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Based on the strong time-series correlation of pixels at different scales (average Pearson correlation coefficients &gt; 0.95), a new long time-series spatiotemporal fusion model (LOTSFM) is proposed for land surface temperature data. The model is distinguished by the following attributes: it employs an extended input framework to sidestep selection biases and enhance result stability while also integrating Julian Day for estimating sensor difference term variations at each pixel location. From 2013 to 2022, 79 pairs of Landsat8\/9 and MODIS images were collected as extended inputs. Multiple rounds of cross-validation were conducted in Beijing, Shanghai, and Guangzhou with an all-round performance assessment (APA), and the average root-mean-square error (RMSE) was 1.60 \u00b0C, 2.16 \u00b0C and 1.71 \u00b0C, respectively, which proved the regional versatility of LOTSFM. The validity of the sensor difference estimation based on Julian days was verified, and the RMSE accuracy significantly improved (p &lt; 0.05). The accuracy and time consumption of five different fusion models were compared, which proved that LOTSFM has stable accuracy performance and a fast fusion process. Therefore, LOTSFM can provide higher spatiotemporal resolution (30 m) land surface temperature research data for the evolution of urban thermal environments and has great application potential in monitoring anthropogenic heat pollution and extreme thermal phenomena.<\/jats:p>","DOI":"10.3390\/rs15215211","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T09:28:13Z","timestamp":1698917293000},"page":"5211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Spatiotemporal Fusion Model of Land Surface Temperature Based on Pixel Long Time-Series Regression: Expanding Inputs for Efficient Generation of Robust Fused Results"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6944-977X","authenticated-orcid":false,"given":"Shize","family":"Chen","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5073-1694","authenticated-orcid":false,"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"}]},{"given":"Xinli","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5440-4081","authenticated-orcid":false,"given":"Qingyan","family":"Meng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"}]},{"given":"Jiangkang","family":"Qian","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jianfeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1021\/es2030438","article-title":"Surface urban heat island across 419 global big cities","volume":"46","author":"Peng","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.enbuild.2014.07.022","article-title":"On the energy impact of urban heat island and global warming on buildings","volume":"82","author":"Santamouris","year":"2014","journal-title":"Energy Build."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Singh, N., Singh, S., and Mall, R. (2020). Urban Ecology, Elsevier.","DOI":"10.1289\/isee.2020.virtual.P-0987"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11160","DOI":"10.1038\/srep11160","article-title":"The footprint of urban heat island effect in China","volume":"5","author":"Zhou","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.rse.2017.09.019","article-title":"Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China","volume":"204","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"103241","article-title":"Coherency and phase delay analyses between land cover and climate across Italy via the least-squares wavelet software","volume":"118","author":"Ghaderpour","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J., Hagan, D.F., and Liu, Y. (2021). Global Land Surface Temperature Change (2003\u20132017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13010044"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guan, Y., Quan, J., Ma, T., Cao, S., Xu, C., and Guo, J. (2023). Identifying Major Diurnal Patterns and Drivers of Surface Urban Heat Island Intensities across Local Climate Zones. Remote Sens., 15.","DOI":"10.3390\/rs15205061"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2011.04.042","article-title":"Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects","volume":"117","author":"Sobrino","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1080\/15481603.2022.2127463","article-title":"How does the ambient environment respond to the industrial heat island effects? An innovative and comprehensive methodological paradigm for quantifying the varied cooling effects of different landscapes","volume":"59","author":"Gao","year":"2022","journal-title":"GIsci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2015.11.005","article-title":"Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China","volume":"172","author":"Shen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"118383","DOI":"10.1016\/j.envpol.2021.118383","article-title":"Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China","volume":"292","author":"Meng","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meng, Q., Liu, W., Zhang, L., Allam, M., Bi, Y., Hu, X., Gao, J., Hu, D., and Jancs\u00f3, T. (2022). Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China. Remote Sens., 14.","DOI":"10.3390\/rs14174340"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30 m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, J., and Chen, B. (2020). Global revisit interval analysis of Landsat-8-9 and Sentinel-2a-2b data for terrestrial monitoring. Sensors, 20.","DOI":"10.3390\/s20226631"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"113222","DOI":"10.1016\/j.rse.2022.113222","article-title":"Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement","volume":"280","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8323","DOI":"10.1080\/01431161.2014.985396","article-title":"Fast spatiotemporal data fusion: Merging LISS III with AWiFS sensor data","volume":"35","author":"Rao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1109\/TGRS.2017.2683444","article-title":"Fusion of Landsat 8 OLI and Sentinel-2 MSI Data","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T. (2018). Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MGRS.2021.3050782","article-title":"Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques","volume":"9","author":"Wu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"063507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","unstructured":"Xue, J., Leung, Y., and Fung, T. (2017). A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images. Remote Sens., 9.","DOI":"10.3390\/rs9121310"},{"key":"ref_30","first-page":"102745","article-title":"Progressive spatiotemporal image fusion with deep neural networks","volume":"108","author":"Cai","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal Reflectance Fusion via Sparse Representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230829","article-title":"HCNNet: A Hybrid Convolutional Neural Network for Spatiotemporal Image Fusion","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.rse.2017.05.011","article-title":"Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps","volume":"196","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"111973","DOI":"10.1016\/j.rse.2020.111973","article-title":"FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details","volume":"248","author":"Guo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111537","DOI":"10.1016\/j.rse.2019.111537","article-title":"SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2019.03.012","article-title":"An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series","volume":"227","author":"Liu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103206","DOI":"10.1016\/j.pce.2022.103206","article-title":"A high spatiotemporal resolution land surface temperature research over Qinghai-Tibet Plateau for 2000\u20132020","volume":"128","author":"Chen","year":"2022","journal-title":"Phys. Chem. Earth"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/LGRS.2012.2227930","article-title":"Generating High Spatiotemporal Resolution Land Surface Temperature for Urban Heat Island Monitoring","volume":"10","author":"Huang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2017.12.003","article-title":"An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes","volume":"206","author":"Quan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TGRS.2020.2999943","article-title":"Spatiotemporal fusion of land surface temperature based on a convolutional neural network","volume":"59","author":"Yin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3390\/rs70201798","article-title":"Comparison of Spatiotemporal Fusion Models: A Review","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1109\/TGRS.2014.2311445","article-title":"Operational Data Fusion Framework for Building Frequent Landsat-Like Imagery","volume":"52","author":"Wang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1002\/2015JD024354","article-title":"Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands","volume":"121","author":"Quan","year":"2016","journal-title":"J. Geophys. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.scitotenv.2016.11.069","article-title":"Variation in the urban vegetation, surface temperature, air temperature nexus","volume":"579","author":"Sheri","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5179","DOI":"10.1109\/TGRS.2020.2973762","article-title":"A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"100062","DOI":"10.1016\/j.srs.2022.100062","article-title":"Spatiotemporal image fusion using multiscale attention-aware two-stream convolutional neural networks","volume":"6","author":"Chen","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"112009","DOI":"10.1016\/j.rse.2020.112009","article-title":"Virtual image pair-based spatio-temporal fusion","volume":"249","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113616","DOI":"10.1016\/j.rse.2023.113616","article-title":"ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications","volume":"294","author":"Chen","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230439","article-title":"A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network","volume":"60","author":"Tan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","first-page":"4688","article-title":"A generalized single-channel method for retrieving land surface temperature from remote sensing data","volume":"108","author":"Sobrino","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"9829","DOI":"10.3390\/rs6109829","article-title":"Land Surface Temperature Retrieval from Landsat 8 TIRS\u2014Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method","volume":"6","author":"Yu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"11244","DOI":"10.3390\/rs61111244","article-title":"Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive","volume":"6","author":"Cook","year":"2014","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2006.06.026","article-title":"New refinements and validation of the MODIS land-surface temperature\/emissivity products","volume":"112","author":"Wan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/0143116031000116417","article-title":"Quality assessment and validation of the MODIS global land surface temperature","volume":"25","author":"Wan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"9885","DOI":"10.1109\/JSTARS.2022.3220897","article-title":"A Comprehensive Flexible Spatiotemporal DAta Fusion Method (CFSDAF) for Generating High Spatiotemporal Resolution Land Surface Temperature in Urban Area","volume":"15","author":"Shi","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.3390\/rs4072033","article-title":"The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China","volume":"4","author":"Xiong","year":"2012","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1111\/j.1600-0668.2006.00434.x","article-title":"Field study of a thermal environment and adaptive model in Shanghai","volume":"16","author":"Ye","year":"2006","journal-title":"Indoor Air"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2017.10.046","article-title":"Spatio-temporal fusion