{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T18:17:36Z","timestamp":1783189056214,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T00:00:00Z","timestamp":1585267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Science and Technology Planning Project of Guangdong Province","award":["Nos. 2018B020207012 and 2018B020207002"],"award-info":[{"award-number":["Nos. 2018B020207012 and 2018B020207002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41901371"],"award-info":[{"award-number":["No. 41901371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["No. GML2019ZD0301"],"award-info":[{"award-number":["No. GML2019ZD0301"]}]},{"DOI":"10.13039\/100012541","name":"Guangdong Innovative and Entrepreneurial Research Team Program","doi-asserted-by":"publisher","award":["No. 2016ZT06D336"],"award-info":[{"award-number":["No. 2016ZT06D336"]}],"id":[{"id":"10.13039\/100012541","id-type":"DOI","asserted-by":"publisher"}]},{"name":"GDAS' Project of Science and Technology Development","award":["Nos. 2018GDASCX-0101 and 2019GDASYL-0302001"],"award-info":[{"award-number":["Nos. 2018GDASCX-0101 and 2019GDASYL-0302001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.<\/jats:p>","DOI":"10.3390\/rs12071082","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6330-7948","authenticated-orcid":false,"given":"Jianhui","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Guangdong University of Education, Guangzhou 510310, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-0412","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongda","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiwen","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-3943","authenticated-orcid":false,"given":"Wenlong","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9354-0457","authenticated-orcid":false,"given":"Binghao","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.rse.2012.11.007","article-title":"Temperature-land cover interactions: The inversion of urban heat island phenomenon in desert city areas","volume":"130","author":"Lazzarini","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.scitotenv.2017.07.217","article-title":"Temporal trends of surface urban heat islands and associated determinants in major Chinese cities","volume":"609","author":"Yao","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4762","DOI":"10.1109\/JSTARS.2015.2468594","article-title":"Land Surface Temperature and Surface Air Temperature in Complex Terrain","volume":"8","author":"Mutiibwa","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.scitotenv.2018.06.209","article-title":"Patterns of land change and their potential impacts on land surface temperature change in Yangon, Myanmar","volume":"643","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.scitotenv.2018.03.350","article-title":"Effects of urban form on the urban heat island effect based on spatial regression model","volume":"634","author":"Yin","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zawadzka, J., Corstanje, R., Harris, J.A., and Truckell, I. (2019). Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data. Int. J. Digit. Earth.","DOI":"10.1080\/17538947.2019.1593527"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1080\/014311697219079","article-title":"Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect","volume":"18","author":"Lo","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/TGRS.2010.2060342","article-title":"Sharpening Thermal Imageries: A Generalized Theoretical Framework From an Assimilation Perspective","volume":"49","author":"Zhan","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2011.05.027","article-title":"Downscaling land surface temperature for urban heat island diurnal cycle analysis","volume":"117","author":"Zaksek","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1080\/01431161.2018.1489164","article-title":"Spatial downscaling of land surface temperature in combination with TVDI and elevation","volume":"40","author":"Zhang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/JSTARS.2016.2519099","article-title":"Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site","volume":"9","author":"Bisquert","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0034-4257(03)00036-1","article-title":"Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship","volume":"85","author":"Kustas","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.rse.2006.10.006","article-title":"A vegetation index based technique for spatial sharpening of thermal imagery","volume":"107","author":"Agam","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.rse.2011.03.008","article-title":"High-resolution urban thermal sharpener (HUTS)","volume":"115","author":"Dominguez","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"163","article-title":"Evaluation of the DisTrad thermal sharpening methodology for urban areas","volume":"19","author":"Essa","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"L02402","DOI":"10.1029\/2007GL032195","article-title":"Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions","volume":"35","author":"Agam","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"L02408","DOI":"10.1029\/2008GL036544","article-title":"Disaggregation of GOES land surface temperatures using surface emissivity","volume":"36","author":"Inamdar","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/JSTARS.2015.2396032","article-title":"Regression-Kriging Technique to Downscale Satellite-Derived Land Surface Temperature in Heterogeneous Agricultural Landscape","volume":"8","author":"Mukherjee","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1109\/LGRS.2008.2000739","article-title":"Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images","volume":"5","author":"Fasbender","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1109\/JSTARS.2016.2514367","article-title":"Downscaling of Landsat and MODIS Land Surface Temperature over the Heterogeneous Urban Area of Milan","volume":"9","author":"Bonafoni","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"553","DOI":"10.5721\/EuJRS20164929","article-title":"Downscaling Landsat Land Surface Temperature over the urban area of Florence","volume":"49","author":"Bonafoni","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1109\/JSTARS.2019.2919936","article-title":"Downscaling of Urban Land Surface Temperature Based on Multi-Factor Geographically Weighted Regression","volume":"12","author":"Wu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X. (2017). Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression. Remote Sens., 9.","DOI":"10.3390\/rs9080789"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cageo.2019.01.004","article-title":"Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels","volume":"124","author":"Ebrahimy","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bartkowiak, P., Castelli, M., and Notarnicola, C. (2019). Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas. Remote Sens., 11.","DOI":"10.3390\/rs11111319"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/LGRS.2013.2257668","article-title":"Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis","volume":"10","author":"Keramitsoglou","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2013.03.023","article-title":"Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration","volume":"135","author":"Bindhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1109\/JSTARS.2019.2896923","article-title":"Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature","volume":"12","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Sannier, C., and Corpetti, T. (2016). Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree. Remote Sens., 8.","DOI":"10.3390\/rs8070606"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.09.016","article-title":"Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery","volume":"219","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water Bodies\u2019 Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, R., Liu, J., and Xu, J. (2018). Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis. Sensors, 18.","DOI":"10.3390\/s18092873"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, Y., Zhong, K., Ruan, H., and Liu, X. (2016). Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China. Water, 8.","DOI":"10.3390\/w8120550"},{"key":"ref_43","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_44","first-page":"589","article-title":"A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)","volume":"5","author":"Hanqiu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","first-page":"259","article-title":"A Geostatistical Framework for Area-To-Point Spatial Interpolation","volume":"36","author":"Kyriakidis","year":"2004","journal-title":"Geogr. Anal."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TGRS.2015.2457672","article-title":"A New Geostatistical Solution to Remote Sensing Image Downscaling","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s11004-007-9129-1","article-title":"Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units","volume":"40","author":"Goovaerts","year":"2008","journal-title":"Math. Geosci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.rse.2015.06.003","article-title":"Downscaling MODIS images with area-to-point regression kriging","volume":"166","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Renard, F., Alonso, L., Fitts, Y., Hadjiosif, A., and Comby, J. (2019). Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands. Remote Sens., 11.","DOI":"10.3390\/rs11030299"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1082\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:12:31Z","timestamp":1760173951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,27]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071082"],"URL":"https:\/\/doi.org\/10.3390\/rs12071082","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,27]]}}}