{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T12:43:58Z","timestamp":1763037838218,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901055"],"award-info":[{"award-number":["41901055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Land surface temperatures (LST) are vital parameters in land surface\u2013atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current reconstruction methods are limited for maintaining spatial details and high accuracies. We developed a new gap-free algorithm termed the spatial feature-considered random forest regression (SFRFR) model; it builds stable nonlinear relationships to connect the LST with related parameters, including terrain elements, land coverage types, spectral indexes, surface reflectance data, and the spatial feature of the LST, to reconstruct the missing LST data. The SFRFR model reconstructed gap-free LST data retrieved from the Landsat 8 satellite on 27 July 2017 in Wuhan. The results show that the SFRFR model exhibits the best performance according to the various evaluation metrics among the SFRFR, random forest regression and spline interpolation, with a coefficient of determination (R2) reaching 0.96, root-mean-square error (RMSE) of 0.55, and mean absolute error (MAE) of 0.55. Then, we reconstructed gap-free LST data gathered in Wuhan from 2016 to 2021 to analyze urban thermal environment changes and found that 2020 presented the coolest temperatures. The SFRFR model still displayed satisfactory results, with an average R2 of 0.91 and an MAE of 0.63. We further discuss and discover the factors affecting the visual performance of SFRFR and identify the research priority to circumvent these disadvantages. Overall, this study provides a simple, practical method for acquiring gap-free LST data to help us better understand the spatiotemporal LST variation process.<\/jats:p>","DOI":"10.3390\/s23020913","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:57:33Z","timestamp":1673578653000},"page":"913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China"],"prefix":"10.3390","volume":"23","author":[{"given":"Zefeng","family":"Wu","sequence":"first","affiliation":[{"name":"School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfen","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Key Laboratory of Agricultural Remote Sensing and Information System, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangliang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nclimate2196","article-title":"Land management and land-cover change have impacts of similar magnitude on surface temperature","volume":"4","author":"Luyssaert","year":"2014","journal-title":"Nat. Clim. Change"},{"key":"ref_2","first-page":"452","article-title":"Remote sensing of drought: Progress, challenges and opportunities","volume":"53","author":"AghaKouchak","year":"2015","journal-title":"RvGeo"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"223","DOI":"10.5194\/hess-15-223-2011","article-title":"Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery","volume":"15","author":"Anderson","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.scitotenv.2018.04.105","article-title":"Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective","volume":"635","author":"Peng","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6540","DOI":"10.1175\/JCLI-D-10-05000.1","article-title":"Accelerated Changes of Environmental Conditions on the Tibetan Plateau Caused by Climate Change","volume":"24","author":"Zhong","year":"2011","journal-title":"J. Clim."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Asrar, G.R. (2019). Advances in Quantitative Earth Remote Sensing: Past, Present and Future. Sensors, 19.","DOI":"10.3390\/s19245399"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0034-4257(92)90096-3","article-title":"A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies","volume":"42","author":"Hook","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Crist\u00f3bal, J., Jim\u00e9nez-Mu\u00f1oz, J., Prakash, A., Mattar, C., Skokovi\u0107, D., and Sobrino, J. (2018). An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band. Remote Sens., 10.","DOI":"10.3390\/rs10030431"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhong, X.K., Huo, X., Ren, C., Labed, J., and Li, Z.L. (2016). Retrieving Land Surface Temperature from Hyperspectral Thermal Infrared Data Using a Multi-Channel Method. Sensors, 16.","DOI":"10.3390\/s16050687"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"647","DOI":"10.3390\/rs70100647","article-title":"A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data","volume":"7","author":"Du","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7857","DOI":"10.1080\/01431161.2014.978036","article-title":"Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape","volume":"35","author":"Fan","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sarafanov, M., Kazakov, E., Nikitin, N.O., and Kalyuzhnaya, A.V. (2020). A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI. Remote Sens., 12.","DOI":"10.3390\/rs12233865"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1080\/01431160601034902","article-title":"A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images","volume":"28","author":"Maxwell","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1080\/01431161.2015.1047991","article-title":"Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico","volume":"36","author":"Franklin","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.3390\/rs6042845","article-title":"A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, H., and Weng, Q. (2018). Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment. Sensors, 18.","DOI":"10.3390\/s18114058"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1109\/TGRS.2013.2284489","article-title":"Spatial Interpolation to Predict Missing Attributes in GIS Using Semantic Kriging","volume":"52","author":"Bhattacharjee","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/01431161.2015.1007248","article-title":"Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting","volume":"36","author":"Ozelkan","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1111\/j.1467-8659.2011.01971.x","article-title":"A Survey of Specularity Removal Methods","volume":"30","author":"Artusi","year":"2011","journal-title":"Comput. Graph. Forum"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MGRS.2021.3050782","article-title":"Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques","volume":"9","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.cageo.2013.08.009","article-title":"Reconstruction of the land surface temperature time series using harmonic analysis","volume":"61","author":"Xu","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10738","DOI":"10.1109\/TGRS.2021.3053284","article-title":"A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data","volume":"59","author":"Yao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Feng","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2021JD035598","DOI":"10.1029\/2021JD035598","article-title":"Long-Term and Fine-Scale Surface Urban Heat Island Dynamics Revealed by Landsat Data Since the 1980s: A Comparison of Four Megacities in China","volume":"127","author":"Li","year":"2022","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/LGRS.2012.2227930","article-title":"Generating High Spatiotemporal Resolution Land Surface Temperature for Urban Heat Island Monitoring","volume":"10","author":"Bo","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mo, Y., Xu, Y., Chen, H., and Zhu, S. (2021). A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions. Remote Sens., 13.","DOI":"10.3390\/rs13142838"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111931","DOI":"10.1016\/j.rse.2020.111931","article-title":"Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS\/Terra land products and MSG geostationary satellite data","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Zhao, W., Ma, M., and He, K. (2021). Gap-Free LST Generation for MODIS\/Terra LST Product Using a Random Forest-Based Reconstruction Method. Remote Sens., 13.","DOI":"10.3390\/rs13142828"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.03.006","article-title":"Downscaling land surface temperatures at regional scales with random forest regression","volume":"178","author":"Hutengs","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ejiagha, I.R., Ahmed, M.R., Dewan, A., Gupta, A., Rangelova, E., and Hassan, Q.K. (2022). Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors. Sensors, 22.","DOI":"10.3390\/s22082894"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41598-017-19088-x","article-title":"Relationship among land surface temperature and LUCC, NDVI in typical karst area","volume":"8","author":"Deng","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.geoderma.2018.03.026","article-title":"Satellite land surface temperature and reflectance related with soil attributes","volume":"325","author":"Bedin","year":"2018","journal-title":"Geoderma"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.rse.2007.08.004","article-title":"A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS","volume":"112","author":"Sims","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100790","DOI":"10.1016\/j.uclim.2021.100790","article-title":"Assessment of urban cooling effect based on downscaled land surface temperature: A case study for Fukuoka, Japan","volume":"36","author":"Peng","year":"2021","journal-title":"Urban Clim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1109\/JSTARS.2020.3014586","article-title":"An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2006.06.026","article-title":"New refinements and validation of the MODIS Land-Surface Temperature\/Emissivity products","volume":"112","author":"Wan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2011.12.015","article-title":"Evaluation of Earth Observation based global long term vegetation trends\u2014Comparing GIMMS and MODIS global NDVI time series","volume":"119","author":"Fensholt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture-climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth Sci. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"21904","DOI":"10.1109\/ACCESS.2019.2896241","article-title":"Downscaling Land Surface Temperatures Using a Random Forest Regression Model with Multitype Predictor Variables","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.scitotenv.2018.03.050","article-title":"Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China","volume":"631\u2013632","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6458","DOI":"10.1109\/TGRS.2016.2585198","article-title":"Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China","volume":"54","author":"Duan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"67115","DOI":"10.1007\/s11356-022-20572-9","article-title":"Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models","volume":"29","author":"Kartal","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0898-1221(82)90009-8","article-title":"Smooth interpolation of scattered data by local thin plate splines","volume":"8","author":"Franke","year":"1982","journal-title":"Comput. Math. Appl."},{"key":"ref_50","first-page":"24","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_52","first-page":"358","article-title":"Classification and Regression Trees (CART)","volume":"40","author":"Breiman","year":"1984","journal-title":"Biometrics"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tang, K., Zhu, H., and Ni, P. (2021). Spatial Downscaling of Land Surface Temperature over Heterogeneous Regions Using Random Forest Regression Considering Spatial Features. Remote Sens., 13.","DOI":"10.3390\/rs13183645"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, F., Jiang, H., Hu, H., Zhong, K., Jing, W., Yang, J., and Jia, B. (2020). Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging. Remote Sens., 12.","DOI":"10.3390\/rs12071082"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"135244","DOI":"10.1016\/j.scitotenv.2019.135244","article-title":"Spatial quantitative analysis of the potential driving factors of land surface temperature in different \u201cCenters\u201d of polycentric cities: A case study in Tianjin, China","volume":"706","author":"Hu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"8341","DOI":"10.1109\/JSTARS.2021.3105582","article-title":"Influencing Factors of Spatial Heterogeneity of Land Surface Temperature in Nanjing, China","volume":"14","author":"Fan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/913\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:40Z","timestamp":1760119480000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/913"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020913"],"URL":"https:\/\/doi.org\/10.3390\/s23020913","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,12]]}}}