{"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":1784803735623,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,8]],"date-time":"2016-01-08T00:00:00Z","timestamp":1452211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Programme of the Chinese Academy of Sciences","award":["KZZD-EW-08"],"award-info":[{"award-number":["KZZD-EW-08"]}]},{"name":"NSFC-UNEP International Cooperation Programe","award":["4151101069"],"award-info":[{"award-number":["4151101069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data has been widely used in the fields of crop and rice classification. The cloudy and rainy weather characteristics of the monsoon season greatly reduce the likelihood of obtaining high-quality optical remote sensing images. In addition, the diverse crop-planting system in Vietnam also hinders the comparison of NDVI among different crop stages. To address these problems, we apply a Dynamic Time Warping (DTW) distance-based similarity measure approach and use the entire yearly NDVI time series to reduce the inaccuracy of classification using a single image. We first de-noise the NDVI time series using S-G filtering based on the TIMESAT software. Then, a standard NDVI time-series base for rice growth is established based on field survey data and Google Earth sample data. NDVI time-series data for each pixel are constructed and the DTW distance with the standard rice growth NDVI time series is calculated. Then, we apply thresholds to extract rice growth areas. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice-cropping map reveal a high mapping accuracy at the national scale between the statistical data, with the corresponding R2 being as high as 0.809; however, the mapped rice accuracy decreased at the provincial scale due to the reduced number of rice planting areas per province. An analysis of the results indicates that the 500-m resolution MODIS data are limited in terms of mapping scattered rice parcels. The results demonstrate that the DTW-based similarity measure of the NDVI time series can be effectively used to map large-area rice cropping systems with diverse cultivation processes.<\/jats:p>","DOI":"10.3390\/rs8010019","type":"journal-article","created":{"date-parts":[[2016,1,8]],"date-time":"2016-01-08T23:38:27Z","timestamp":1452296307000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":163,"title":["Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0030-2335","authenticated-orcid":false,"given":"Xudong","family":"Guan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaohuan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6953-1916","authenticated-orcid":false,"given":"Xuelian","family":"Meng","sequence":"additional","affiliation":[{"name":"Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingsheng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,8]]},"reference":[{"key":"ref_1","first-page":"454","article-title":"Rice in the global food supply","volume":"502","author":"Fairhurst","year":"2002","journal-title":"World"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1080\/01431161.2012.738946","article-title":"Remote sensing of rice crop areas","volume":"34","author":"Kuenzer","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","unstructured":"Ricepedia The Online Authority on Rice: Rice Species. Available online: http:\/\/ricepedia.org\/."},{"key":"ref_4","unstructured":"General Statistics Office of Vietnam Statistical Yearbook of Vietnam, Available online: http:\/\/www.gso.gov.vn\/."},{"key":"ref_5","first-page":"143","article-title":"Crop modeling and remote-sensing for yield prediction","volume":"43","author":"Bouman","year":"1995","journal-title":"Neth. J. Agric. Sci."},{"key":"ref_6","unstructured":"Thiruvengadachari, S., and Sakhtivadivel, R. (1997). Satellite Remote Sensing for Assessment of Irrigation System Performance: A Case Study in India, IWMI Research Report."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0378-4290(97)00064-6","article-title":"Monitoring rice reflectance at field level for estimating biomass and lai","volume":"55","author":"Casanova","year":"1998","journal-title":"Field Crop. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0034-4257(98)00081-9","article-title":"The propagation of foliar biochemical absorption features in forest canopy reflectance: A theoretical analysis","volume":"67","author":"Dawson","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"872","DOI":"10.2134\/agronj2004.0162","article-title":"Predicting rice yield using canopy reflectance measured at booting stage","volume":"97","author":"Chang","year":"2005","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of spectral remote sensing for agronomic decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s11430-009-0094-z","article-title":"A scheme for regional rice yield estimation using ENVISAT ASAR data","volume":"52","author":"Shen","year":"2009","journal-title":"Sci. China Ser. D-Earth Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/014311698216134","article-title":"Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year","volume":"19","author":"Fang","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2005.08.012","article-title":"Spatial and temporal patterns of China\u2019s cropland during 1990\u20132000: An analysis based on Landsat-TM data","volume":"98","author":"Liu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/014311698216404","article-title":"Classification of multi-temporal Spot-XS satellite data for mapping rice fields on a west African floodplain","volume":"19","author":"Turner","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0924-2716(97)83003-1","article-title":"Mapping of crop rotation using multi-date Indian remote sensing satellite digital data","volume":"52","author":"Panigrahy","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01431169208904046","article-title":"Linear mixture modelling applied to AVHRR data for crop area estimation","volume":"13","author":"Quarmby","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Q.Z., Zhang, H.X., Du, X., Wen, N., and Tao, Q.S. (2014). County-level rice area estimation in southern China using remote sensing data. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.083657"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Roberts, D.A. (2002). Large area mapping of land-cover change in Rond\u00f4nia using multi-temporal spectral mixture analysis and decision tree classifiers. J. Geophys. Res., 107.","DOI":"10.1029\/2001JD000374"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetation sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.007","article-title":"Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010","volume":"91","author":"Gumma","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.asr.2011.09.011","article-title":"Monitoring of rice cropping intensity in the upper Mekong delta, Vietnam using time-series MODIS data","volume":"49","author":"Chen","year":"2012","journal-title":"Adv. Space Res."