{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:34:16Z","timestamp":1774935256952,"version":"3.50.1"},"reference-count":101,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fondation de France and the French Space Agency (CNES)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finist\u00e8re with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH\/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH\/VV, and finally, heading in VV and VH polarizations.<\/jats:p>","DOI":"10.3390\/rs14184437","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9197-522X","authenticated-orcid":false,"given":"Guanyao","family":"Xie","sequence":"first","affiliation":[{"name":"Laboratory LETG-Brest, G\u00e9omer, UMR 6554 CNRS, IUEM UBO, 29200 Brest, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1141-3233","authenticated-orcid":false,"given":"Simona","family":"Niculescu","sequence":"additional","affiliation":[{"name":"Laboratory LETG-Brest, G\u00e9omer, UMR 6554 CNRS, IUEM UBO, 29200 Brest, France"},{"name":"Department of Geography, University of Western Brittany, 3 Rue des Archives, 29238 Brest, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111814","DOI":"10.1016\/j.rse.2020.111814","article-title":"Sentinel-1 Time Series Data for Monitoring the Phenology of Winter Wheat","volume":"246","author":"Schlund","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2018.02.050","article-title":"Mapping Agricultural Land Abandonment from Spatial and Temporal Segmentation of Landsat Time Series","volume":"210","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-Scale Soybean Mapping and Area Estimation in the United States Using Medium Resolution Satellite Imagery and Field Survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, C., Bian, Y., Zhou, T., and Pan, J. (2019). Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors, 19.","DOI":"10.3390\/s19102401"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0168-1699(95)00049-6","article-title":"Comparison of Sensors and Techniques for Crop Yield Mapping","volume":"14","author":"Birrell","year":"1996","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI Time Series for Crop Mapping in the State of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6472","DOI":"10.3390\/rs6076472","article-title":"Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa","volume":"6","author":"Forkuor","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xie, G., and Niculescu, S. (2021). Remote Sensing Mapping and Monitoring of Land Cover\/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-Classification Comparison (PCC). Remote Sens., 13.","DOI":"10.3390\/rs13193899"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Zhou, T., Pan, J., Zhang, P., Wei, S., and Han, T. (2017). Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors, 17.","DOI":"10.3390\/s17061210"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-Area Crop Mapping Using Time-Series MODIS 250 m NDVI Data: An Assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Lu, Z., Li, S., Lei, Y., Chu, Q., Yin, X., and Chen, F. (2020). Large-Scale and High-Resolution Crop Mapping in China Using Sentinel-2 Satellite Imagery. Agriculture, 10.","DOI":"10.3390\/agriculture10100433"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TGRS.2003.817200","article-title":"Wheat Cycle Monitoring Using Radar Data and a Neural Network Trained by a Model","volume":"42","author":"Ferrazzoli","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ibrahim, E.S., Rufin, P., Nill, L., Kamali, B., Nendel, C., and Hostert, P. (2021). Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13173523"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5721\/EuJRS20124535","article-title":"Evaluation of Random Forest Method for Agricultural Crop Classification","volume":"45","author":"Ok","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compag.2015.05.001","article-title":"Crop Classification of Upland Fields Using Random Forest of Time-Series Landsat 7 ETM+ Data","volume":"115","author":"Tatsumi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","first-page":"587","article-title":"Assessment of Sentinel-1A Data for Rice Crop Classification Using Random Forests and Support Vector Machines","volume":"33","author":"Son","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","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_21","first-page":"102032","article-title":"Crop Classification from Full-Year Fully-Polarimetric L-Band UAVSAR Time-Series Using the Random Forest Algorithm","volume":"87","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"683","DOI":"10.5194\/isprs-archives-XLII-5-683-2018","article-title":"Crop classification on single date sentinel-2 imagery using random forest and suppor vector machine","volume":"XLII-5","author":"Saini","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_23","first-page":"884","article-title":"Comparing Object-Based and Pixel-Based Classifications for Mapping Savannas","volume":"13","author":"Whiteside","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","first-page":"C7","article-title":"Object-Based Classification vs. Pixel-Based Classification: Comparitive Importance of Multi-Resolution Imagery","volume":"38","author":"Weih","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nasrallah, A., Baghdadi, N., El Hajj, M., Darwish, T., Belhouchette, H., Faour, G., Darwich, S., and Mhawej, M. (2019). Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping. Remote Sens., 11.","DOI":"10.3390\/rs11192228"},{"key":"ref_26","first-page":"188","article-title":"Mapping Crop Phenology Using NDVI Time-Series Derived from HJ-1 A\/B Data","volume":"34","author":"Pan","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","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\u2013289","author":"Gan","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112232","DOI":"10.1016\/j.rse.2020.112232","article-title":"Comparing Land Surface Phenology of Major European Crops as Derived from SAR and Multispectral Data of Sentinel-1 and -2","volume":"253","author":"Meroni","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wali, E., Tasumi, M., and Moriyama, M. (2020). Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology\u2014Case Study in Miyazaki, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12010189"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2017.07.031","article-title":"Tracking Crop Phenological Development Using Multi-Temporal Polarimetric Radarsat-2 Data","volume":"210","author":"Canisius","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual Polarimetric Radar Vegetation Index for Crop Growth Monitoring Using Sentinel-1 SAR Data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"(2022, August 21). G\u00e9oportail, Available online: https:\/\/www.geoportail.gouv.fr\/."},{"key":"ref_33","unstructured":"Rouault, S. (2022, August 21). Observer L\u2019occupation des Sols Pour Guider les Politiques D\u2019am\u00e9nagement (MOS). Available online: https:\/\/www.adeupa-brest.fr\/nos-publications\/observer-loccupation-des-sols-pour-guider-les-politiques-damenagement-mos-0."},{"key":"ref_34","unstructured":"(2022, August 21). Agence d\u2019Urbanisme Brest Bretagne|ADEUPa Brest. Available online: https:\/\/adeupa-brest.fr\/."},{"key":"ref_35","unstructured":"(2022, August 22). Chambres d\u2019Agriculture de Bretagne. Available online: http:\/\/www.chambres-agriculture-bretagne.fr\/synagri\/accueilRegion."},{"key":"ref_36","unstructured":"(2022, August 18). Sentinel-2\u2014Missions\u2014Sentinel Online\u2014Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_37","unstructured":"(2022, August 18). Sentinel-1\u2014Missions\u2014Sentinel Online\u2014Sentinel Online. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/missions\/sentinel-1."},{"key":"ref_38","unstructured":"(2022, August 18). Registre Parcellaire Graphique (RPG), Available online: https:\/\/artificialisation.developpement-durable.gouv.fr\/bases-donnees\/registre-parcellaire-graphique."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A Review of Vegetation Indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_41","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press. [5th ed.]."},{"key":"ref_42","unstructured":"Rouse, J.W., Haas, R.H., Scheel, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the 3rd Earth Resource Technology Satellite (ERTS) Symposium, Washington, DC, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"14428","DOI":"10.3390\/rs71114428","article-title":"Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing","volume":"7","author":"Mulianga","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Valero, S., Arnaud, L., Planells, M., and Ceschia, E. (2021). Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13234891"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yin, L., You, N., Zhang, G., Huang, J., and Dong, J. (2020). Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12010162"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_48","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_49","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_50","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1017\/S0021859617000879","article-title":"Classification of Multi-Temporal Spectral Indices for Crop Type Mapping: A Case Study in Coalville, UK","volume":"156","author":"Palchoudhuri","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Samasse, K., Hanan, N.P., Anchang, J.Y., and Diallo, Y. (2020). A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12091436"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Singh, M., Gupta, P.K., Tyagi, V., Flusser, J., \u00d6ren, T., and Valentino, G. (2020). Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique. International Conference on Advances in Computing and Data Sciences, Springer.","DOI":"10.1007\/978-981-15-6634-9"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, Z., and Chen, Z. (2011, January 24\u201329). Remote Sensing Indicators for Crop Growth Monitoring at Different Scales. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6050124"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2013.11.006","article-title":"Optimizing Multi-Resolution Segmentation Scale Using Empirical Methods: Exploring the Sensitivity of the Supervised Discrepancy Measure Euclidean Distance 2 (ED2)","volume":"87","author":"Witharana","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","unstructured":"Darwish, A., Leukert, K., and Reinhardt, W. (2003, January 21\u201325). Image Segmentation for the Purpose of Object-Based Classification. Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","unstructured":"(2021, April 14). ECognition Suite Documentation. Available online: https:\/\/docs.ecognition.com\/v9.5.0\/Page%20collection\/eCognition%20Suite%20Documentation.htm?tocpath=Documentation%20eCognition%20Suite%7C_____0."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (1999). Supervised Classification Techniques. Remote Sensing Digital Image Analysis: An Introduction, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for Land Cover Classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of Land Cover Classification Accuracy Assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1111\/j.1525-1497.2004.30091.x","article-title":"The Use of \u201cOverall Accuracy\u201d to Evaluate the Validity of Screening or Diagnostic Tests","volume":"19","author":"Alberg","year":"2004","journal-title":"J. Gen. Intern. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the Unsuitability of the Kappa Coefficient in the Assessment and Comparison of the Accuracy of Thematic Maps Obtained by Image Classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/0034-4257(94)90103-1","article-title":"Assessing the Classification Accuracy of Multisource Remote Sensing Data","volume":"47","author":"Fitzgerald","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Mutanga, O., and Kumar, L. (2019). Google Earth Engine Applications. Remote Sens., 11.","DOI":"10.3390\/rs11050591"},{"key":"ref_68","unstructured":"(2022, July 04). Sentinel-1 Algorithms|Google Earth Engine. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TGRS.2003.813353","article-title":"Understanding C-Band Radar Backscatter from Wheat Canopy Using a Multiple-Scattering Coherent Model","volume":"41","author":"Picard","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","unstructured":"Koller, D., and Sahami, M. (July, January 28). Hierarchically Classifying Documents Using Very Few Words. Proceedings of the 14th International Conference on Machine Learning (ICML), San Francisco, CA, USA."},{"key":"ref_71","unstructured":"Drummond, C., Elazmeh, W., Japkowicz, N., and Macskassy, S.A. (2007). A Review of Performance Evaluation Measures for Hierarchical Classifiers. Evaluation Methods for Machine Learning II: Papers from the AAAI-2007 Workshop, AAAI Technical Report WS-07-05, AAAI Press."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sagot, M.-F., and Walter, M.E.M.T. (2007, January 29\u201331). Comparing Several Approaches for Hierarchical Classification of Proteins with Decision Trees. Proceedings of the Advances in Bioinformatics and Computational Biology, Angra dos Reis, Brazil.","DOI":"10.1007\/978-3-540-73731-5"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10618-010-0175-9","article-title":"A Survey of Hierarchical Classification across Different Application Domains","volume":"22","author":"Silla","year":"2011","journal-title":"Data Min. Knowl. Disc."},{"key":"ref_74","unstructured":"Burred, J.J., and Lerch, A. (2003, January 8\u201311). A Hierarchical Approach to Automatic Musical Genre Classification. Proceedings of the 6th international Conference on Digital Audio Effects, London, UK."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"10251","DOI":"10.1007\/s11042-017-5443-x","article-title":"CNN-RNN: A Large-Scale Hierarchical Image Classification Framework","volume":"77","author":"Guo","year":"2018","journal-title":"Multimed Tools Appl."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Fan, J., Gao, Y., and Luo, H. (2007, January 23\u201327). Hierarchical Classification for Automatic Image Annotation. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands.","DOI":"10.1145\/1277741.1277763"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.scitotenv.2014.04.048","article-title":"OBIA Based Hierarchical Image Classification for Industrial Lake Water","volume":"487","author":"Karaman","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/07038992.1998.10855243","article-title":"Hierarchical Image Classification and Extraction of Forest Species Composition and Crown Closure from Airborne Multispectral Images","volume":"24","author":"Gerylo","year":"1998","journal-title":"Can. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01431161003743173","article-title":"Assessing Object-Based Classification: Advantages and Limitations","volume":"1","author":"Liu","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1080\/10106049.2015.1027291","article-title":"Pixel-Based and Object-Based Classifications Using High- and Medium-Spatial-Resolution Imageries in the Urban and Suburban Landscapes","volume":"30","author":"Estoque","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2016.09.029","article-title":"Comparison of Object-Based and Pixel-Based Random Forest Algorithm for Wetland Vegetation Mapping Using High Spatial Resolution GF-1 and SAR Data","volume":"73","author":"Fu","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object Based Image Analysis for Remote Sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.compag.2009.06.004","article-title":"Object- and Pixel-Based Analysis for Mapping Crops and Their Agro-Environmental Associated Measures Using QuickBird Imagery","volume":"68","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5194\/isprsarchives-XXXIX-B7-185-2012","article-title":"Support vector machine classification of object-based data for crop mapping, using multi-temporal landsat imagery","volume":"XXXIX-B7","author":"Devadas","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_86","first-page":"311","article-title":"The Comparison Index: A Tool for Assessing the Accuracy of Image Segmentation","volume":"9","author":"Lymburner","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_87","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Opportunities and Limitations of Object Based Image Analysis for Detecting Urban Impervious and Vegetated Surfaces Using True-Colour Aerial Photography. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer. Lecture Notes in Geoinformation and Cartography."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_89","first-page":"27","article-title":"A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions","volume":"2","author":"Gao","year":"2008","journal-title":"Online J. Earth Sci."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Berhane, T.M., Lane, C.R., Wu, Q., Anenkhonov, O.A., Chepinoga, V.V., Autrey, B.C., and Liu, H. (2018). Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes. Remote Sens., 10.","DOI":"10.3390\/rs10010046"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting Crop Yield Using Remotely Sensed Vegetation Indices and Crop Phenology Metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","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_93","doi-asserted-by":"crossref","first-page":"4643","DOI":"10.1080\/01431160802632249","article-title":"Multi-Year Monitoring of Rice Crop Phenology through Time Series Analysis of MODIS Images","volume":"30","author":"Boschetti","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Six, J., Plant, R.E., and Pe\u00f1a, J.M. (2018). Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California. Remote Sens., 10.","DOI":"10.3390\/rs10111745"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/JSTARS.2016.2539498","article-title":"Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion with SAR and Temperature","volume":"9","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_96","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_97","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rse.2017.06.022","article-title":"A New Method for Crop Classification Combining Time Series of Radar Images and Crop Phenology Information","volume":"198","author":"Bargiel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agricultural Canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ban, Y. (2016). A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring. Multitemporal Remote Sensing: Methods and Applications, Springer International Publishing. Remote Sensing and Digital Image Processing.","DOI":"10.1007\/978-3-319-47037-5"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1080\/01431161.2020.1862440","article-title":"A Phenological Object-Based Approach for Rice Crop Classification Using Time-Series Sentinel-1 Synthetic Aperture Radar (SAR) Data in Taiwan","volume":"42","author":"Son","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.3390\/s8063880","article-title":"Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels","volume":"8","author":"Vlaeminck","year":"2008","journal-title":"Sensors"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4437\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:24:15Z","timestamp":1760142255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,6]]},"references-count":101,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184437"],"URL":"https:\/\/doi.org\/10.3390\/rs14184437","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,6]]}}}