{"title":"On the reduction of the systematic error in imaging radiometry by aperture synthesis: a new approach for the SMOS space mission","authors":"A. Khazâal, H. Carfantan, É. Anterrieu","doi":"10.1109/MICRAD.2008.4579478","DOIUrl":null,"url":null,"abstract":"The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. This work is concerned with the reduction of the systematic error (or bias) in the reconstruction of radiometric brightness temperature maps from SMOS interferometric measurements. A recent and efficient method has been proposed for reducing this error. However, a residual bias still persists. A new approach for reducing this bias down to residual values less than 0.1 K is presented here and illustrated with numerical simulations.","PeriodicalId":193521,"journal":{"name":"2008 Microwave Radiometry and Remote Sensing of the Environment","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Microwave Radiometry and Remote Sensing of the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MICRAD.2008.4579478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. This work is concerned with the reduction of the systematic error (or bias) in the reconstruction of radiometric brightness temperature maps from SMOS interferometric measurements. A recent and efficient method has been proposed for reducing this error. However, a residual bias still persists. A new approach for reducing this bias down to residual values less than 0.1 K is presented here and illustrated with numerical simulations.