{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:43Z","timestamp":1760144563300,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"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":["U2142212","2022YFC3004200","2021JC0009"],"award-info":[{"award-number":["U2142212","2022YFC3004200","2021JC0009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["U2142212","2022YFC3004200","2021JC0009"],"award-info":[{"award-number":["U2142212","2022YFC3004200","2021JC0009"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["U2142212","2022YFC3004200","2021JC0009"],"award-info":[{"award-number":["U2142212","2022YFC3004200","2021JC0009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean\u2013atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (&lt;5 m\/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals.<\/jats:p>","DOI":"10.3390\/rs16091551","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Polarized Bidirectional Reflectance Distribution Function Matrix Derived from Two-Scale Roughness Theory and Its Applications in Active Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5634-7199","authenticated-orcid":false,"given":"Lingli","family":"He","sequence":"first","affiliation":[{"name":"Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fuzhong","family":"Weng","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"}]},{"given":"Jinghan","family":"Wen","sequence":"additional","affiliation":[{"name":"Qingdao Joint Institute of Marine Meteorology, Chinese Academy of Meteorological Sciences, Qingdao 266000, China"}]},{"given":"Tong","family":"Jia","sequence":"additional","affiliation":[{"name":"Qingdao Joint Institute of Marine Meteorology, Chinese Academy of Meteorological Sciences, Qingdao 266000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2022.113220","article-title":"Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements","volume":"281","author":"Alerskans","year":"2022","journal-title":"Remote Sens. 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