R. Akbar, R. Chen, Alireza Tabatabaeenejad, M. Moghaddam
{"title":"协同使用AirMOSS p波段SAR与SMAP l波段雷达辐射计进行土壤水分反演","authors":"R. Akbar, R. Chen, Alireza Tabatabaeenejad, M. Moghaddam","doi":"10.1109/ICEAA.2016.7731518","DOIUrl":null,"url":null,"abstract":"We present and discuss an inter-comparison between microwave remote sensing observations from the NASA Soil Moisture Active-Passive (SMAP) mission and those from the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). Earth gridded 1, 3, and 9 km AirMOSS and SMAP SAR observations show, in general, a strong correlation (R2 ≥ 0. 5). However, the correlation degrades at extreme AirMOSS local incidence angles. L-band brightness temperature from SMAP and P-band backscatter from AirMOSS are almost uncorrelated (R2 ≤ -0.3) and indicate the need for more mathematically comprehensive techniques to merge these datasets to estimate surface soil moisture.","PeriodicalId":434972,"journal":{"name":"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Synergistic use of AirMOSS P-band SAR with the SMAP L-band radar-radiometer for soil moisture retrieval\",\"authors\":\"R. Akbar, R. Chen, Alireza Tabatabaeenejad, M. Moghaddam\",\"doi\":\"10.1109/ICEAA.2016.7731518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present and discuss an inter-comparison between microwave remote sensing observations from the NASA Soil Moisture Active-Passive (SMAP) mission and those from the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). Earth gridded 1, 3, and 9 km AirMOSS and SMAP SAR observations show, in general, a strong correlation (R2 ≥ 0. 5). However, the correlation degrades at extreme AirMOSS local incidence angles. L-band brightness temperature from SMAP and P-band backscatter from AirMOSS are almost uncorrelated (R2 ≤ -0.3) and indicate the need for more mathematically comprehensive techniques to merge these datasets to estimate surface soil moisture.\",\"PeriodicalId\":434972,\"journal\":{\"name\":\"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEAA.2016.7731518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAA.2016.7731518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Synergistic use of AirMOSS P-band SAR with the SMAP L-band radar-radiometer for soil moisture retrieval
We present and discuss an inter-comparison between microwave remote sensing observations from the NASA Soil Moisture Active-Passive (SMAP) mission and those from the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). Earth gridded 1, 3, and 9 km AirMOSS and SMAP SAR observations show, in general, a strong correlation (R2 ≥ 0. 5). However, the correlation degrades at extreme AirMOSS local incidence angles. L-band brightness temperature from SMAP and P-band backscatter from AirMOSS are almost uncorrelated (R2 ≤ -0.3) and indicate the need for more mathematically comprehensive techniques to merge these datasets to estimate surface soil moisture.