for daily Sentinel-2 images","volume":"204","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"108452","DOI":"10.1016\/j.fcr.2022.108452","article-title":"An enhanced spatiotemporal fusion method\u2014Implications for DNN based time-series LAI estimation by using Sentinel-2 and MODIS","volume":"279","author":"Li","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_68","first-page":"102333","article-title":"Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)","volume":"100","author":"Qiu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"113309","DOI":"10.1016\/j.rse.2022.113309","article-title":"VSDF: A variation-based spatiotemporal data fusion method","volume":"283","author":"Xu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2018.06.010","article-title":"Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas","volume":"215","author":"Peng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1080\/03610926.2021.1934032","article-title":"A minimum matrix valued risk estimator combining restricted and ordinary least squares estimators","volume":"52","author":"Mirezi","year":"2023","journal-title":"Commun. Stat.-Theor. Methods"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.scitotenv.2018.11.267","article-title":"Land use change, urbanization, and change in landscape pattern in a metropolitan area","volume":"655","author":"Dadashpoor","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"4367","DOI":"10.1080\/01431161.2013.777488","article-title":"A spatial and temporal reflectance fusion model considering sensor observation differences","volume":"34","author":"Shen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/LGRS.2012.2185034","article-title":"Robustness of Annual Cycle Parameters to Characterize the Urban Thermal Landscapes","volume":"9","author":"Bechtel","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Jia, D., Cheng, C., Song, C., Shen, S., Ning, L., and Zhang, T. (2021). A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions. Remote Sens., 13.","DOI":"10.3390\/rs13040645"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Jia, D., Song, C., Cheng, C., Shen, S., Ning, L., and Hui, C. (2020). A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12040698"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"112325","DOI":"10.1016\/j.rse.2021.112325","article-title":"Blocks-removed spatial unmixing for downscaling MODIS images","volume":"256","author":"Wang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_80","first-page":"1","article-title":"A Flexible Object-Level Processing Strategy to Enhance the Weight Function-Based Spatiotemporal Fusion Method","volume":"60","author":"Guo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"113002","DOI":"10.1016\/j.rse.2022.113002","article-title":"A novel framework to assess all-round performances of spatiotemporal fusion models","volume":"274","author":"Zhu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1007\/s12665-016-5549-x","article-title":"Hot dark spot index method based on multi-angular remote sensing for leaf area index retrieval","volume":"75","author":"Meng","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern. Recognit."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"112770","DOI":"10.1016\/j.rse.2021.112770","article-title":"A reliable and adaptive spatiotemporal data fusion method for blending multi-spatiotemporal-resolution satellite images","volume":"268","author":"Shi","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"112437","DOI":"10.1016\/j.rse.2021.112437","article-title":"A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature","volume":"260","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Liu, M., Ke, Y., Yin, Q., Chen, X., and Im, J. (2019). Comparison of five spatio-temporal satellite image fusion models over landscapes with various spatial heterogeneity and temporal variation. Remote Sens., 11.","DOI":"10.3390\/rs11222612"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"112130","DOI":"10.1016\/j.rse.2020.112130","article-title":"Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction","volume":"252","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.scs.2017.06.006","article-title":"Urban thermal risk reduction: Developing and implementing spatially explicit services for resilient cities","volume":"34","author":"Keramitsoglou","year":"2017","journal-title":"Sustain. Cities Soc."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e2021JD036131","DOI":"10.1029\/2021JD036131","article-title":"Understanding the Impact of Urban Expansion and Lake Shrinkage on Summer Climate and Human Thermal Comfort in a Land-Water Mosaic Area","volume":"127","author":"Deng","year":"2022","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_91","first-page":"103195","article-title":"Cloud-covered MODIS LST reconstruction by combining assimilation data and remote sensing data through a nonlocality-reinforced network","volume":"117","author":"Gong","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"116290","DOI":"10.1016\/j.geoderma.2022.116290","article-title":"Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire","volume":"430","author":"Marcos","year":"2023","journal-title":"Geoderma"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Li, Y., Yan, W., An, S., Gao, W., Jia, J., Tao, S., and Wang, W. (2023). A Spatio-Temporal Fusion Framework of UAV and Satellite Imagery for Winter Wheat Growth Monitoring. Drones, 7.","DOI":"10.3390\/drones7010023"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Arabi Aliabad, F., Ghafarian Malmiri, H., Sarsangi, A., Sekertekin, A., and Ghaderpour, E. (2023). Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15164053"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5211\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:16:06Z","timestamp":1760130966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,2]]},"references-count":94,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215211"],"URL":"https:\/\/doi.org\/10.3390\/rs15215211","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,2]]}}}