{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:41Z","timestamp":1760145281061,"version":"build-2065373602"},"reference-count":6,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"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>Accurate land surface temperature (LST) retrieval from satellite data is pivotal in environmental monitoring and scientific research. This study addresses the impact of variability in the effective wavelengths used for LST retrieval from the Thermal Infrared Sensor (TIRS) data of Landsat 8. We conduct a detailed analysis comparing the effective wavelengths reported by Yu et al. (2014) and those derived from data provided by the USGS. Our analysis reveals significant variability in the effective wavelengths for bands 10 and 11 of Landsat 8. By applying Planck\u2019s Law and utilizing the K1 and K2 coefficients available in the metadata of Landsat 8 products, we derive the effective wavelengths for bands 10 and 11. We also rederive the effective wavelength by integrating the spectral response function of the TIRS1 sensor. Our findings indicate that the effective wavelength for band 10 is 10.814 \u03bcm, aligning with the USGS data, while the effective wavelength for band 11 is 12.013 \u03bcm. We discuss the implications of these corrected effective wavelengths on the accuracy of LST retrieval algorithms, particularly the single channel algorithm (SC) and the radiative transfer equation (RT) employed by Yu et al. The importance of using precise effective wavelengths in satellite-based temperature retrieval is emphasized, to ensure the reliability and consistency of results. This analysis underscores the critical role of accurate spectral calibration parameters in remote sensing studies and provides valuable insights in the field of land surface temperature retrieval from Landsat 8 TIRS data.<\/jats:p>","DOI":"10.3390\/rs16142514","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T13:22:36Z","timestamp":1720531356000},"page":"2514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comment on Yu et al. Land Surface Temperature Retrieval from Landsat 8 TIRS\u2014Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sens. 2014, 6, 9829\u20139852"],"prefix":"10.3390","volume":"16","author":[{"given":"Almustafa Abd Elkader","family":"Ayek","sequence":"first","affiliation":[{"name":"Department of Topography, Faculty of Civil Engineering, University of Aleppo, Aleppo 12212, Syria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4735-9750","authenticated-orcid":false,"given":"Bilel","family":"Zerouali","sequence":"additional","affiliation":[{"name":"Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, B.P. 78C, Ouled Fares, Chlef 02180, Algeria"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9829","DOI":"10.3390\/rs6109829","article-title":"Land Surface Temperature Retrieval from Landsat 8 TIRS\u2014Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method","volume":"6","author":"Yu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","unstructured":"(2023, January 05). United States Geological Survey (USGS), Available online: https:\/\/www.usgs.gov\/landsat-missions\/using-usgs-landsat-level-1-data-product."},{"key":"ref_3","first-page":"703","article-title":"The research of split-window algorithm on the MODIS","volume":"30","author":"MAO","year":"2005","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_4","unstructured":"(2023, June 15). Spectral Characteristics Viewer, Available online: https:\/\/landsat.usgs.gov\/spectral-characteristics-viewer."},{"key":"ref_5","first-page":"8112","article-title":"A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data","volume":"109","author":"Sobrino","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1080\/01431160010006971","article-title":"A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region","volume":"22","author":"Qin","year":"2001","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2514\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:12:10Z","timestamp":1760109130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":6,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142514"],"URL":"https:\/\/doi.org\/10.3390\/rs16142514","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,7,9]]}}}