{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T19:40:16Z","timestamp":1775677216624,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["2020B0301030004"],"award-info":[{"award-number":["2020B0301030004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31860144"],"award-info":[{"award-number":["31860144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sugarcane is an important sugar and biofuel crop with high socio-economic importance, and its planted area has increased rapidly in recent years. China is the world\u2019s third or fourth sugarcane producer. However, to our knowledge, no study has investigated the mapping of sugarcane cultivation areas across entire China. In this study, we developed a phenology-based method to identify sugarcane plantations in China at 30-m spatial resolution from 2016\u20132020 using the time-series of Landsat and Sentinel-1\/2 images derived from Google Earth Engine (GEE) platform. The method worked by comparing the phenological similarity in normalized difference vegetation index (NDVI) series between unknown pixels and sugarcane samples. The phenological similarity was assessed using the time-weighted dynamic time warping method (TWDTW), which has less sensitivity to training samples than machine learning methods and therefore can be easily applied to large areas with limited samples. More importantly, our method introduced multiple and moving time standard phenological curves of sugarcane to the TWDTW by fully considering the variable crop life-cycle of sugarcane, particularly its long harvest season spanning from December to March of the following year. Validations showed the method performed well in 2019, with overall accuracies of 93.47% and 92.74% for surface reflectance (SR) and top of atmosphere reflectance (TOA) data, respectively. The sugarcane maps agreed well with the agricultural statistical areas from 2016\u20132020. The mapping accuracies using TOA data were comparable to SR data in 2019\u20132020, but outperformed SR data in 2016\u20132018 when SR data had lower availability on GEE. The sugarcane maps produced in this study can be used to monitor growing conditions and production of sugarcane and, therefore, can benefit sugarcane management, sustainable sugarcane production, and national food security.<\/jats:p>","DOI":"10.3390\/rs14051274","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"1274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets"],"prefix":"10.3390","volume":"14","author":[{"given":"Yi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoting","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baihong","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangrong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Geomatics & Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangqian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenping","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1080\/01431160701874603","article-title":"The application of remote sensing techniques to sugarcane (Saccharum spp. Hybrid) production: A review of the literature","volume":"29","author":"Ahmed","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1079\/IVP2005643","article-title":"Sugarcane biotechnology: The challenges and opportunities","volume":"41","author":"Lakshmanan","year":"2005","journal-title":"Vitr. Cell. Dev. Biol.-Plant"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.5958\/0976-0741.2015.00022.7","article-title":"Agronomy of sugarbeet cultivation\u2014A review","volume":"36","author":"Brar","year":"2015","journal-title":"Agric. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.renene.2016.02.057","article-title":"Bioconversion of sugarcane crop residue for value added products\u2014An overview","volume":"98","author":"Sindhu","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.biombioe.2013.08.040","article-title":"Greenhouse gas mitigation potential from green harvested sugarcane scenarios in sao paulo state, Brazil","volume":"59","author":"Bordonal","year":"2013","journal-title":"Biomass Bioenergy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.rser.2015.07.137","article-title":"Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane (Saccharum officinarum) plantation in South-Central Brazil","volume":"52","author":"Bordonal","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/nclimate3410","article-title":"Brazilian sugarcane ethanol as an expandable green alternative to crude oil use","volume":"7","author":"Jaiswal","year":"2017","journal-title":"Nat. Clim. Change"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.apenergy.2008.11.025","article-title":"Good or bad bioethanol from a greenhouse gas perspective\u2014What determines this?","volume":"86","author":"Borjesson","year":"2009","journal-title":"Appl. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"574","DOI":"10.3390\/su4040574","article-title":"Remote sensing time series to evaluate direct land use change of recent expanded sugarcane crop in brazil","volume":"4","author":"Adami","year":"2012","journal-title":"Sustainability"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.foodpol.2018.06.005","article-title":"Rapid expansion of sugarcane crop for biofuels and influence on food production in the first producing region of brazil","volume":"79","author":"Defante","year":"2018","journal-title":"Food Policy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10113-014-0652-6","article-title":"Forest dynamics and land-use transitions in the brazilian atlantic forest: The case of sugarcane expansion","volume":"15","author":"Ferreira","year":"2015","journal-title":"Reg. Environ. Chang."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1038\/nclimate2239","article-title":"Payback time for soil carbon and sugar-cane ethanol","volume":"4","author":"Mello","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/nclimate1067","article-title":"Direct impacts on local climate of sugar-cane expansion in brazil","volume":"1","author":"Loarie","year":"2011","journal-title":"Nat. Clim. Change"},{"key":"ref_14","unstructured":"FAOSTAT, Food and Agriculture Organization of the United Nations (FAO) (2020). FAO Statistical Databases, FAO."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"de Oliveira, A. (2018). Sugarcane Production in China. Sugarcane: Technology and Research, IntechOpen.","DOI":"10.5772\/intechopen.69564"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/TGRS.2009.2015769","article-title":"Monitoring sugarcane growth using envisat asar data","volume":"47","author":"Lin","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"76","article-title":"Study on the extraction of sugarcane planting areas from eos\/modis data","volume":"33","author":"Tan","year":"2007","journal-title":"Meteorol. Mon."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"044514","DOI":"10.1117\/1.JRS.13.044514","article-title":"Capability of multidate radarsat-2 data to identify sugarcane lodging","volume":"13","author":"Li","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Som-ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., and Immitzer, M. (2021). Remote sensing applications in sugarcane cultivation: A review. Remote Sens., 13.","DOI":"10.3390\/rs13204040"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1590\/1807-1929\/agriambi.v21n6p427-432","article-title":"Mapping of sugarcane crop area in the parana state using landsat\/tm\/oli and irs\/liss-3 images","volume":"21","author":"Johann","year":"2017","journal-title":"Rev. Bras. Eng. Agric. E Ambient."