{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:53:57Z","timestamp":1760151237181,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["41871260"],"award-info":[{"award-number":["41871260"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The satellite-based remote-sensing detection of CO2 can provide long-term and continuous observations at the global scale, and is the primary observational approach to monitoring CO2 changes. Surface albedo and aerosol are two significant challenges for retrieving CO2 concentrations from near-infrared remote-sensing data. This research addressed the dependence of radiance sensitivities on CO2 concentration, aerosol, surface albedo, and water vapor. The band ratio method was used to retrieve CO2 with band selection of the high- and low-sensitivity channels to restrain the influence of surface albedo and aerosol. Results showed that the band ratio method had better efficiency at reducing the impact of aerosol and surface albedo than that of the optical estimation method. The retrieval error of aerosol was reduced by 2.5% overall. We validated the band ratio method retrieval results with two TCCON sites and the GOSAT L3 product. Pearson\u2019s correlation coefficient, mean bias, mean absolute bias, and root mean squared error of the monthly retrieval data of the band ratio method showed high agreement between the TCCON in Park Falls and Wollongong. These results indicate that the band ratio method based on normalized sensitivity can effectively reduce the influence of surface albedo and aerosol.<\/jats:p>","DOI":"10.3390\/rs14051106","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity"],"prefix":"10.3390","volume":"14","author":[{"given":"Liang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengbo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-6637","authenticated-orcid":false,"given":"Yong","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.5194\/amt-11-3111-2018","article-title":"Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements","volume":"11","author":"Wu","year":"2018","journal-title":"Atmos. 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