{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:00:17Z","timestamp":1783184417304,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["42101352"],"award-info":[{"award-number":["42101352"]}],"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>Accurate determination of crop phenology information is essential for effective field management and decision-making processes. Remote sensing time series analyses are widely employed to extract the phenological stages. Each crop\u2019s phenological stage has its unique characteristic on the crop plant, while the satellite-derived crop phenology refers to some key transition dates in time series satellite observations. Current techniques primarily estimate specific phenological stages by detecting points with distinctive features on the remote sensing time series curve. But these stages may be different from the Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale, which is commonly used to identify the phenological development stages of crops. Moreover, when aiming to extract various phenological stages concurrently, it becomes necessary to adjust the extraction strategy for each unique feature. This need for distinct strategies at each stage heightens the complexity of simultaneous extraction. In this study, we utilize the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data and propose a phenology extraction framework based on the Derivative Dynamic Time Warping (DDTW) algorithm. This method is capable of simultaneously extracting complete phenological stages, and the results demonstrate that the Root Mean Square Errors (RMSEs, days) of detected phenology on the BBCH scale for corn were less than 6 days overall.<\/jats:p>","DOI":"10.3390\/rs15143456","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Corn Phenology Detection Using the Derivative Dynamic Time Warping Method and Sentinel-2 Time Series"],"prefix":"10.3390","volume":"15","author":[{"given":"Junyan","family":"Ye","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhua","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510631, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dairong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoxuan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased Plant Growth in the Northern High Latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3451","DOI":"10.1080\/014311699211499","article-title":"Surface Phenology and Satellite Sensor-Derived Onset of Greenness: An Initial Comparison","volume":"20","author":"Schwartz","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1029\/97GB00330","article-title":"A Continental Phenology Model for Monitoring Vegetation Responses to Interannual Climatic Variability","volume":"11","author":"White","year":"1997","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8379391","DOI":"10.34133\/2021\/8379391","article-title":"Mapping Crop Phenology in near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities","volume":"2021","author":"Gao","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.3354\/cr01411","article-title":"Relationships between the Evaporative Stress Index and Winter Wheat and Spring Barley Yield Anomalies in the Czech Republic","volume":"70","author":"Anderson","year":"2016","journal-title":"Clim. Res."},{"key":"ref_6","unstructured":"Walthall, C., Anderson, C., Takle, E., Baumgard, L., and Wright-Morton, L. (2013). Climate Change and Agriculture in the United States: Effects and Adaptation, Adventure Scientists."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.rse.2015.11.034","article-title":"The Evaporative Stress Index as an Indicator of Agricultural Drought in Brazil: An Assessment Based on Crop Yield Impacts","volume":"174","author":"Anderson","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.rse.2018.02.020","article-title":"Field-Scale Mapping of Evaporative Stress Indicators of Crop Yield: An Application over Mead, NE, USA","volume":"210","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1029\/1999GL011113","article-title":"Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity","volume":"27","author":"Asner","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1080\/01431160310001647705","article-title":"An AVHRR-Based Model of Groundnut Yields in the Peanut Basin of Senegal","volume":"25","author":"Knudby","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1890\/0012-9658(1997)078[0953:AEFNFT]2.0.CO;2","article-title":"ANPP Estimates from NDVI for the Central Grassland Region of the United States","volume":"78","author":"Paruelo","year":"1997","journal-title":"Ecology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s004840000062","article-title":"Phenology in Central Europe\u2014Differences and Trends of Spring Phenophases in Urban and Rural Areas","volume":"44","author":"Roetzer","year":"2000","journal-title":"Int. J. Biometeorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/319195a0","article-title":"Relationship between Atmospheric CO2 Variations and a Satellite-Derived Vegetation Index","volume":"319","author":"Tucker","year":"1986","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.isprsjprs.2022.12.025","article-title":"Near Real-Time Detection and Forecasting of within-Field Phenology of Winter Wheat and Corn Using Sentinel-2 Time-Series Data","volume":"196","author":"Liao","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.rse.2010.04.019","article-title":"A Two-Step Filtering Approach for Detecting Maize and Soybean Phenology with Time-Series MODIS Data","volume":"114","author":"Sakamoto","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108019","DOI":"10.1016\/j.agrformet.2020.108019","article-title":"Comparison of MODIS-Based Vegetation Indices and Methods for Winter Wheat Green-up Date Detection in Huanghuai Region of China","volume":"288","author":"Gan","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","first-page":"19","article-title":"Spatially Detailed Retrievals of Spring Phenology from Single-Season High-Resolution Image Time