{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:22:40Z","timestamp":1778790160538,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871274"],"award-info":[{"award-number":["41871274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971402"],"award-info":[{"award-number":["61971402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CXJJ19B10"],"award-info":[{"award-number":["CXJJ19B10"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic High-Tech Innovation Fund of Chinese Academy of Sciences","award":["41871274"],"award-info":[{"award-number":["41871274"]}]},{"name":"Strategic High-Tech Innovation Fund of Chinese Academy of Sciences","award":["61971402"],"award-info":[{"award-number":["61971402"]}]},{"name":"Strategic High-Tech Innovation Fund of Chinese Academy of Sciences","award":["CXJJ19B10"],"award-info":[{"award-number":["CXJJ19B10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the last two decades, spaceborne polarimetric synthetic aperture radar (PolSAR) has been widely used to penetrate sea ice surfaces to achieve fully polarimetric high-resolution imaging at all times of day and in a range of weather conditions. Model-based polarimetric decomposition is a powerful tool used to extract useful physical and geometric information about sea ice from the matrix datasets acquired by PolSAR. The volume scattering of sea ice is usually modeled as the incoherent average of scatterings of a large volume of oriented ellipsoid particles that are uniformly distributed in 3D space. This uniform spatial distribution is often approximated as a uniform orientation distribution (UOD), i.e., the particles are uniformly oriented in all directions. This is achieved in the existing literature by ensuring the canting angle \u03c6 and tilt angle \u03c4 of particles uniformly distributed in their respective ranges and introducing a factor cos\u2061\u03c4 in the ensemble average. However, we find this implementation of UOD is not always effective, while a real UOD can be realized by distributing the solid angles of particles uniformly in 3D space. By deriving the total solid angle of the canting-tilt cell spanned by particles and combining the differential relationship between solid angle and Euler angles \u03c6 and \u03c4, a complete expression of the joint probability density function p\u03c6,\u03c4 that can always ensure the uniform orientation of particles of sea ice is realized. By ensemble integrating the coherency matrix of \u03c6,\u03c4-oriented particle with p\u03c6,\u03c4, a generalized modeling of the volume coherency matrix of 3D uniformly oriented spheroid particles is obtained, which covers factors such as radar observation geometry, particle shape, canting geometry, tilt geometry and transmission effect in a multiplicative way. The existing volume scattering models of sea ice constitute special cases. The performance of the model in the characterization of the volume behaviors was investigated via simulations on a volume of oblate and prolate particles with the differential reflectivity ZDR, polarimetric entropy H and scattering \u03b1 angle as descriptors. Based on the model, several interesting orientation geometries were also studied, including the aligned orientation, complement tilt geometry and reflection symmetry, among which the complement tilt geometry is specifically highlighted. It involves three volume models that correspond to the horizontal tilt, vertical tilt and random tilt of particles within sea ice, respectively. To match the models to PolSAR data for adaptive decomposition, two selection strategies are provided. One is based on ZDR, and the other is based on the maximum power fitting. The scattering power that reduces the rank of coherency matrix by exactly one without violating the physical realizability condition is obtained to make full use of the polarimetric scattering information. Both the models and decomposition were finally validated on the Gaofen-3 PolSAR data of a young ice area in Prydz Bay, Antarctica. The adaptive decomposition result demonstrates not only the dominant vertical tilt preference of brine inclusions within sea ice, but also the subordinate random tilt preference and non-negligible horizontal tilt preference, which are consistent with the geometric selection mechanism that the c-axes of polycrystallines within sea ice would gradually align with depth. The experiment also indicates that, compared to the strategy based on ZDR, the maximum power fitting is preferable because it is entirely driven by the model and data and is independent of any empirical thresholds. Such soft thresholding enables this strategy to adaptively estimate the negative ZDR offset introduced by the transmission effect, which provides a novel inversion of the refractive index of sea ice based on polarimetric model-based decomposition.