{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:24:02Z","timestamp":1773797042566,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"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>Sea surface temperature (SST) is a key hydrological variable which can be monitored via satellite. One source of thermal data with a spatial resolution high enough to study sub-mesoscale processes in coastal waters may be the Landsat mission. The Thermal Infrared Sensor on board Landsat 8 collects data in two bands, which allows for the use of the well-known nonlinear split-window formula to estimate SST (NLSST) using top-of-the-atmosphere (TOA) brightness temperature. To calibrate its coefficients a significant number of matchup points are required, representing a wide range of atmospheric conditions. In this study over 1200 granules of satellite data and 12 time series of in situ measurements from buoys and platforms operating in the Baltic Sea over a period of more than 6 years were used to select matchup points, derive NLSST coefficients and evaluate the results. To filter out pixels contaminated by clouds, ice or land influences, the IdePix algorithm was used with Quality Assessment Band and additional test of the adjacent pixels. Various combinations of flags were tested. The results show that the NLSST coefficients derived previously for coastal areas, characterised by a more humid atmosphere, might overestimate low SST values. Formulas derived for the Baltic Sea produced biases close to 0 \u00b0C and RMSEs in the range of 0.49\u20130.52 \u00b0C.<\/jats:p>","DOI":"10.3390\/rs13224619","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T02:42:28Z","timestamp":1637116948000},"page":"4619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea"],"prefix":"10.3390","volume":"13","author":[{"given":"Katarzyna","family":"Bradtke","sequence":"first","affiliation":[{"name":"Institute of Oceanography, University of Gda\u0144sk, Marsza\u0142ka Pi\u0142sudskiego 46, 81-378 Gdynia, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1175\/JCLI-D-15-0663.1","article-title":"Sea Surface Temperature Climate Data Record for the North Sea and Baltic Sea","volume":"29","author":"Karagali","year":"2016","journal-title":"J. Clim."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lepp\u00e4ranta, M., and Myrberg, K. (2009). Physical Oceanography of the Baltic Sea, Springer.","DOI":"10.1007\/978-3-540-79703-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4168","DOI":"10.1029\/2018JC013948","article-title":"Temperature Variability of the Baltic Sea since 1850 and Attribution to Atmospheric Forcing Variables","volume":"124","author":"Kniebusch","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.oceano.2015.04.004","article-title":"Spatial and Temporal Variability of Sea Surface Temperature in the Baltic Sea Based on 32-Years (1982\u20132013) of Satellite Data","volume":"57","author":"Stramska","year":"2015","journal-title":"Oceanologia"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"631","DOI":"10.5697\/oc.54-4.631","article-title":"Remote-Sensing Observations of Coastal Sub-Mesoscale Eddies in the South-Eastern Baltic","volume":"54","author":"Gurova","year":"2012","journal-title":"Oceanologia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"369","DOI":"10.5697\/oc.54-3.369","article-title":"A Statistical Approach to Coastal Upwelling in the Baltic Sea Based on the Analysis of Satellite Data for 1990\u20132009","volume":"54","author":"Lehmann","year":"2012","journal-title":"Oceanologia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3389\/fmars.2019.00420","article-title":"Observational Needs of Sea Surface Temperature","volume":"6","author":"Armstrong","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101739","DOI":"10.1016\/j.hal.2019.101739","article-title":"Cyanobacterial Blooms in the Baltic Sea: Correlations with Environmental Factors","volume":"92","author":"Kahru","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/meps101001","article-title":"Cyanobacterial Blooms Cause Heating of the Sea Surface","volume":"101","author":"Kahru","year":"1993","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_10","first-page":"1652","article-title":"Heating Rate within the Upper Ocean in Relation to Its Bio-Optical State","volume":"24","author":"Morel","year":"1994","journal-title":"J. Phys. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1357\/002224007784219020","article-title":"Field Investigation on Seawater Temperature Variability in Relation to Horizontal Optical Gradient","volume":"65","author":"Matciak","year":"2007","journal-title":"J. Mar. Res."