F. Mazeh, Bilal Hammoud, H. Ayad, F. Ndagijimana, G. Faour, M. Fadlallah, J. Jomaah
{"title":"基于多入射角的L波段和X波段雷达后向散射雪深反演算法","authors":"F. Mazeh, Bilal Hammoud, H. Ayad, F. Ndagijimana, G. Faour, M. Fadlallah, J. Jomaah","doi":"10.1109/HPCS.2018.00021","DOIUrl":null,"url":null,"abstract":"The objective of this work is to develop an algorithm to estimate snow thickness over ground from backscattering measurements at L- and X-band (1.5 and 10 GHz) using multi incidence angles (0°, 10° and 30°). The return signal from the medium is due to the ground roughness, the snow volume, and the noise from the radar system. So, surface and volume scattering effects are modeled from physics forward models, and noise effects are modeled by including a white Gaussian noise into the simulation. This inversion algorithm involves two steps. The first is to estimate snow density using L-band co-polarized backscattering coefficient. The second is to estimate the snow depth from X-band co-polarized backscattering coefficients using dual incidence angles. For a 0.02 noise variance, all retrieved values have an error less than 2% for a snow depth range of [50-300] cm.","PeriodicalId":308138,"journal":{"name":"2018 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Snow Depth Retrieval Algorithm from Radar Backscattering Measurements at L- and X- Band Using Multi-Incidence Angles\",\"authors\":\"F. Mazeh, Bilal Hammoud, H. Ayad, F. Ndagijimana, G. Faour, M. Fadlallah, J. Jomaah\",\"doi\":\"10.1109/HPCS.2018.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this work is to develop an algorithm to estimate snow thickness over ground from backscattering measurements at L- and X-band (1.5 and 10 GHz) using multi incidence angles (0°, 10° and 30°). The return signal from the medium is due to the ground roughness, the snow volume, and the noise from the radar system. So, surface and volume scattering effects are modeled from physics forward models, and noise effects are modeled by including a white Gaussian noise into the simulation. This inversion algorithm involves two steps. The first is to estimate snow density using L-band co-polarized backscattering coefficient. The second is to estimate the snow depth from X-band co-polarized backscattering coefficients using dual incidence angles. For a 0.02 noise variance, all retrieved values have an error less than 2% for a snow depth range of [50-300] cm.\",\"PeriodicalId\":308138,\"journal\":{\"name\":\"2018 International Conference on High Performance Computing & Simulation (HPCS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on High Performance Computing & Simulation (HPCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPCS.2018.00021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCS.2018.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Snow Depth Retrieval Algorithm from Radar Backscattering Measurements at L- and X- Band Using Multi-Incidence Angles
The objective of this work is to develop an algorithm to estimate snow thickness over ground from backscattering measurements at L- and X-band (1.5 and 10 GHz) using multi incidence angles (0°, 10° and 30°). The return signal from the medium is due to the ground roughness, the snow volume, and the noise from the radar system. So, surface and volume scattering effects are modeled from physics forward models, and noise effects are modeled by including a white Gaussian noise into the simulation. This inversion algorithm involves two steps. The first is to estimate snow density using L-band co-polarized backscattering coefficient. The second is to estimate the snow depth from X-band co-polarized backscattering coefficients using dual incidence angles. For a 0.02 noise variance, all retrieved values have an error less than 2% for a snow depth range of [50-300] cm.