{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T23:45:32Z","timestamp":1773445532652,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41474010"],"award-info":[{"award-number":["41474010"]}]},{"name":"National Natural Science Foundation of China","award":["61401509"],"award-info":[{"award-number":["61401509"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference\u2013similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH\/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.<\/jats:p>","DOI":"10.3390\/ijgi11080424","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T22:03:42Z","timestamp":1658873022000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Object-Based Automatic Mapping of Winter Wheat Based on Temporal Phenology Patterns Derived from Multitemporal Sentinel-1 and Sentinel-2 Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9312-3514","authenticated-orcid":false,"given":"Limei","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Xiong","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/978-3-662-09366-5_12","article-title":"Regeneration of Plants from Protoplasts of Triticum aestivum L. (Wheat)","volume":"Volume 29","author":"Bajaj","year":"1994","journal-title":"Plant Protoplasts and Genetic Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012010","DOI":"10.1088\/1755-1315\/427\/1\/012010","article-title":"Analysis of Climatic Potential Productivity and Wheat Production in Different Producing Areas of the Northern Hemisphere","volume":"427","author":"Li","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41437-020-0320-1","article-title":"Phenology and Related Traits for Wheat Adaptation","volume":"125","author":"Hyles","year":"2020","journal-title":"Heredity"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.fcr.2018.02.029","article-title":"Combined Effects of Drought and High Temperature on Photosynthetic Characteristics in Four Winter Wheat Genotypes","volume":"223","author":"Urban","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_5","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_6","unstructured":"G\u2019Oes, C., and Bekkers, E. (2022). The Impact of Geopolitical Conflicts on Trade, Growth, and Innovation. arXiv."},{"key":"ref_7","first-page":"61","article-title":"A Soil Layered Water Budget Model for Winter Wheat and Summer Maize","volume":"1","author":"Gong","year":"1995","journal-title":"Acta Agric. Univ. Pekin."},{"key":"ref_8","first-page":"15","article-title":"Estimation of Crop Production and CO2 Fluxes Using Remote Sensing: Application to a Winter Wheat\/Sunflower Rotation","volume":"4","author":"Pique","year":"2021","journal-title":"Environ. Sci. Proc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.5958\/2320-642X.2015.00003.4","article-title":"Soil Organic Carbon: Towards Better Soil Health, Productivity and Climate Change Mitigation","volume":"3","author":"Deb","year":"2015","journal-title":"Clim. Chang. Environ. Sustain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01431160701250390","article-title":"The Use of High-Resolution Image Time Series for Crop Classification and Evapotranspiration Estimate over an Irrigated Area in Central Morocco","volume":"29","author":"Simonneaux","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2015.04.019","article-title":"Land Surface Phenology along Urban to Rural Gradients in the U.S. Great Plains","volume":"165","author":"Walker","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2015.05.011","article-title":"Mapping Paddy Rice Planting Areas Through Time Series Analysis of MODIS Land Surface Temperature and Vegetation Index Data","volume":"106","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2016.05.014","article-title":"Automated Mapping of Soybean and Corn Using Phenology","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jag.2007.11.002","article-title":"Crop Discrimination in Northern China with Double Cropping Systems using Fourier Analysis of Time-Series MODIS Data","volume":"10","author":"Zhang","year":"2008","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kocian, A., Carmassi, G., Cela, F., Incrocci, L., Milazzo, P., and Chessa, S. (2020). Bayesian Sigmoid-type Time Series Forecasting with Missing Data for Greenhouse Crops. Sensors, 20.","DOI":"10.3390\/s20113246"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Caballero, G.R., Platzeck, G., Pezzola, A., Casella, A., and Delegido, J. (2020). Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach. Agronomy, 10.","DOI":"10.3390\/agronomy10060845"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s12665-019-8654-9","article-title":"Incorporation of Textural Information with SAR and Optical Imagery for Improved Land Cover Mapping","volume":"78","author":"Muthukumarasamy","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_18","first-page":"1183","article-title":"Multi-temporal SAR and Optical Data Fusion with Texture Measures for Landcover Classification Based on the Bayesian Theory","volume":"5","author":"Chureesampant","year":"2008","journal-title":"ISPRS. SC. Newlett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, C., Qi, B., Liu, H., Guo, D., and Shao, Y. (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040561"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-Oriented Crop Mapping and Monitoring using Multi-Temporal Polarimetric Radarsat-2 Data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102446","DOI":"10.1016\/j.jag.2021.102446","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 Observ. Geoinform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta\u2013Analysis And Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_24","first-page":"4133","article-title":"Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis","volume":"48","author":"Cho","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.3390\/rs13173433","article-title":"Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms","volume":"13","author":"Verrelst","year":"2021","journal-title":"Remote Sens."