<\/jats:p>","DOI":"10.3390\/rs15123208","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T02:01:33Z","timestamp":1687312893000},"page":"3208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-3781","authenticated-orcid":false,"given":"Dong","family":"Li","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Lu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8125-9425","authenticated-orcid":false,"given":"Yunhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s00376-017-7156-5","article-title":"Remarkable link between projected uncertainties of Arctic sea-ice decline and winter Eurasian climate","volume":"35","author":"Cheung","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1061\/(ASCE)0887-381X(2000)14:2(68)","article-title":"Modeling and forecasting of Bohai Sea ice","volume":"14","author":"Wu","year":"2000","journal-title":"J. Cold. Reg. Eng."},{"key":"ref_3","first-page":"87","article-title":"Features of sea ice disaster in the Bohai Sea in 2010","volume":"20","author":"Sun","year":"2011","journal-title":"J. Nat. Disasters"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1139\/as-2017-0019","article-title":"Financial costs of conducting science in the Arctic: Examples from seabird research","volume":"4","author":"Mallory","year":"2018","journal-title":"Arct. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/0034-4257(93)90039-Z","article-title":"Antarctic sea ice mapping using the AVHRR","volume":"45","author":"Zibordi","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"C02S06","DOI":"10.1029\/2007JC004253","article-title":"Antarctic sea ice parameters from AMSR-E data using two techniques and comparisons with sea ice from SSM\/I","volume":"113","author":"Parkinson","year":"2008","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12561","DOI":"10.1029\/94JC00707","article-title":"A microwave technique for mapping thin sea ice","volume":"99","author":"Cavalieri","year":"1994","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/TGRS.2012.2184123","article-title":"Multiyear Arctic sea ice classification using QuikSCAT","volume":"50","author":"Swan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TGRS.2015.2452215","article-title":"Multiyear Arctic sea ice classification using OSCAT and QuikSCAT","volume":"54","author":"Lindell","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.05.012","article-title":"Classification of new-ice in the Greenland Sea using Satellite SSM\/I radiometer and SeaWinds scatterometer data and comparison with ice model","volume":"97","author":"Tonboe","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lindell, D.B., and Long, D.G. (2016). Multiyear Arctic ice classification using ASCAT and SSMIS. Remote Sens., 8.","DOI":"10.3390\/rs8040294"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1109\/JSTARS.2013.2258327","article-title":"Sea ice detection in the sea of Okhotsk using PALSAR and MODIS data","volume":"6","author":"Wakabayashi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Wang, S., Zhang, H., Fu, Q., and Wang, Y. (2017, January 19\u201322). Gaofen-3 sea ice detection based on deep learning. Proceedings of the Progress in Electromagnetics Research Symposium-Fall, Singapore.","DOI":"10.1109\/PIERS-FALL.2017.8293267"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yang, Y., Shokr, M., Mi, C., Li, X.-M., Cheng, X., and Hui, F. (2021). Deep learning based sea ice classification with Gaofen-3 fully polarimetric SAR data. Remote Sens., 13.","DOI":"10.3390\/rs13081452"},{"key":"ref_15","unstructured":"Huynen, J.R. (1970). Phenomenological Theory of Radar Targets. [Ph.D. Dissertation, Delft University of Technology]."},{"key":"ref_16","unstructured":"Holm, W.A., and Barnes, R.M. (1988, January 20\u201321). On radar polarization mixed target state decomposition techniques. Proceedings of the IEEE National Radar Conference, Ann Arbor, MI, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/LGRS.2006.873229","article-title":"On Huynen\u2019s decomposition of a Kennaugh matrix","volume":"3","author":"Yang","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/TGRS.2015.2464113","article-title":"Unified Huynen phenomenological decomposition of radar targets and its classification applications","volume":"54","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Boerner, W.-M., Cram, L.A., Holm, W.A., Stein, D.E., Wiesbeck, W., Keydel, W., Giuli, D., Gjessing, D.T., Molinet, F.A., and Brand, H. (1992). Direct and Inverse Methods in Radar Polarimetry, Kluwer.","DOI":"10.1007\/978-94-010-9243-2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target scattering decomposition in terms of roll-invariant target parameters","volume":"45","author":"Touzi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TGRS.2007.897929","article-title":"Fitting a two-component scattering model to polarimetric SAR data from forests","volume":"45","author":"Freeman","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/LGRS.2008.2000795","article-title":"Multiple-component scattering model for polarimetric SAR image decomposition","volume":"5","author":"Zhang","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1109\/TGRS.2010.2076285","article-title":"Adaptive model-based decomposition of polarimetric SAR covariance matrices","volume":"49","author":"Arii","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TGRS.2010.2076285","article-title":"Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues","volume":"49","author":"Arii","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/LGRS.2011.2160837","article-title":"Improved