},{"key":"ref_12","unstructured":"Konik, M. (2021). Remote Sensing of Cyanobacteria Blooms and Their Influence on the Remotely Sensed Physical Properties of the Water in the Baltic Sea. [Ph.D. Thesis, Institute of Oceanology of the Polish Academy of Sciences]."},{"key":"ref_13","first-page":"68","article-title":"The Operational Method of Filling Information Gaps in Satellite Imagery Using Numerical Models","volume":"75","author":"Konik","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111366","DOI":"10.1016\/j.rse.2019.111366","article-title":"Half a Century of Satellite Remote Sensing of Sea-Surface Temperature","volume":"233","author":"Minnett","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.rse.2012.01.016","article-title":"SST Diurnal Variability in the North Sea and the Baltic Sea","volume":"121","author":"Karagali","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmarsys.2012.05.011","article-title":"MODIS-Based Sea Surface Temperature of the Baltic Sea Curonian Lagoon","volume":"129","author":"Kozlov","year":"2014","journal-title":"J. Mar. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jmarsys.2012.07.008","article-title":"Assimilating NOAA SST Data into the BSH Operational Circulation Model for the North and Baltic Seas: Inference about the Data","volume":"105\u2013108","author":"Losa","year":"2012","journal-title":"J. Mar. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"525","DOI":"10.5194\/os-14-525-2018","article-title":"Assimilating High-Resolution Sea Surface Temperature Data Improves the Ocean Forecast Potential in the Baltic Sea","volume":"14","author":"Liu","year":"2018","journal-title":"Ocean Sci."},{"key":"ref_19","first-page":"451","article-title":"Algorithm for the Remote Sensing of the Baltic Ekosystem (DESAMBEM), Part 1: Mathematical Apparatus","volume":"50","author":"Darecki","year":"2008","journal-title":"Oceanologia"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jmarsys.2005.01.004","article-title":"Optimal Interpolation of Sea Surface Temperature for the North Sea and Baltic Sea","volume":"65","author":"She","year":"2007","journal-title":"J. Mar. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., and Chen, B. (2020). Global Revisit Interval Analysis of Landsat-8-9 and Sentinel-2A-2B Data for Terrestrial Monitoring. Sensors, 20.","DOI":"10.3390\/s20226631"},{"key":"ref_22","unstructured":"(2021, September 01). Landsat 8 (L8) Data Users Handbook, Available online: https:\/\/www.usgs.gov\/media\/files\/landsat-8-data-users-handbook."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kuroda, H., and Toya, Y. (2020). High-Resolution Sea Surface Temperatures Derived from Landsat 8: A Study of Submesoscale Frontal Structures on the Pacific Shelf off the Hokkaido Coast, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12203326"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"909","DOI":"10.5194\/os-11-909-2015","article-title":"High-Resolution Satellite Turbidity and Sea Surface Temperature Observations of River Plume Interactions during a Significant Flood Event","volume":"11","author":"Brando","year":"2015","journal-title":"Ocean Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"149","DOI":"10.2112\/SI74-014.1","article-title":"Detection of Low Salinity Groundwater Seeping into the Eastern Laizhou Bay (China) with the Aid of Landsat Thermal Data","volume":"74","author":"Xing","year":"2016","journal-title":"J. Coast. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3389\/fmars.2017.00329","article-title":"Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality","volume":"4","author":"Trinh","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_27","first-page":"10","article-title":"Assessment of Chlorophyll-a, SST and Diffuse Attenuation Coefficient (Kd490) in Northwest of Persian Gulf Using Landsat 8 Satellite Data","volume":"5","author":"Savari","year":"2016","journal-title":"Int. J. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3389\/fmars.2017.00190","article-title":"Oyster Aquaculture Site Selection Using Landsat 8-Derived Sea Surface Temperature, Turbidity, and Chlorophyll a","volume":"4","author":"Snyder","year":"2017","journal-title":"Front. Mar. Sci"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"90","DOI":"10.4028\/www.scientific.net\/AMM.862.90","article-title":"Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area","volume":"862","author":"Cahyono","year":"2017","journal-title":"Appl. Mech. Mater."