},{"key":"ref_26","first-page":"315","article-title":"K-Means and ISODATA Clustering Algorithms for Landcover Classification using Remote Sensing","volume":"48","author":"Abbas","year":"2016","journal-title":"Sindh Univ. Res. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2017.08.036","article-title":"An Evaluation of Monthly Impervious Surface Dynamics by Fusing Landsat and MODIS Time Series in the Pearl River Delta, China, from 2000 To 2015","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2018.09.008","article-title":"Tracking Annual Cropland Changes from 1984 to 2016 using Time-Series Landsat Images with a Change-Detection and Post-Classification Approach: Experiments from Three Sites in Africa","volume":"218","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Zhang, B., and Liu, M. (2018). Crop Classification Based on Temporal Information using Sentinel-1 SAR Time-Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11010053"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1134\/S1064230714040169","article-title":"Similarity Measures and Comparison Metrics for Image Shapes","volume":"53","author":"Vizilter","year":"2014","journal-title":"J. Comput. Syst. Sci. Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1175\/1520-0442(2001)014<2790:CODMIC>2.0.CO;2","article-title":"Choice of Distance Matrices in Cluster Analysis: Defining Regions","volume":"14","author":"Mimmack","year":"2010","journal-title":"J. Clim."},{"key":"ref_32","first-page":"27","article-title":"A New K-mean Color Image Segmentation with Cosine Distance for Satellite Images","volume":"1","author":"Modh","year":"2012","journal-title":"IJEAT"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature Selection of Time Series MODIS Data for Early Crop Classification using Random Forest: A Case Study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Ortega-Terol, D., Hern\u00e1ndez-L\u00f3pez, D., Ballesteros, R., and Moreno, M.A. (2020). Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification using Landsat-8 and Sentinel-2 Data. Remote Sens., 12.","DOI":"10.3390\/rs12111735"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ma, Z., Liu, Z., Zhao, Y., Zhang, L., and Li, S. (2020). An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS J. Photogramm. Remote Sens., 9.","DOI":"10.3390\/ijgi9110648"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10104","DOI":"10.3390\/app112110104","article-title":"Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping","volume":"11","author":"Zhao","year":"2021","journal-title":"Appl. Sci."},{"key":"ref_37","first-page":"11150411","article-title":"Terrasar-X and Radarsat-2 for Crop Classification and Acreage Estimation","volume":"2","author":"Mcnairn","year":"2009","journal-title":"IGARSS"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1016\/S2095-3119(19)62615-8","article-title":"Winter Wheat Identification by Integrating Spectral and Temporal Information Derived from Multi-Resolution Remote Sensing Data","volume":"18","author":"Zhang","year":"2019","journal-title":"J. Integr. Agr."},{"key":"ref_39","first-page":"9","article-title":"Area Extraction and Interannual Variation Monitoring of Winter Wheat in Counties Based on GF-1 Satellite","volume":"49","author":"Zuo","year":"2019","journal-title":"J. Henan Univ. (Nat. Sci.)"},{"key":"ref_40","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_41","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. Agr."},{"key":"ref_42","first-page":"7","article-title":"Planting Area Extraction of Winter Wheat Based on Multi-Temporal SAR Data and Optical Imagery","volume":"33","author":"Zhou","year":"2017","journal-title":"Trans. CSAE"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., OdongoBraun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and Polygons using Simple Non-Iterative Clustering. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, L., Abubakar, G.A., and Huang, J. (2021). High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13061148"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106449","DOI":"10.1016\/j.cmpb.2021.106449","article-title":"Estimators and Confidence Intervals of f2 Using Bootstrap Methodology for the Comparison of Dissolution Profiles","volume":"212","author":"Zxa","year":"2021","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hajj, M.E., Baghdadi, N., Bazzi, H., and Zribi, M. (2019). Penetration Analysis of SAR Signals in the C And L Bands for Wheat, Maize, and Grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11010031"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"13","DOI":"10.11648\/j.ajrs.20190701.13","article-title":"A Microwave Scattering Model for Simulating the C-Band SAR Backscatter of Wheat Canopy","volume":"7","author":"Yan","year":"2019","journal-title":"Ame. J. Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:54Z","timestamp":1760140614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["ijgi11080424"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11080424","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,26]]}}}