four-component model-based target decomposition for polarimetric SAR data","volume":"9","author":"Shan","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/TGRS.2013.2257603","article-title":"On complete model-based decomposition of polarimetric SAR coherency matrix data","volume":"52","author":"Cui","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1109\/TGRS.2013.2255615","article-title":"General polarimetric model-based decomposition for coherency matrix","volume":"52","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3724\/SP.J.1146.2012.00897","article-title":"A novel freeman decomposition based on nonnegative eigenvalue decomposition with non-reflection symmetry","volume":"35","author":"Liu","year":"2013","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/TGRS.2013.2259177","article-title":"Comparison of nonnegative eigenvalue decompositions with and without reflection symmetry assumptions","volume":"52","author":"Wang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.1109\/TGRS.2013.2262051","article-title":"Generalized polarimetric model-based decompositions using incoherent scattering models","volume":"52","author":"Lee","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/LGRS.2014.2313955","article-title":"An improvement on the complete model-based decomposition of polarimetric SAR data","volume":"11","author":"An","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, Q., Ballester-Berman, J.D., Lopez-Sanchez, J.M., Zhu, J., and Wang, C. (2016). Quantitative analysis of polarimetric model-based decomposition methods. Remote Sens., 8.","DOI":"10.3390\/rs8120977"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8371","DOI":"10.1109\/TGRS.2019.2920762","article-title":"Seven-component scattering power decomposition of POLSAR coherency matrix","volume":"57","author":"Singh","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.1109\/TGRS.2020.2983758","article-title":"A mathematical extension to the general four-component scattering power decomposition with unitary transformation of coherency matrix","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2010.2056692","article-title":"Polarimetric decomposition over glacier ice using long-wavelength airborne PolSAR","volume":"49","author":"Sharma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Moen, M.-A.N., Ferro-Famil, L., Doulgeris, A.P., Anfinsen, S.N., Gerland, S., and Eltoft, T. (2013, January 26\u201330). Polarimetric decomposition analysis of Sea Ice data. Proceedings of the International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, Frascati, Italy."},{"key":"ref_42","first-page":"95","article-title":"Polarimetric scattering characteristics based sea ice types classification by polarimetric synthetic aperture radar: Taking sea ice in the Bohai Sea for example","volume":"35","author":"Xi","year":"2013","journal-title":"Acta Ceanologica Sin."},{"key":"ref_43","unstructured":"Eltoft, T., Doulgeris, A.P., and Grahn, J. (2014, January 2\u20136). Model-based polarimetric decomposition of Arctic sea ice. Proceedings of the European Conference on Synthetic Aperture Radar, Berlin, Germany."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1080\/01431161.2013.879345","article-title":"Application of a three-component scattering model over snow-covered first-year sea ice using polarimetric C-band SAR data","volume":"35","author":"Hossain","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/JSTARS.2014.2356552","article-title":"A polarimetric decomposition method for ice in the Bohai Sea using C-band PolSAR data","volume":"8","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TGRS.2015.2477168","article-title":"Polarimetric decomposition of L-band PolSAR backscattering over the Austfonna ice cap","volume":"54","author":"Parrella","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111910","DOI":"10.1016\/j.rse.2020.111910","article-title":"Observations of SAR polarimetric parameters of lake and fast sea ice during the early growth phase","volume":"247","author":"Shokr","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Parrella, G., Hajnsek, I., and Papathanassiou, K.P. (2021). Retrieval of Firn Thickness by Means of Polarisation Phase Differences in L-Band SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13214448"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"11593","DOI":"10.1109\/JSTARS.2021.3126069","article-title":"Model-based interpretation of PolSAR data for the characterization of glacier zones in Greenland","volume":"14","author":"Parrella","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/07038992.2021.2003701","article-title":"Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring","volume":"48","author":"Shokr","year":"2022","journal-title":"CAN J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3904","DOI":"10.1109\/JSTARS.2022.3170732","article-title":"Investigation of polarimetric decomposition for Arctic summer sea ice classification using Gaofen-3 fully polarimetric SAR data","volume":"15","author":"He","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"13665","DOI":"10.1029\/95JC00937","article-title":"Polarimetric signatures of sea ice: 1. Theoretical