{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T04:28:51Z","timestamp":1772684931178,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC0505702"],"award-info":[{"award-number":["2017YFC0505702"]}]},{"name":"National Key R&amp;D Program of China","award":["2019YFB2102902"],"award-info":[{"award-number":["2019YFB2102902"]}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["QYZDB-SSW-DQC034"],"award-info":[{"award-number":["QYZDB-SSW-DQC034"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201904910497"],"award-info":[{"award-number":["201904910497"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth\u2019s land\u2013atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013\u20132018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29\u20132.65 K), root mean square error (2.92\u20133.25 K), and coefficients of determination (0.82\u20130.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition.<\/jats:p>","DOI":"10.3390\/rs13050971","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Two-Step Integrated MLP-GTWR Method to Estimate 1 km Land Surface Temperature with Complete Spatial Coverage in Humid, Cloudy Regions"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1437-3575","authenticated-orcid":false,"given":"Zhen","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin F.","family":"Zaitchik","sequence":"additional","affiliation":[{"name":"Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongzhe","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiping","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1002\/joc.4766","article-title":"A statistical framework for estimating air temperature using MODIS land surface temperature data: Estimating air temperature using modis land surface temperature","volume":"37","author":"Janatian","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/hess-17-39-2013","article-title":"Exploiting remote sensing land surface temperature in distributed hydrological modelling: The example of the Continuum Model","volume":"17","author":"Silvestro","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"024010","DOI":"10.1088\/1748-9326\/aa9e93","article-title":"Impacts of land cover transitions on surface temperature in China based on satellite observations","volume":"13","author":"Zhang","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1002\/met.287","article-title":"Remote sensing land surface temperature for meteorology and climatology: A review","volume":"18","author":"Tomlinson","year":"2011","journal-title":"Meteorol. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s11269-013-0337-9","article-title":"Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application","volume":"27","author":"Srivastava","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/LGRS.2017.2753203","article-title":"Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15","volume":"14","author":"Colliander","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, T., Zhao, T., Shi, J., Wu, S., Liu, D., Qin, H., and Zhao, K. (2017). High-resolution mapping of freeze\/thaw status in China via fusion of MODIS and AMSR2 data. Remote Sens., 9.","DOI":"10.3390\/rs9121339"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2016.12.009","article-title":"Burn severity mapping from landsat MESMA fraction images and land surface temperature","volume":"190","author":"Quintano","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"064016","DOI":"10.1088\/1748-9326\/ab2103","article-title":"Using the evaporative stress index to monitor flash drought in Australia","volume":"14","author":"Nguyen","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.12.041501","article-title":"Application of MODIS land surface temperature data: A systematic literature review and analysis","volume":"12","author":"Phan","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_12","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. Atmospheres"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Yoo, C., Im, J., Cho, D., Yokoya, N., Xia, J., and Bechtel, B. (2020). Estimation of all-weather 1 Km MODIS land surface temperature for humid summer days. Remote Sens., 12.","DOI":"10.3390\/rs12091398"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111462","DOI":"10.1016\/j.rse.2019.111462","article-title":"Estimating daily average surface air temperature using satellite land surface temperature and top-of-atmosphere radiation products over the tibetan plateau","volume":"234","author":"Rao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Ghafarian Malamiri, H.R., Rousta, I., Olafsson, H., Zare, H., and Zhang, H. (2018). Gap-filling of MODIS time series land surface temperature (LST) products using singular spectrum analysis (SSA). Atmosphere, 9.","DOI":"10.3390\/atmos9090334"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, J., Tan, J., Jin, R., Li, X., and Zhang, Y. (2018). Reconstruction of MODIS land surface temperature products based on multi-temporal information. Remote Sens., 10.","DOI":"10.3390\/rs10071112"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.010","article-title":"Creating a seamless 1 Km 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_20","doi-asserted-by":"crossref","unstructured":"Yu, W., Tan, J., Ma, M., Li, X., She, X., and Song, Z. (2019). An effective similar-pixel reconstruction of the high-frequency cloud-covered areas of Southwest China. Remote Sens., 11.","DOI":"10.3390\/rs11030336"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1029\/2018JD028976","article-title":"Recovering land surface temperature under cloudy skies considering the solar-cloud-satellite geometry: Application to MODIS and landsat-8 data","volume":"124","author":"Wang","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6393","DOI":"10.1029\/2018JD030213","article-title":"Regional impacts of urban irrigation on surface heat fluxes and rainfall in Central Arizona","volume":"124","author":"Yang","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"111191","DOI":"10.1016\/j.rse.2019.05.010","article-title":"A Physical model-based method for retrieving urban land surface temperatures under cloudy conditions","volume":"230","author":"Fu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111863","DOI":"10.1016\/j.rse.2020.111863","article-title":"Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach","volume":"246","author":"Long","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2019.02.006","article-title":"Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures","volume":"224","author":"Xia","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1002\/wcc.8","article-title":"State-of-the-art with regional climate models","volume":"1","author":"Rummukainen","year":"2010","journal-title":"WIREs Clim. Chang."