{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:04:15Z","timestamp":1781021055969,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Provincial Natural Science Foundation","award":["ZR2020MD019"],"award-info":[{"award-number":["ZR2020MD019"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["SHJB2021-81"],"award-info":[{"award-number":["SHJB2021-81"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["CXGC2022E07"],"award-info":[{"award-number":["CXGC2022E07"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["CXGC2022A33"],"award-info":[{"award-number":["CXGC2022A33"]}]},{"name":"Shandong Academy of Agricultural Sciences Unveils Science and Technology Difficult Projects","award":["ZR2020MD019"],"award-info":[{"award-number":["ZR2020MD019"]}]},{"name":"Shandong Academy of Agricultural Sciences Unveils Science and Technology Difficult Projects","award":["SHJB2021-81"],"award-info":[{"award-number":["SHJB2021-81"]}]},{"name":"Shandong Academy of Agricultural Sciences Unveils Science and Technology Difficult Projects","award":["CXGC2022E07"],"award-info":[{"award-number":["CXGC2022E07"]}]},{"name":"Shandong Academy of Agricultural Sciences Unveils Science and Technology Difficult Projects","award":["CXGC2022A33"],"award-info":[{"award-number":["CXGC2022A33"]}]},{"name":"Shandong Academy of Agricultural Sciences","award":["ZR2020MD019"],"award-info":[{"award-number":["ZR2020MD019"]}]},{"name":"Shandong Academy of Agricultural Sciences","award":["SHJB2021-81"],"award-info":[{"award-number":["SHJB2021-81"]}]},{"name":"Shandong Academy of Agricultural Sciences","award":["CXGC2022E07"],"award-info":[{"award-number":["CXGC2022E07"]}]},{"name":"Shandong Academy of Agricultural Sciences","award":["CXGC2022A33"],"award-info":[{"award-number":["CXGC2022A33"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman\u2013Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.<\/jats:p>","DOI":"10.3390\/s22166087","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"6087","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods"],"prefix":"10.3390","volume":"22","author":[{"given":"Meng","family":"Wang","sequence":"first","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Geospatial Information Center, Beijing 100070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6206-3918","authenticated-orcid":false,"given":"Dongrui","family":"Han","sequence":"additional","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuehui","family":"Hou","sequence":"additional","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouzhen","family":"Liang","sequence":"additional","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyan","family":"Sui","sequence":"additional","affiliation":[{"name":"Shandong Academy of Agricultural Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112576","DOI":"10.1016\/j.rse.2021.112576","article-title":"Pre- and within-season crop type classification trained with archival land cover information","volume":"264","author":"Johnson","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/S0034-4257(00)00212-1","article-title":"Rice monitoring and production estimation using multitemporal RADARSAT","volume":"76","author":"Shao","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.isprsjprs.2005.05.001","article-title":"Rice crop parameter retrieval using multi-temporal, multi-incidence angle Radarsat SAR data","volume":"59","author":"Chakraborty","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1080\/01431160500239172","article-title":"SAR signature investigation of rice crop using RADARSAT data","volume":"27","author":"Choudhury","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/S0924-2716(97)00009-9","article-title":"Discrimination of rice crop grown under different cultural practices using temporal ERS-1 synthetic aperture radar data","volume":"52","author":"Chakraborty","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.rse.2009.04.005","article-title":"Potential of SAR sensors TerraSAR-X, ASAR\/ENVISAT and PALSAR\/ALOS for monitoring sugarcane crops on Reunion Island","volume":"113","author":"Baghdadi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1080\/01431161.2011.587844","article-title":"Crop classification using multi-configuration SAR data in the North China Plain","volume":"33","author":"Jia","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shang, J., McNairn, H., Champagne, C., and Jiao, X. (2009). Application of Multi-Frequency Synthetic Aperture Radar (SAR) in Crop Classification. Advances in Geoscience and Remote Sensing, IntechOpen.","DOI":"10.5772\/8321"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2013.03.014","article-title":"Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery","volume":"134","author":"Dong","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isprsjprs.2008.07.006","article-title":"Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories","volume":"64","author":"McNairn","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1080\/01431160903160777","article-title":"Land-cover classification using radar and optical images: A case study in Central Mexico","volume":"31","author":"Woodhouse","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Phan, H., Le Toan, T., Bouvet, A., Nguyen, L.D., Pham Duy, T., and Zribi, M. (2018). Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data. Sensors, 18.","DOI":"10.3390\/s18010316"},{"key":"ref_14","first-page":"102059","article-title":"In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data","volume":"88","author":"Dey","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12361","DOI":"10.1109\/JSTARS.2021.3130186","article-title":"Multitemporal Polarimetric SAR Change Detection for Crop Monitoring and Crop Type Classification","volume":"14","author":"Marino","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4129","DOI":"10.1080\/01431160701840182","article-title":"Comparison of polarimetric SAR observables in terms of classification performance","volume":"29","author":"Alberga","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"220","DOI":"10.5589\/m11-029","article-title":"Classification of polarimetric SAR images using Support Vector Machines","volume":"37","author":"Entezari","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6301","DOI":"10.1080\/01431160902842391","article-title":"Mapping paddy rice with multitemporal ALOS\/PALSAR imagery in southeast China","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/LGRS.2013.2279005","article-title":"Identification of Coherent Scatterers in SAR Images Based on the Analysis of Polarimetric Signatures","volume":"11","author":"Strzelczyk","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2017.02.014","article-title":"Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery","volume":"193","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2015.04.018","article-title":"Monthly short-term detection of land development using RADARSAT-2 polarimetric SAR imagery","volume":"164","author":"Qi","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"201","article-title":"A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations","volume":"58","author":"Tamiminia","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1080\/10106049.2019.1695957","article-title":"SAR polarimetric analysis for major land covers including pre-monsoon crops","volume":"36","author":"Verma","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"An, Q., Pan, Z., and You, H. (2018). Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18020334"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liu, L., Qiu, X., and Lei, B. (2017). Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode. Sensors, 17.","DOI":"10.3390\/s17071578"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, W., Yang, J., Zhao, J., Shi, H., and Yang, L. (2018). An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3. Sensors, 18.","DOI":"10.3390\/s18020559"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/LGRS.2011.2162935","article-title":"Four-Component Scattering Power Decomposition With Extended Volume Scattering Model","volume":"9","author":"Sato","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1109\/TGRS.2012.2212446","article-title":"General Four-Component Scattering Power Decomposition With Unitary Transformation of Coherency Matrix","volume":"51","author":"Singh","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1049\/cje.2016.08.028","article-title":"A Multi-component Decomposition Method for Polarimetric SAR Data","volume":"26","author":"Wei","year":"2017","journal-title":"Chin. J. Electron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.1109\/TGRS.2010.2041242","article-title":"Three-Component Model-Based Decomposition for Polarimetric SAR Data","volume":"48","author":"An","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Arii, M., Zyl, J.v., and Kim, Y. (2012, January 22\u201327). Improvement of adaptive-model based decomposition with polarization orientation compensation. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351628"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TGRS.2011.2128325","article-title":"Model-Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Nonnegative Eigenvalues","volume":"49","author":"Zyl","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6662","DOI":"10.1109\/JSTARS.2021.3071161","article-title":"Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6087\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:08:51Z","timestamp":1760141331000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,15]]},"references-count":38,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166087"],"URL":"https:\/\/doi.org\/10.3390\/s22166087","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,15]]}}}