Series","volume":"59","author":"Vrieling","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1080\/01431160802549237","article-title":"Sensitivity of Vegetation Phenology Detection to the Temporal Resolution of Satellite Data","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.isprsjprs.2018.02.011","article-title":"Refined Shape Model Fitting Methods for Detecting Various Types of Phenological Information on Major US Crops","volume":"138","author":"Sakamoto","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2022.01.023","article-title":"Mapping Corn and Soybean Phenometrics at Field Scales over the United States Corn Belt by Fusing Time Series of Landsat 8 and Sentinel-2 Data with VIIRS Data","volume":"186","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"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":"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_25","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_26","doi-asserted-by":"crossref","unstructured":"Romani, L.A., Goncalves, R.R.V., Zullo, J., Traina, C., and Traina, A.J. (2010, January 25\u201330). New DTW-Based Method to Similarity Search in Sugar Cane Regions Represented by Climate and Remote Sensing Time Series. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652003"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","unstructured":"Csillik, O., Belgiu, M., Asner, G.P., and Kelly, M. (2019). Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11101257"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guan, X., Liu, G., Huang, C., Meng, X., Liu, Q., Wu, C., Ablat, X., Chen, Z., and Wang, Q. (2018). An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020075"},{"key":"ref_31","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_32","first-page":"72","article-title":"Phenology from Landsat When Data Is Scarce: Using MODIS and Dynamic Time-Warping to Combine Multi-Year Landsat Imagery to Derive Annual Phenology Curves","volume":"54","author":"Baumann","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","unstructured":"Huseby, R.B., Aurdal, L., Eikvil, L., Solberg, R., Vikhamar, D., and Solberg, A. (2005, January 16\u201318). Alignment of Growth Seasons from Satellite Data. Proceedings of the International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, MS, USA."},{"key":"ref_34","unstructured":"Geler, Z. (2015). Role of Similarity Measures in Time Series Analysis. [Ph.D. Thesis, University of Novi Sad (Serbia)]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Geler, Z., Kurbalija, V., Radovanovi\u0107, M., and Ivanovi\u0107, M. (2014, January 16\u201318). Impact of the Sakoe-Chiba Band on the DTW Time Series Distance Measure for k NN Classification. Proceedings of the Knowledge Science, Engineering and Management: 7th International Conference, KSEM 2014, Sibiu, Romania.","DOI":"10.1007\/978-3-319-12096-6_10"},{"key":"ref_36","unstructured":"Kurbalija, V., Radovanovi\u0107, M., Geler, Z., and Ivanovi\u0107, M. (2011). the Third International Conference on Software, Services and Semantic Technologies S3T 2011, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.knosys.2013.10.021","article-title":"The Influence of Global Constraints on Similarity Measures for Time-Series Databases","volume":"56","author":"Kurbalija","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cheng, K., and Wang, J. (2019). Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China. Multidiscip. Digit. Publ. Inst., 10.","DOI":"10.3390\/f10111040"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, F., Yang, G., Yang, X., Cen, H., Zhu, Y., Han, S., Yang, H., He, Y., and Zhao, C. (2021). Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data. Remote Sens., 13.","DOI":"10.3390\/rs13091836"},{"key":"ref_40","unstructured":"Kumar, V., and Grossman, R. (2011). Proceedings of the 2001 SIAM International Conference on Data Mining, SIAM."},{"key":"ref_41","unstructured":"Rath, T.M., and Manmatha, R. (2003, January 18\u201320). Word Image Matching Using Dynamic Time Warping. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_42","first-page":"17","article-title":"Validation of Development Models for Winter Cereals and Maize with Independent Agrophenological Observations in the BBCH Scale","volume":"14","author":"Ventura","year":"2009","journal-title":"Riv. Ital. Di Agrometeorol."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved Cloud and Cloud Shadow Detection in Landsats 4\u20138 and Sentinel-2 Imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Navarro, A., Rolim, J., Miguel, I., Catal\u00e3o, J., Silva, J., Painho, M., and Vekerdy, Z. (2016). Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements. Remote Sens., 8.","DOI":"10.3390\/rs8060525"},{"key":"ref_46","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_47","unstructured":"Ahn, B. (2023, July 06). Agriculture and Agri-Food Canada (AAFC). Available online: https:\/\/agriculture.canada.ca\/en."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1046\/j.1365-3180.1997.d01-70.x","article-title":"Use of the Extended BBCH Scale\u2014General for the Descriptions of the Growth Stages of Mono; and Dicotyledonous Weed Species","volume":"37","author":"Hess","year":"1997","journal-title":"Weed Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a Two-Band Enhanced Vegetation Index without a Blue Band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_51","first-page":"100414","article-title":"Limitations of Cloud Cover for Optical Remote Sensing of Agricultural Areas across South America","volume":"20","author":"Prudente","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud Cover throughout the Agricultural Growing Season: Impacts on Passive Optical Earth Observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3456\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:09:02Z","timestamp":1760126942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,8]]},"references-count":52,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143456"],"URL":"https:\/\/doi.org\/10.3390\/rs15143456","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,8]]}}}