{"title":"利用AIRS高光谱观测优化尘埃红外折射率","authors":"Yi Wang, Jun Wang, Xiaoguang Xu","doi":"10.1364/HISE.2019.HTH1B.1","DOIUrl":null,"url":null,"abstract":"Climate models lack accurate dust refractive index measurements for estimating radiative forcing. AIRS hyperspectral observations are used to optimize dust refractive index in infrared spectrum through integration of PCA and inverse modelling techniques.","PeriodicalId":174423,"journal":{"name":"Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using AIRS hyperspectral observations to optimize dust refractive index in infrared spectrum\",\"authors\":\"Yi Wang, Jun Wang, Xiaoguang Xu\",\"doi\":\"10.1364/HISE.2019.HTH1B.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Climate models lack accurate dust refractive index measurements for estimating radiative forcing. AIRS hyperspectral observations are used to optimize dust refractive index in infrared spectrum through integration of PCA and inverse modelling techniques.\",\"PeriodicalId\":174423,\"journal\":{\"name\":\"Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/HISE.2019.HTH1B.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/HISE.2019.HTH1B.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using AIRS hyperspectral observations to optimize dust refractive index in infrared spectrum
Climate models lack accurate dust refractive index measurements for estimating radiative forcing. AIRS hyperspectral observations are used to optimize dust refractive index in infrared spectrum through integration of PCA and inverse modelling techniques.
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