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1080\/01431161.2018.1508920","article-title":"A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements","volume":"40","author":"Huang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TGRS.2019.2892417","article-title":"A Method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","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_33","first-page":"D04113","article-title":"Land surface temperature from Ka Band (37 GHz) passive microwave observations","volume":"114","author":"Holmes","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1002\/2016JD026144","article-title":"Inversion of AMSR-E observations for land surface temperature estimation: 1. methodology and evaluation with station temperature","volume":"122","author":"Prigent","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1080\/01431160110063788","article-title":"On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions","volume":"22","author":"Owe","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8839","DOI":"10.1029\/98JD00275","article-title":"Physical retrieval of land surface temperature using the special sensor microwave imager","volume":"103","author":"Weng","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.1109\/TGRS.2019.2895351","article-title":"A Geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions","volume":"57","author":"Peng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.isprsjprs.2020.07.014","article-title":"Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data","volume":"167","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"70","article-title":"Nighttime lights compositing using the VIIRS day-night band: Preliminary results","volume":"35","author":"Baugh","year":"2013","journal-title":"Proc. Asia-Pac. Adv. Netw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-02810-8","article-title":"The mark of vegetation change on earth\u2019s surface energy balance","volume":"9","author":"Duveiller","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"12459","DOI":"10.3390\/rs70912459","article-title":"Mapping impervious surface distribution with integration of SNNP VIIRS-DNB and MODIS NDVI Data","volume":"7","author":"Guo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.agrformet.2006.02.011","article-title":"Overview of ChinaFLUX and evaluation of its eddy covariance measurement","volume":"137","author":"Yu","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ermida, S.L., Soares, P., Mantas, V., G\u00f6ttsche, F.-M., and Trigo, I.F. (2020). Google Earth Engine open-source code for land surface temperature estimation from the landsat series. Remote Sens., 12.","DOI":"10.3390\/rs12091471"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2019.02.020","article-title":"Validation of collection 6 MODIS land surface temperature product using in situ measurements","volume":"225","author":"Duan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"G\u00f6ttsche, F.-M., Olesen, F.-S., Trigo, I.F., Bork-Unkelbach, A., and Martin, M.A. (2016). Long term validation of land surface temperature retrieved from MSG\/SEVIRI with continuous in-situ measurements in Africa. Remote Sens., 8.","DOI":"10.3390\/rs8050410"},{"key":"ref_50","first-page":"D11109","article-title":"Estimation of surface long wave radiation and broadband emissivity using moderate resolution imaging spectroradiometer (modis) land surface temperature\/emissivity products","volume":"110","author":"Wang","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_51","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2017.12.018","article-title":"Satellite-based mapping of daily high-resolution ground PM2. 5 in China via space-time regression modeling","volume":"206","author":"He","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_54","first-page":"431","article-title":"Geographically weighted regression","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. Stat."},{"key":"ref_55","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.jhydrol.2011.02.020","article-title":"Uncertainty of downscaling method in quantifying the impact of climate change on hydrology","volume":"401","author":"Chen","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.jhydrol.2006.08.006","article-title":"Resampling of regional climate model output for the simulation of extreme river flows","volume":"332","author":"Leander","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/TGRS.2013.2251887","article-title":"Measurement and simulation of topographic effects on passive microwave remote sensing over mountain areas: A case study from the Tibetan Plateau","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.rse.2016.03.037","article-title":"Assessing uncertainty and sensor biases in passive microwave data across high mountain Asia","volume":"181","author":"Smith","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/36.58971","article-title":"Land surface temperature derived from the SSM\/I passive microwave brightness temperatures","volume":"28","author":"McFarland","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Forte, G.F., Camps, A., Tarongi, J.M., and Vall-Llossera, M. (2012, January 22\u201327). Study of radio frequency interference effects on radiometry bands in urban environments. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351364"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103907","DOI":"10.1016\/j.landurbplan.2020.103907","article-title":"Heatwave-induced human health risk assessment in megacities based on heat stress-social vulnerability-human exposure framework","volume":"203","author":"Dong","year":"2020","journal-title":"Landsc. Urban Plan."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TNN.2009.2032543","article-title":"Feature selection for MLP neural network: The use of random permutation of probabilistic outputs","volume":"20","author":"Yang","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1289\/ehp.8288","article-title":"Dermal exposure to jet fuel JP-8 significantly contributes to the production of urinary naphthols in fuel-cell maintenance workers","volume":"114","author":"Chao","year":"2006","journal-title":"Environ. Health Perspect."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5699","DOI":"10.1002\/2015JD024402","article-title":"Toward \u201cAll Weather,\u201d long record, and real-time land surface temperature retrievals from microwave satellite observations: Microwave land surface temperature","volume":"121","author":"Prigent","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s11430-007-2053-x","article-title":"A Physics-based statistical algorithm for retrieving land surface temperature from amsr-e passive microwave data","volume":"50","author":"Mao","year":"2007","journal-title":"Sci. China Ser. Earth Sci."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Parinussa, R., Lakshmi, V., Johnson, F., and Sharma, A. (2016). Comparing and combining remotely sensed land surface temperature products for improved hydrological applications. Remote Sens., 8.","DOI":"10.3390\/rs8020162"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/971\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:49Z","timestamp":1760160769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":67,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050971"],"URL":"https:\/\/doi.org\/10.3390\/rs13050971","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}