{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T21:09:19Z","timestamp":1785013759435,"version":"3.55.0"},"reference-count":15,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T00:00:00Z","timestamp":1523318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It has been noticed that the Landsat 8 Thermal Infrared Sensor (TIRS) had an issue with stray light since its launch in 2013. This artifact is due to out-of-field radiance that scatters onto the TIRS focal plane. Much effort has been taken to develop an algorithm to remove this artifact. One proposed approach involves using TIRS data itself (referred to as TIRS-on-TIRS) to retrieve the true sensor-reaching radiance. This approach has been proven to be operational and supports the TIRS Collection-1 product. A methodology of calibrating the TIRS sensor with information from the Geostationary Operational Environmental Satellite (GOES) instrument may optimally reduce the stray light effect for special cases where there is a large temperature contrast between the edge of the TIRS image and out-of-field radiance (referred to as GOES-on-TIRS). This paper illustrates a GOES to TIRS conversion (GTTC) algorithm with the North American Regional Reanalysis (NARR) data to support the GOES-on-TIRS method. Results show this GOES_TIRS correction method performs similarly to the TIRS Collection-1 product. Additionally, a simplified methodology is proposed to improve the GOES data processing which can operationalize the GOES-on-TIRS algorithm. Results also show that, using the proposed algorithm with these special cases, the maximum difference between the Collection-1 product and the GOES-on-TIRS correction results in a temperature difference from 0.5% to 0.7%.<\/jats:p>","DOI":"10.3390\/rs10040589","type":"journal-article","created":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T13:06:08Z","timestamp":1523365568000},"page":"589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Practical Approach to Landsat 8 TIRS Stray Light Correction Using Multi-Sensor Measurements"],"prefix":"10.3390","volume":"10","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmett","family":"Ientilucci","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,10]]},"reference":[{"key":"ref_1","unstructured":"NASA (2013). 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Proceedings of the SPIE Earth Observing Systems XIX, San Diego, CA, USA.","DOI":"10.1117\/12.2063457"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10435","DOI":"10.3390\/rs61110435","article-title":"Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor","volume":"6","author":"Montanaro","year":"2014","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3963","DOI":"10.1364\/AO.54.003963","article-title":"Toward an operational stray light correction for the Landsat 8 thermal infrared sensor","volume":"54","author":"Montanaro","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2017.01.029","article-title":"Derivation and validation of the stray light correction algorithm for thethermal infrared sensor onboard landsat 8","volume":"191","author":"Montanaro","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11607","DOI":"10.3390\/rs61111607","article-title":"Landsat-8 thermal infrared sensor TIRS vicarious radiometric calibration","volume":"6","author":"Barsi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ientilucci, E., Raqueno, N., and Schott, J. (2017). Landsat 8 TIRS calibration with external sensors. Proc. SPIE, 9218.","DOI":"10.1117\/12.2272766"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.12.012007","article-title":"Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data","volume":"12","author":"Gerace","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","unstructured":"(2017, March 21). NOAA Office of Satellite, and Product Operations, Geostationary Operational Environmental Satellites (GOES), Available online: https:\/\/www.class.ncdc.noaa.gov\/saa\/products\/welcome."},{"key":"ref_11","unstructured":"(2017, March 21). Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, National Centers for Environmental Prediction\/National Weather Service\/NOAA\/U.S. Department of Commerce (2005): NCEP North American Regional Reanalysis (NARR). Available online: http:\/\/rda.ucar.edu\/datasets\/ds608.0\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1061\/(ASCE)0733-9453(1994)120:3(115)","article-title":"Determination of Look Angles to Geostationary Communication Satellites","volume":"120","author":"Soler","year":"1994","journal-title":"J. Surv. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2015.04.023","article-title":"A decade of sea surface temperature from MODIS","volume":"165","author":"Kilpatrick","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cook, M.J., and Schott, J.R. (2013). Initial validation of atmospheric compensation for a Landsat land surface temperature product. Proc. SPIE, 8743.","DOI":"10.1117\/12.2015320"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11244","DOI":"10.3390\/rs61111244","article-title":"Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a Land Surface Temperature (LST) product from the archive","volume":"6","author":"Cook","year":"2014","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/589\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:15Z","timestamp":1760194815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,10]]},"references-count":15,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040589"],"URL":"https:\/\/doi.org\/10.3390\/rs10040589","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,10]]}}}