and spatiotemporal characteristics of winter wheat\u2013summer maize in Shandong Province using NDVI time series. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0226508"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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. Forest Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2019.03.028","article-title":"A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems","volume":"228","author":"Yang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"335","article-title":"Review on methods of remote sensing time-series data reconstruction","volume":"13","author":"Li","year":"2009","journal-title":"J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kai, Z., J\u00f6nsson, P., Jin, H., and Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sens., 9.","DOI":"10.3390\/rs9121271"},{"key":"ref_46","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_47","first-page":"331","article-title":"Monitoring multiple cropping index of Henan Province, China based on MODIS-EVI time series data and Savitzky-Golay Filtering algorithm","volume":"119","author":"Wang","year":"2019","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhang, X., Huang, Y., Hong, C., Zhang, Z., Huang, X., Zeng, J., Tang, J., and Zhang, R. (2019, January 21\u201322). Monitoring of winter wheat and summer corn phenology in Xiong\u2019an new area based on NDVI time series. Proceedings of the International Conference on Wireless Communication, Network and Multimedia Engineering (WCNME 2019), Guilin, China.","DOI":"10.2991\/wcnme-19.2019.52"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, J., Gao, X., Ma, H., Huang, H., Su, W., Meng, J., Li, Y., Chen, H., and Yin, D. (2020). Prediction of winter wheat maturity dates through assimilating remotely sensed leaf area index into crop growth model. Remote Sens., 12.","DOI":"10.3390\/rs12182896"},{"key":"ref_50","first-page":"1","article-title":"Survey on similarity measurement of time series data mining","volume":"32","author":"Chen","year":"2017","journal-title":"Cont. Decis."},{"key":"ref_51","first-page":"1114","article-title":"Research and implementation of similarity computation for spatiotemporal trajectories","volume":"48","author":"Tu","year":"2020","journal-title":"Comp. Digit. Engin."},{"key":"ref_52","first-page":"287","article-title":"Study on spatial-temporal multiscale adaptive method of gesture recognition","volume":"44","author":"Wang","year":"2017","journal-title":"Comp. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A time-weighted dynamic time warping method for land-use and land-cover mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Select. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Spackman, K.A. (1989). Signal detection theory: Valuable tools for evaluating inductive learning. Proceedings of the Sixth International Workshop on Machine Learning, Elsevier.","DOI":"10.1016\/B978-1-55860-036-2.50047-3"},{"key":"ref_56","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:28:41Z","timestamp":1760365721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020343"],"URL":"https:\/\/doi.org\/10.3390\/rs14020343","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}