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1080\/01431160500300297","article-title":"Classifying rangeland vegetation type and coverage from NDVI time series using Fourier filtered cycle similarity","volume":"26","author":"Geerken","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Keogh, E.J., and Pazzani, M.J. (2001, January 21). Derivative dynamic time warping. Proceedings of the SIAM International Conference on Data Mining, Columbus, OH, USA.","DOI":"10.1137\/1.9781611972719.1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","article-title":"Exact indexing of dynamic time warping","volume":"7","author":"Keogh","year":"2005","journal-title":"Knowl. Inf. Syst."},{"key":"ref_28","unstructured":"Berndt, D.J., and Clifford, J. (1994, January 12). Using Dynamic Time Warping to Find Patterns in Time Series. Proceedings of the KDD Workshop, Seattle, WA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ins.2010.09.024","article-title":"Discovering multi-label temporal patterns in sequence databases","volume":"181","author":"Chen","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_30","first-page":"31","article-title":"Subsequence matching under time warping in time-series databases: Observation, optimization, and performance results","volume":"23","author":"Kim","year":"2008","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.csl.2015.04.002","article-title":"Text-to-speech synthesis system with Arabic diacritic recognition system","volume":"34","author":"Rebai","year":"2015","journal-title":"Comput. Speech Lang."},{"key":"ref_32","unstructured":"Dockstader, S.L., Bergkessel, K.A., and Tekalp, A.M. (2002, January 15). Feature extraction for the analysis of gait and human motion. Proceedings of the 16th International Conference on Pattern Recognition, Quebec, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1016\/j.ins.2009.02.016","article-title":"Mining frequent trajectory patterns in spatial-temporal databases","volume":"179","author":"Lee","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2011.04.015","article-title":"Shape-based template matching for time series data","volume":"26","author":"Niennattrakul","year":"2012","journal-title":"Knowl. Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","article-title":"A global averaging method for dynamic time warping, with applications to clustering","volume":"44","author":"Petitjean","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.engappai.2014.12.015","article-title":"Fuzzy clustering of time series data using dynamic time warping distance","volume":"39","author":"Izakian","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.knosys.2014.12.003","article-title":"Support vector-based algorithms with weighted dynamic time warping kernel function for time series classification","volume":"75","author":"Jeong","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Maclean, J.L., Dawe, D.C., Hardy, B., and Hettel, G.P. (2002). Rice Almanac: Source Book for the Most Important Economic Activity on Earth, CABI Publishing. [2nd ed.].","DOI":"10.1079\/9780851996363.0000"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.landurbplan.2009.02.002","article-title":"Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery","volume":"92","author":"Toshihiro","year":"2009","journal-title":"Landscape Urban Plan."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs6010135","article-title":"A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong delta, Vietnam","volume":"6","author":"Son","year":"2013","journal-title":"Remote Sens."},{"key":"ref_42","unstructured":"Greg, E. LAADS Web, Available online: http:\/\/ladsweb.nascom.nasa.gov\/."},{"key":"ref_43","unstructured":"U.S. Department of the Interior, and U.S. Geological Survey MOD09A1|LP DAAC: NASA Land Data Products and Services, Available online: http:\/\/lpdaac.usgs.gov\/dataset_discovery\/modis\/modis_products_table\/mod09a1\/."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1016\/j.rse.2011.06.020","article-title":"A comparison of time series similarity measures for classification and change detection of ecosystem dynamics","volume":"115","author":"Lhermitte","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","unstructured":"CGIAR-CSI SRTM 90m DEM Digital Elevation Database. Available online: http:\/\/srtm.csi.cgiar.org\/."},{"key":"ref_47","unstructured":"Statistical Documentation and Service Centre\u2014General Statistics Office of Vietnam, Available online: http:\/\/www.gso.gov.vn\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2730","DOI":"10.1080\/01431161.2012.750037","article-title":"Comparison and enhancement of MODIS cloud mask products for Southeast Asia","volume":"34","author":"Leinenkugel","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The best index slope extraction (BISE)\u2014A method for reducing noise in NDVI time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.asr.2005.08.037","article-title":"Reconstructing pathfinder AVHRR land NDVI time-series data for the northwest of China","volume":"37","author":"Ma","year":"2006","journal-title":"Adv. Space Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.asr.2009.05.009","article-title":"A simplified data assimilation method for reconstructing time-series MODIS NDVI data","volume":"44","author":"Gu","year":"2009","journal-title":"Adv. Space Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., and Liu, H.H. (1998, January 8). The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, London, UK.","DOI":"10.1098\/rspa.1998.0193"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2008.09.003","article-title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","volume":"113","author":"Hird","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","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-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","article-title":"Weighted dynamic time warping for time series classification","volume":"44","author":"Jeong","year":"2011","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/1\/19\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:17:28Z","timestamp":1760210248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/1\/19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,8]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["rs8010019"],"URL":"https:\/\/doi.org\/10.3390\/rs8010019","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,8]]}}}