{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T02:24:47Z","timestamp":1768616687440,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a vital physical parameter in geoscience research and plays a prominent role in surface and atmosphere interaction. Due to technical restrictions, the spatiotemporal resolution of satellite remote sensing LST data is relatively low, which limits the potential applications of these data. An LST downscaling algorithm can effectively alleviate this problem and endow the LST data with more spatial details. Considering the spatial nonstationarity, downscaling algorithms have been gradually developed from least square models to geographical models. The current geographical LST downscaling models only consider the linear relationship between LST and auxiliary parameters, whereas non-linear relationships are neglected. Our study addressed this issue by proposing an LST downscaling algorithm based on a non-linear geographically weighted regressive (NL-GWR) model and selected the optimal combination of parameters to downscale the spatial resolution of a moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m. We selected Jinan city in north China and Wuhan city in south China from different seasons as study areas and used Landsat 8 images as reference data to verify the downscaling LST. The results indicated that the NL-GWR model performed well in all the study areas with lower root mean square error (RMSE) and mean absolute error (MAE), rather than the linear model.<\/jats:p>","DOI":"10.3390\/rs13081580","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T11:21:38Z","timestamp":1618831298000},"page":"1580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0513-2980","authenticated-orcid":false,"given":"Shumin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Youming","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Kaixiang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5302-9849","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xiaobo","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xiuhong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"ref_1","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_2","first-page":"68","article-title":"A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States","volume":"10","author":"Weng","year":"2008","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Peng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1002\/hyp.6679","article-title":"Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain","volume":"21","author":"Sun","year":"2007","journal-title":"Hydrol. Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2014.09.013","article-title":"Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature","volume":"156","author":"Wu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3531","DOI":"10.1080\/01431160802562289","article-title":"Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM+ imagery","volume":"30","author":"Rajasekar","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16293","DOI":"10.3390\/rs71215826","article-title":"Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring","volume":"7","author":"Wu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2015.04.014","article-title":"An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20 year NOAA AVHRR data over Canada","volume":"164","author":"Kandasamy","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.rse.2010.08.003","article-title":"Phenological change detection while accounting for abrupt and gradual trends in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ecolind.2015.07.029","article-title":"Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China","volume":"60","author":"Luo","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.ecolmodel.2015.05.022","article-title":"Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes","volume":"312","author":"Mo","year":"2015","journal-title":"Ecol. Model."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.rse.2007.01.004","article-title":"Cross-scalar satellite phenology from ground, Landsat, and MODIS data","volume":"109","author":"Fisher","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2014.08.009","article-title":"Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery","volume":"97","author":"Weng","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5298","DOI":"10.1002\/2017WR020700","article-title":"Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach","volume":"53","author":"Sun","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2012.12.014","article-title":"Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats","volume":"131","author":"Zhan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","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":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., and He, K. (2014). Learning a Deep Convolutional Network for Image Super-Resolution, Springer.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_21","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2014). Accelerating the Super-Resolution Convolutional Neural Network. European Conference on Computer Vision, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., and Huszar, F. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, Y., Gong, Z., Zhong, P., and Shan, J. (2017). Unsupervised Representation Learning with Deep Convolutional Neural Network for Remote Sensing Images. International Conference on Image and Graphics, Springer.","DOI":"10.1007\/978-3-319-71589-6_9"},{"key":"ref_24","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv."},{"key":"ref_25","unstructured":"Berthelot, D., Schumm, T., and Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks. arXiv."