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/feart.2020.00007","article-title":"Baltic Sea Operational Oceanography\u2014A Stimulant for Regional Earth System Research","volume":"8","author":"She","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Syariz, M.A., Jaelani, L.M., Subehi, L., Pamungkas, A., Koenhardono, E.S., and Sulisetyono, A. (2015). Retrieval of Sea Surface Temperature over Poteran Island Water of Indonesia with Landsat 8 TIRS Image: A Preliminary Algorithm, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.","DOI":"10.5194\/isprsarchives-XL-2-W4-87-2015"},{"key":"ref_32","first-page":"13","article-title":"Retrieving Coastal Sea Surface Temperature from Landsat-8 TIRS for Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia","volume":"16","author":"Susilo","year":"2019","journal-title":"Int. J. Remote Sens. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111518","DOI":"10.1016\/j.rse.2019.111518","article-title":"Automated Water Surface Temperature Retrieval from Landsat 8\/TIRS","volume":"237","author":"Vanhellemont","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","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 the Thermal Infrared Sensor Onboard Landsat 8","volume":"191","author":"Gerace","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jang, J.-C., and Park, K.-A. (2019). High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI\/TIRS Data at Coastal Regions. Remote Sens., 11.","DOI":"10.3390\/rs11222687"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bayat, F., and Hasanlou, M. (2016). Feasibility Study of Landsat-8 Imagery for Retrieving Sea Surface Temperature (Case Study Persian Gulf), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.","DOI":"10.5194\/isprsarchives-XLI-B8-1107-2016"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"27999","DOI":"10.1029\/98JC02370","article-title":"The Development and Operational Application of Nonlinear Algorithms for the Measurement of Sea Surface Temperatures with the NOAA Polar-Orbiting Environmental Satellites","volume":"103","author":"Walton","year":"1998","journal-title":"J. Geophys. Res."},{"key":"ref_39","unstructured":"Appendix, G. (2014). NOAA KLM User\u2019s Guide with NOAA-N, N Prime, and MetOp Supplements."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1029\/1999JC000065","article-title":"Overview of the NOAA\/NASA Pathfinder Version 4.2 Algorithm for Sea Surface Temperature and Associated Matchup Database","volume":"106","author":"Kilpatrick","year":"2001","journal-title":"J. Geophys. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.1029\/93JC03215","article-title":"Correcting Infrared Satellite Estimates of Sea Surface Temperature for Atmospheric Water Vapor Attenuation","volume":"99","author":"Emery","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_42","unstructured":"(2021). U.S. Geological Survey Landsat Collection 2 (Version 1.1, 15 January, 2021), U.S. Geological Survey Fact Sheet 2021-3002."},{"key":"ref_43","unstructured":"Copernicus Marine In Situ TAC (2021, March 01). Copernicus Marine In Situ TAC Quality Information Document for Near Real Time In Situ Products (QUID and SQO). V2.1; Copernicus Marine In Situ TAC: 2021. Available online: https:\/\/archimer.ifremer.fr\/doc\/00646\/75807\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1016\/j.compchemeng.2004.01.009","article-title":"On-Line Outlier Detection and Data Cleaning","volume":"28","author":"Liu","year":"2004","journal-title":"Comput. Chem. Eng."},{"key":"ref_45","unstructured":"(2021, September 01). Landsat 8 Operational Land Imager (OLI)\u2014Thermal Infrared Sensor (TIRS) Solar and View Angle Generation Algorithm de-Scription Document (Add). LSDS-1928 v.2.0, Available online: https:\/\/www.usgs.gov\/media\/files\/lsds-1928-landsat-8-olitirs-solar-view-angle-generation-add."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and Expansion of TheFmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4\u20137,8, and Sentinel 2 Images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"(2021, September 01). Landsat 8\u20139 Operational Land Imager (OLI)\u2014Thermal Infrared Sensor (TIRS) Collection 2 Level 1 (L1) Data Format Control Book (DFCB). LSDS-1822 Version 6.0, Available online: https:\/\/www.usgs.gov\/media\/files\/landsat-8-9-olitirs-collection-2-level-1-data-format-control-book."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s40747-019-00128-0","article-title":"Cloud Detection Methodologies: Variants and Development\u2014A Review","volume":"6","author":"Mahajan","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5033","DOI":"10.1080\/0143116031000095880","article-title":"Radiometric Measurements of the Sea-Surface Skin Temperature: The Competing Roles of the Diurnal Thermocline and the Cool Skin","volume":"24","author":"Minnett","year":"2003","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:22Z","timestamp":1760167882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":49,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224619"],"URL":"https:\/\/doi.org\/10.3390\/rs13224619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]}}}