{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T12:18:58Z","timestamp":1785586738878,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T00:00:00Z","timestamp":1621900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.<\/jats:p>","DOI":"10.3390\/ijerph18115659","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T13:14:21Z","timestamp":1621948461000},"page":"5659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4965-7276","authenticated-orcid":false,"given":"Yue","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Environment Science and Geography, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7974-6408","authenticated-orcid":false,"given":"Wenpeng","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Environment Science and Geography, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alahmad, B., Tomasso, L.P., Al-Hemoud, A., James, P., and Koutrakis, P. (2020). Spatial Distribution of Land Surface Temperatures in Kuwait: Urban Heat and Cool Islands. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17092993"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhan, Q., Gao, S., and Liu, H. (2019). How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16203865"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhang, J., Shi, X., and Liu, S. (2020). Impacts of Building Features on the Cooling Effect of Vegetation in Community-Based MicroClimate: Recognition, Measurement and Simulation from a Case Study of Beijing. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17238915"},{"key":"ref_4","unstructured":"Aliabad, F.A., Zare, M., and Malamiri, H.G. (2020). A comparative assessment of the accuracies of split-window algorithms for retrieving of land surface temperature using Landsat 8 data. Model. Earth Syst. Environ., 1\u201315."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2020.08.024","article-title":"An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces","volume":"169","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100696","DOI":"10.1016\/j.uclim.2020.100696","article-title":"Exploratory analysis of cooling effect of urban lakes on land surface temperature in Bucharest (Romania) using Landsat imagery","volume":"34","author":"Cheval","year":"2020","journal-title":"Urban Clim."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103979","DOI":"10.1016\/j.landurbplan.2020.103979","article-title":"Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature","volume":"206","author":"Cao","year":"2021","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-020-06068-1","article-title":"Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India","volume":"13","author":"Shahfahad","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.5194\/essd-12-2555-2020","article-title":"A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017","volume":"12","author":"Zhao","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.ecolind.2020.107230","article-title":"Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area","volume":"122","author":"Sekertekin","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102443","DOI":"10.1016\/j.scs.2020.102443","article-title":"Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature","volume":"63","author":"Guo","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100051","DOI":"10.1016\/j.acags.2020.100051","article-title":"Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data","volume":"9","author":"Taloor","year":"2021","journal-title":"Appl. Comput. Geosci."},{"key":"ref_13","first-page":"204","article-title":"A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data","volume":"76","author":"Wang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.3390\/rs4082477","article-title":"Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)","volume":"4","author":"Schott","year":"2012","journal-title":"Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2004.02.003","article-title":"Land surface temperature retrieval from LANDSAT TM 5","volume":"90","author":"Sobrino","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Mu\u00f1oz, J.C., and Sobrino, J.A. (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res. Space Phys., 108.","DOI":"10.1029\/2003JD003480"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2015.01.026","article-title":"Relationships between dominant plant species, fractional cover and Land Surface Temperature in a Mediterranean ecosystem","volume":"167","author":"Roberts","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"e00599","article-title":"Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria","volume":"10","author":"Nse","year":"2020","journal-title":"Sci. Afr."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sekertekin, A., and Bonafoni, S. (2020). Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. Remote Sens., 12.","DOI":"10.3390\/rs12020294"},{"key":"ref_21","unstructured":"(2021, May 22). United States Geological Survey, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_22","unstructured":"(2021, May 22). National Meteorological Information Center. Available online: https:\/\/data.cma.cn\/."},{"key":"ref_23","unstructured":"(2021, May 22). Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center, Available online: https:\/\/ladsweb.nascom.nasa.gov\/."},{"key":"ref_24","unstructured":"(2021, May 22). GLOBELAND30. Available online: http:\/\/www.globallandcover.com\/."},{"key":"ref_25","unstructured":"(2021, May 22). Atmospheric Correction Parameter Calculator, Available online: http:\/\/atmcorr.gsfc.nasa.gov\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.3390\/rs70404268","article-title":"An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","unstructured":"(2021, May 22). Historical Meteorology of Iceberg Geese. Available online: http:\/\/weather.bsyan.com\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1029\/JC080i036p05113","article-title":"Estimation of sea surface temperatures from two infrared window measurements with different absorption","volume":"80","author":"McMillin","year":"1975","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.3390\/s140405768","article-title":"Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm","volume":"14","author":"Rozenstein","year":"2014","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, C., He, X., Yan, F., Yu, L., Bu, K., Yang, J., Chang, L., and Zhang, S. (2017). Mapping the Influence of Land Use\/Land Cover Changes on the Urban Heat Island Effect\u2014A Case Study of Changchun, China. Sustainability, 9.","DOI":"10.3390\/su9020312"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.quaint.2020.04.052","article-title":"Assessing the impact of land use land cover changes on land surface temperature over Pune city, India","volume":"575","author":"Gohain","year":"2021","journal-title":"Quat. Int."},{"key":"ref_32","first-page":"131","article-title":"Assessment of variation of land use\/land cover and its impact on land surface temperature of Asansol subdivision","volume":"24","author":"Das","year":"2021","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100688","DOI":"10.1016\/j.uclim.2020.100688","article-title":"Effects of landscape composition and patterns on land surface temperature: Urban heat island case study for Nigde, Turkey","volume":"34","author":"Soydan","year":"2020","journal-title":"Urban Clim."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e04485","DOI":"10.1016\/j.heliyon.2020.e04485","article-title":"Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand","volume":"6","author":"Adulkongkaew","year":"2020","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, C., He, X., Wang, R., Yan, F., Yu, L., Bu, K., Yang, J., Chang, L., and Zhang, S. (2017). The Effect of Urban Green Spaces on the Urban Thermal Environment and Its Seasonal Variations. Forests, 8.","DOI":"10.3390\/f8050153"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, Y., and Yao, Y. (2019). Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images. Sensors, 19.","DOI":"10.3390\/s19225049"},{"key":"ref_37","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":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/11\/5659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:07:42Z","timestamp":1760162862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/11\/5659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,25]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["ijerph18115659"],"URL":"https:\/\/doi.org\/10.3390\/ijerph18115659","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,25]]}}}