},{"key":"ref_26","first-page":"427","article-title":"Single frame infrared image super-resolution algorithm based on generative adversarial nets","volume":"37","author":"Shao","year":"2018","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1080\/014311698214578","article-title":"Pixel block intensity modulation: Adding spatial detail to TM band 6 thermal imagery","volume":"19","author":"Guo","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2009.07.017","article-title":"Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation","volume":"113","author":"Stathopoulou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multisensor multiresolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2012.12.020","article-title":"Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach","volume":"131","author":"Deng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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\u2013radiometric temperature relationship","volume":"85","author":"Kustas","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TGRS.2009.2033180","article-title":"Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data with an Artificial Neural Network","volume":"48","author":"Yang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1080\/01431161.2012.725957","article-title":"Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: A case study in Shanghai, China","volume":"34","author":"Zhu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431161.2016.1145363","article-title":"Disaggregation of LST over India: Comparative analysis of different vegetation indices","volume":"37","author":"Eswar","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"162085","DOI":"10.1109\/ACCESS.2020.3021034","article-title":"Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas","volume":"8","author":"Qi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, R., Gao, W., and Peng, W. (2020). Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou. Remote Sens., 12.","DOI":"10.3390\/rs12132134"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"633","DOI":"10.3390\/rs10040633","article-title":"Downscaling of ASTER Thermal Images Based on Geographically Weighted Regression Kriging","volume":"10","author":"Osvaldo","year":"2018","journal-title":"Remote Sens."},{"key":"ref_41","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_42","first-page":"6458","article-title":"Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model","volume":"54","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"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":"84","article-title":"Radiance-based validation of land surface temperature products derived from Collection 6 MODIS thermal infrared data","volume":"70","author":"Duan","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.02.003","article-title":"Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces","volume":"126","author":"Duan","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","first-page":"1","article-title":"The Retrieval of Land Surface Temperature and Emissivity byRemote Sensing Data: Theory and Digital Simulation","volume":"2","author":"Qinhuo","year":"1998","journal-title":"J. Remote Sens."},{"key":"ref_47","first-page":"5113","article-title":"Estimation of sea surface temperatures from two infrared window measurements with different absorption","volume":"80","year":"1975","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/01431168708954793","article-title":"The impact of spectral emissivity on the measurement of land surface temperature from a satellite","volume":"8","author":"Becker","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1080\/01431160010006971","article-title":"A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region","volume":"22","author":"Qin","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"3285","article-title":"The Analysis of Consistency between HJ-1B and Landsat 5 TM for Retrieving LST Based on the Single-Channel Algorithm","volume":"30","author":"Luo","year":"2010","journal-title":"Spect. Anal."},{"key":"ref_51","first-page":"964","article-title":"Land surface temperature retrieval from Landsat 8 thermal infrared data using mono-window algorithm","volume":"19","author":"Hu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","first-page":"251","article-title":"Determining the impacts of land cover\/use categories on land surface temperature using landsat8-oli","volume":"41","author":"Balcik","year":"2016","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.3390\/rs70404268","article-title":"An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/0034-4257(91)90069-I","article-title":"Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5","volume":"38","author":"Sobrino","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2008.2001636","article-title":"Split-Window Coefficients for Land Surface Temperature Retrieval From Low-Resolution Thermal Infrared Sensors","volume":"5","author":"Sobrino","year":"2008","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s00704-011-0464-2","article-title":"Spatio-temporal prediction of daily temperatures using time-series of MODIS LST image","volume":"107","author":"Hengl","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1080\/026937996137909","article-title":"The geography of parameter space: An investigation of spatial non-stationarity","volume":"10","author":"Fotheringham","year":"1996","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/1540-5907.00013","article-title":"The Local Voter: A Geographically Weighted Approach to Ecological Inference","volume":"47","author":"Calvo","year":"2003","journal-title":"Am. J. Political Sci."},{"key":"ref_59","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_60","first-page":"23","article-title":"Downscaling land surface temperatures with multi-spectral and multi-resolution images","volume":"18","author":"Zhan","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_61","unstructured":"Schnell, J.A. (1974). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. Great Plains Corridor, NASA\/GSFC Type III Final Report."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_63","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_64","unstructured":"Kawamura, M. (1996, January 9\u201319). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data roc. Proceedings of the 51st Annual Conference of the Japan Society of Civil Engineers, Vienna, Austria."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1111\/j.0002-9092.2004.600_2.x","article-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","volume":"86","author":"McMillen","year":"2004","journal-title":"Am. J. Agric. Econ."},{"key":"ref_67","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":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"849","DOI":"10.3390\/rs8100849","article-title":"Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values","volume":"8","author":"Gemma","year":"2016","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1080\/01431161.2018.1547448","article-title":"Analysis of remotely-sensed ecological indexes\u2019 influence on urban thermal environment dynamic using an integrated ecological index: A case study of Xi\u2019an, China","volume":"40","author":"Zhu","year":"2019","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:50Z","timestamp":1760161790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081580"],"URL":"https:\/\/doi.org\/10.3390\/rs13081580","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,19]]}}}