{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:31:00Z","timestamp":1778650260840,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program on monitoring, early warning and prevention of major natural disasters","award":["2017YFC1502406"],"award-info":[{"award-number":["2017YFC1502406"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571342"],"award-info":[{"award-number":["41571342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51579135"],"award-info":[{"award-number":["51579135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["8192025"],"award-info":[{"award-number":["8192025"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China\u2019s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research.<\/jats:p>","DOI":"10.3390\/rs11222610","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:17:25Z","timestamp":1573125445000},"page":"2610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China\u2019s Landmass Using the BME Method"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9047-2551","authenticated-orcid":false,"given":"Yunfei","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-7303","authenticated-orcid":false,"given":"Yunhao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiping","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0168-1923(95)02259-Z","article-title":"Terminology in thermal infrared remote sensing of natural surfaces","volume":"77","author":"Norman","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0168-1923(95)02261-U","article-title":"An interpretation of methodologies for indirect measurement of soil water content","volume":"77","author":"Carlson","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0168-1923(95)02265-Y","article-title":"Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature","volume":"77","author":"Norman","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/0168-1923(95)02264-X","article-title":"A one-layer resistance model for estimating regional evapotranspiration using remote sensing data","volume":"77","author":"Zhang","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.1175\/2008JCLI2097.1","article-title":"Evaluation of the Surface Radiation Budget in the Atmospheric Component of the Hadley Centre Global Environmental Model (HadGEM1)","volume":"21","author":"Ringer","year":"2008","journal-title":"J. Clim."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/j.agrformet.2009.05.016","article-title":"Advances in thermal infrared remote sensing for land surface modeling","volume":"149","author":"Kustas","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1175\/2010JAMC2460.1","article-title":"Evaluation of the Relationship between Air and Land Surface Temperature under Clear and Cloudy-Sky Conditions","volume":"50","author":"Gallo","year":"2011","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.rse.2014.04.024","article-title":"Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA","volume":"150","author":"Kloog","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"110","article-title":"Comparison of MODIS Land Surface Temperature and Air Temperature over the Continental USA Meteorological Stations","volume":"40","author":"Zhang","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1038\/nclimate2704","article-title":"Impacts of temperature and its variability on mortality in New England","volume":"5","author":"Shi","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.isprsjprs.2017.04.007","article-title":"Simultaneous inversion of multiple land surface parameters from MODIS optical\u2013thermal observations","volume":"128","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gillespie, A.R., Matsunaga, T., Rokugawa, S., and Hook, S.J. (1996). Temperature and emissivity separation from advanced spaceborne thermal emission and reflection radiometer (ASTER) images. Infrared Spaceborne Remote Sensing IV, Proceedings of SPIE\u2019s 1996 International Symposium on Optical Science, Engineering, and Instrumentation, Denver, CO, United States, 4\u20139 August 1996, SPIE.","DOI":"10.1117\/12.255172"},{"key":"ref_16","unstructured":"Wan, Z. (2007). Collection-5 MODIS Land Surface Temperature Products Users\u2019 Guide, University of California."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1080\/01431160802235860","article-title":"Split-window algorithm for land surface temperature estimation from MSG1-SEVIRI data","volume":"29","author":"Jiang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","unstructured":"(2019, November 05). MODIS Web, Available online: https:\/\/modis.gsfc.nasa.gov\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Neteler, M. (2010). Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data. Remote Sens., 10.","DOI":"10.3390\/rs1020333"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/LGRS.2014.2348651","article-title":"Reconstructing MODIS LST Based on Multitemporal Classification and Robust Regression","volume":"12","author":"Zeng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.rse.2011.12.019","article-title":"A daily merged MODIS Aqua\u2013Terra land surface temperature data set for the conterminous United States","volume":"119","author":"Crosson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3808\/jei.200400035","article-title":"Estimation of land surface temperature using spatial interpolation and satellite-derived surface emissivity","volume":"4","author":"Yang","year":"2004","journal-title":"J. Environ. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/LGRS.2013.2263553","article-title":"Reconstruction of Time-Series MODIS LST in Central Qinghai-Tibet Plateau Using Geostatistical Approach","volume":"10","author":"Ke","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Metz, M., Rocchini, D., and Neteler, M. (2014). Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail. Remote Sens., 6.","DOI":"10.3390\/rs6053822"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2014.10.001","article-title":"An effective approach for gap-filling continental scale remotely sensed time-series","volume":"98","author":"Weiss","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"083525","DOI":"10.1117\/1.JRS.8.083525","article-title":"Estimating the land-surface temperature of pixels covered by clouds in MODIS products","volume":"8","author":"Yu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cageo.2017.04.007","article-title":"Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data","volume":"105","author":"Sun","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.010","article-title":"Creating a seamless 1km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2017.04.008","article-title":"A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data","volume":"195","author":"Duan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2013.09.003","article-title":"Generation of a time-consistent land surface temperature product from MODIS data","volume":"140","author":"Duan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27037","DOI":"10.1029\/2000JD900318","article-title":"A generalized algorithm for retrieving cloudy sky skin temperature from satellite thermal infrared radiances","volume":"105","author":"Jin","year":"2000","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kou, X., Jiang, L., Bo, Y., Yan, S., and Chai, L. (2016). Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sens., 8.","DOI":"10.3390\/rs8020105"},{"key":"ref_35","first-page":"265","article-title":"Estimating land-surface temperature under clouds using MSG\/SEVIRI observations","volume":"13","author":"Lu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2016.03.011","article-title":"Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN","volume":"117","author":"Shwetha","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"905","DOI":"10.3390\/rs70100905","article-title":"Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Scarino, B., Minnis, P., Palikonda, R., Reichle, R.H., Morstad, D., Yost, C., Shan, B., and Liu, Q. (2013). Retrieving Clear-Sky Surface Skin Temperature for Numerical Weather Prediction Applications from Geostationary Satellite Data. Remote Sens., 5.","DOI":"10.3390\/rs5010342"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2018.08.021","article-title":"Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands","volume":"217","author":"Lai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.026","article-title":"Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)","volume":"197","author":"Amani","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_42","first-page":"435","article-title":"Bayesian Maximum Entropy Analysis and Mapping: A Farewell to Kriging Estimators?","volume":"30","author":"Christakos","year":"1998","journal-title":"Math. Geosci."},{"key":"ref_43","unstructured":"Christakos, G. (2000). Modern Spatiotemporal Geostatistics, Oxford University Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9717","DOI":"10.1029\/2000JD900780","article-title":"BME representation of particulate matter distributions in the state of California on the basis of uncertain measurements","volume":"106","author":"Christakos","year":"2001","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1029\/2001WR000743","article-title":"Computational Bayesian maximum entropy solution of a stochastic advection-reaction equation in the light of site-specific information","volume":"38","author":"Kolovos","year":"2002","journal-title":"Water Resour. Res."},{"key":"ref_46","first-page":"97","article-title":"A quantified Bayesian Maximum Entropy estimate of Antarctic krill abundance across the Scotia Sea and in small-scale management units from the CCAMLR-2000 survey","volume":"13","author":"Heywood","year":"2006","journal-title":"CCAMLR Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1111\/j.1365-2389.2007.00981.x","article-title":"Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information","volume":"59","author":"Brus","year":"2007","journal-title":"Eur. J. Soil Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lee, S.J., and Wentz, E.A. (2008). Applying Bayesian Maximum Entropy to extrapolating local-scale water consumption in Maricopa County, Arizona. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006101"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.sste.2009.07.005","article-title":"A Bayesian Maximum Entropy approach to address the change of support problem in the spatial analysis of childhood asthma prevalence across North Carolina","volume":"1","author":"Lee","year":"2009","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s00477-009-0317-z","article-title":"Space\u2013time forecasting using soft geostatistics: A case study in forecasting municipal water demand for Phoenix, Arizona","volume":"24","author":"Lee","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7746","DOI":"10.1021\/es2003827","article-title":"Using River Distance and Existing Hydrography Data Can Improve the Geostatistical Estimation of Fish Tissue Mercury at Unsampled Locations","volume":"45","author":"Money","year":"2011","journal-title":"Environ. Sci. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1021\/es4040528","article-title":"An LUR\/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary Sources","volume":"48","author":"Reyes","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s00477-014-1015-z","article-title":"Space and time dynamics of urban water demand in Portland, Oregon and Phoenix, Arizona","volume":"29","author":"Lee","year":"2015","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1109\/JSTARS.2015.2425691","article-title":"Merging Satellite Ocean Color Data with Bayesian Maximum Entropy Method","volume":"8","author":"Shi","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1111\/ejss.12295","article-title":"Updating digital soil maps with new data: A case study of soil organic matter in Jiangsu, China","volume":"66","author":"Sun","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_56","first-page":"91","article-title":"Improving Environmental Prediction by Assimilating Auxiliary Information","volume":"26","author":"Yang","year":"2015","journal-title":"J. Environ. Inform."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.spasta.2016.05.005","article-title":"Emerging patterns in multi-sourced data modeling uncertainty","volume":"18","author":"Kolovos","year":"2016","journal-title":"Spat. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00477-014-1005-1","article-title":"BME prediction of continuous geographical properties using auxiliary variables","volume":"30","author":"Yang","year":"2016","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00477-015-1078-5","article-title":"A GIS tool for spatiotemporal modeling under a knowledge synthesis framework","volume":"30","author":"Yu","year":"2016","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s00477-017-1419-7","article-title":"Bayesian maximum entropy approach and its applications: A review","volume":"32","author":"He","year":"2017","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.atmosenv.2017.10.062","article-title":"High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique","volume":"173","author":"Xiao","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2013.03.021","article-title":"Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method","volume":"135","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_63","first-page":"54","article-title":"Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing","volume":"32","author":"Gao","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.1002\/2015JD024571","article-title":"Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method","volume":"121","author":"Tang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Qin, D., and Ding, Y. (2016). Climate and Environmental Change in China 1951\u20132012, Springer-Verlag.","DOI":"10.1007\/978-3-662-48482-1"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1080\/01431160701294653","article-title":"Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information","volume":"28","author":"Coops","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.rse.2018.12.008","article-title":"A practical method for reducing terrain effect on land surface temperature using random forest regression","volume":"221","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_68","unstructured":"Christakos, G., Bogaert, P., and Serre, M. (2012). Temporal GIS: Advanced Functions for Field-Based Applications, Springer Science & Business Media."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2018.04.005","article-title":"A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud","volume":"141","author":"Zeng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.06.003","article-title":"An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States","volume":"142","author":"Pede","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.ecolind.2017.08.007","article-title":"Improved space-time mapping of PM2.5 distribution using a domain transformation method","volume":"85","author":"Christakos","year":"2018","journal-title":"Ecol. Indic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2610\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:35Z","timestamp":1760189555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":71,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222610"],"URL":"https:\/\/doi.org\/10.3390\/rs11222610","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,7]]}}}