},{"key":"ref_21","first-page":"218","article-title":"An ensemble pansharpening approach for finer-scale mapping of sugarcane with landsat 8 imagery","volume":"33","author":"Johnson","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mulyono, S. (2016, January 17\u201319). Identifying sugarcane plantation using landsat-8 images with support vector machines. Proceedings of the 2nd International Conference of Indonesian Society for Remote Sensing, Yogyakarta, Indonesia.","DOI":"10.1088\/1755-1315\/47\/1\/012008"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1080\/01431160500296735","article-title":"Multi-temporal analysis of modis data to classify sugarcane crop","volume":"27","author":"Xavier","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.rse.2009.04.009","article-title":"Integrating spot-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices\u2014The case of sugarcane harvest on reunion island","volume":"113","author":"Begue","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.compag.2010.12.012","article-title":"Evaluating high resolution spot 5 satellite imagery for crop identification","volume":"75","author":"Yang","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Huang, J., Wang, J., Zhang, K., Kuang, Z., Zhong, S., and Song, X. (2015). Object-oriented classification of sugarcane using time-series middle-resolution remote sensing data based on adaboost. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142069"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s12517-016-2815-x","article-title":"Sugarcane crop identification from liss iv data using isodata, mlc, and indices based decision tree approach","volume":"10","author":"Verma","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/LGRS.2014.2372037","article-title":"Sugarcane mapping in tillering period by quad-polarization terrasar-x data","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.rse.2009.04.005","article-title":"Potential of sar sensors terrasar-x, asar\/envisat and palsar\/alos for monitoring sugarcane crops on reunion island","volume":"113","author":"Baghdadi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, D., Jing, W., Xu, J., Huang, J., Yang, J., and Chen, S. (2019). Early season mapping of sugarcane by applying machine learning algorithms to sentinel-1a\/2 time series data: A case study in zhanjiang city, China. Remote Sens., 11.","DOI":"10.3390\/rs11070861"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Virnodkar, S.S., Pachghare, V.K., Patil, V.C., and Jha, S.K. (2020). Application of Machine Learning on Remote Sensing Data for Sugarcane Crop Classification: A Review. ICT Analysis and Applications, Springer.","DOI":"10.1007\/978-981-15-0630-7_55"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring us agriculture: The us department of agriculture, national agricultural statistics service, cropland data layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.3390\/rs2041057","article-title":"Studies on the rapid expansion of sugarcane for ethanol production in sao paulo state (Brazil) using landsat data","volume":"2","author":"Sugawara","year":"2010","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2018.06.017","article-title":"Generalized space-time classifiers for monitoring sugarcane areas in brazil","volume":"215","author":"Rocha","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"127","article-title":"A generalized space-time obia classification scheme to map sugarcane areas at regional scale, using landsat images time-series and the random forest algorithm","volume":"80","author":"Rocha","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6892","DOI":"10.1080\/01431161.2020.1755738","article-title":"A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data","volume":"41","author":"Dong","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"Modis phenology-derived, multi-year distribution of conterminous us crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111951","DOI":"10.1016\/j.rse.2020.111951","article-title":"Mapping sugarcane plantation dynamics in guangxi, china, by time series sentinel-1, sentinel-2 and landsat images","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series modis 250 m vegetation index data for crop classification in the US central great plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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_43","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. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pan, B., Zheng, Y., Shen, R., Ye, T., Zhao, W., Dong, J., Ma, H., and Yuan, W. (2021). High resolution distribution dataset of double-season paddy rice in china. Remote Sens., 13.","DOI":"10.3390\/rs13224609"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite image time series analysis under time warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping rice cropping systems in vietnam using an ndvi-based time-series similarity measurement based on dtw distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_47","first-page":"268","article-title":"Vegetable classification in indonesia using dynamic time warping of sentinel-1a dual polarization sar time series","volume":"78","author":"Li","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","unstructured":"National Bureau of Statistics of China National Statistical Yearbook 2017, 2018, 2019, 2020, 2017\u20132020."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.06.014","article-title":"A robust method for reconstructing global modis evi time series on the google earth engine","volume":"155","author":"Kong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Abramov, S., Rubel, O., Lukin, V., Kozhemiakin, R., Kussul, N., Shelestov, A., and Lavreniuk, M. (2017, January 23\u201328). Speckle reducing for sentinel-1 sar data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127463"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_53","first-page":"21","article-title":"Imagens de sat\u00e9lite no mapeamento e estimativa de \u00e1rea de cana-de-a\u00e7\u00facar em S\u00e3o Paulo: Ano-safra 2003\/04","volume":"52","author":"Berka","year":"2005","journal-title":"Agric. S\u00e3o Paulo"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and change detection using landsat tm data: When and how to correct atmospheric effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of landsat and modis imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"96095","DOI":"10.1117\/1.JRS.9.096095","article-title":"Spatiotemporal image-fusion model for enhancing the temporal resolution of landsat-8 surface reflectance images using modis images","volume":"9","author":"Hazaymeh","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal modis-landsat data fusion for relative radiometric normalization, gap filling, and prediction of landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:39Z","timestamp":1760135559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":58,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051274"],"URL":"https:\/\/doi.org\/10.3390\/rs14051274","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,5]]}}}