model","volume":"100","author":"Nghiem","year":"1995","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Weeks, W.F., and Ackley, S.F. (1986). The Growth, Structure, and Properties of Sea Ice, Springer.","DOI":"10.1007\/978-1-4899-5352-0_2"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1109\/TGRS.2009.2031101","article-title":"Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data","volume":"48","author":"Neumann","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TGRS.2010.2046331","article-title":"A general characterization for polarimetric scattering from vegetation canopies","volume":"48","author":"Arii","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3838","DOI":"10.1109\/TGRS.2011.2138146","article-title":"Volume scattering modeling in PolSAR decompositions: Study of ALOS PALSAR data over boreal forest","volume":"49","author":"Antropov","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","first-page":"414","article-title":"An adaptive two-component model-based decomposition on soil moisture estimation for C-band RADARSAT-2 imagery over wheat fields at early growing stages","volume":"13","author":"Huang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"9389","DOI":"10.1029\/95JE00485","article-title":"Backscatter model for the unusual radar properties of the Greenland Ice Sheet","volume":"100","author":"Rignot","year":"1995","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Parrella, G., Papathanassiou, K., and Hajnsek, I. (2015, January 26\u201331). 3-D glacier subsurface characterization using SAR polarimetry. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7327011"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/36.789640","article-title":"Wide-band polarimetric radar inversion studies for vegetation layers","volume":"37","author":"Cloude","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cloude, S. (2010). Polarisation: Applications in Remote Sensing, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"},{"key":"ref_62","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging from Basics to Applications, CRC Press."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/1520-0450(1976)015<0069:PUORDR>2.0.CO;2","article-title":"Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation","volume":"15","author":"Seliga","year":"1976","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1126\/science.225.4667.1145","article-title":"Hail detection with a differential reflectivity radar","volume":"225","author":"Bringi","year":"1984","journal-title":"Science"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1109\/36.718641","article-title":"Saline ice thickness retrieval under diurnal thermal cycling conditions","volume":"36","author":"Shih","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/36.718862","article-title":"Thin saline ice thickness retrieval using time-series C-band polarimetric radar measurements","volume":"36","author":"Shih","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1029\/93RS01605","article-title":"A model with ellipsoidal scatterers for polarimetric remote sensing of anisotropic layered media","volume":"28","author":"Nghiem","year":"1993","journal-title":"Radio Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhang, X.-D. (2017). Matrix Analysis and Applications, Cambridge University Press.","DOI":"10.1017\/9781108277587"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6940","DOI":"10.1109\/TGRS.2018.2845944","article-title":"Adaptive model-based classification of PolSAR data","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1016\/j.rse.2009.12.015","article-title":"Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing","volume":"114","author":"Hong","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1017\/S0022143000022577","article-title":"World Meteorological Organization. WMO sea-ice nomenclature. Terminology, codes and illustrated glossary. Edition 1970","volume":"11","author":"Armstrong","year":"1972","journal-title":"J. Glaciol."},{"key":"ref_72","unstructured":"Apel, J.R., and Jackson, C.R. (2004). Measurements of Sea ice, Synthetic Aperture Radar Marine User\u2019s Manual, Chapter 3."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.coldregions.2014.12.012","article-title":"Numerical study on c-axis orientations of sea ice surface grown under calm sea conditions using a particle method and Voronoi dynamics","volume":"112","author":"Kawano","year":"2015","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1029\/JC083iC10p05105","article-title":"Preferred crystal orientations in the fast ice along the margins of the Arctic Ocean","volume":"83","author":"Weeks","year":"1978","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"522","DOI":"10.3189\/172756504781829800","article-title":"In situ index-of-refraction measurements of the South Polar firn with the RICE detector","volume":"50","author":"Kravchenko","year":"2004","journal-title":"J. Glaciol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:57:33Z","timestamp":1760126253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":75,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123208"],"URL":"https:\/\/doi.org\/10.3390\/rs15123208","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,20]]}}}