CoChat
Sign Up
Cited by 55Advanced Image Fusion TechniquesSoil Moisture and Remote SensingRemote Sensing in Agriculture

The DOI matches its Crossref record.

Thermal infrared remote sensing data downscaling investigations: An overview on current status and perspectives

Open access
Pu, Ruiliang, Bonafoni, Stefania·2023·Remote Sensing Applications: Society and Environment
DOI 10.1016/j.rsase.2023.100921
Open this paper in CoChat — free account

Abstract

Land surface temperature (LST) retrieved from moderate resolution or downscaled from coarse thermal infrared (TIR) data is one of key environment parameters. Over the last four decades, most advanced remote sensing sensors/systems can acquire TIR data at a low spatial resolution but high temporal resolution. However, per different application purposes, both high spatial and temporal resolution TIR data are needed. Given that many investigations on downscaling LST (DLST) processes have been done and findings have been reported in the literature, it necessitates to have an updated review on DLST investigations of the status, trends, and challenges and to recommend future directions. An overview is provided on various polar orbits and geostationary orbits' satellite TIR sensors/systems and on scaling factors’ determination and selection techniques/methods suitable for DLST processes. Existing various techniques/methods for DLST processes are presented and assessed, and limitations and future research directions are identified and recommended. In this review, several concluding remarks were made, including (1) most investigations on DLST processes used coarse spatial resolution but high temporal resolution MODIS TIR data; (2) compared to fusion-based method, the kernel-driven processes are the most frequently used thermal downscaling methods; (3) machine-learning methods have demonstrated their excellent performance and robustness in improving DLST accuracy; (4) more advanced spa…

Overview

Integrity signals

Registry

Crossref · 10.1016/j.rsase.2023.100921

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Still being checked.

Research works printed but not linked: 148

  1. Agam, 2007, A vegetation index-based technique for spatial sharpening of thermal imagery, Remote Sens. Environ., 107, 545, 10.1016/j.rse.2006.10.006 · DOI
  2. Agathangelidis, 2019, Improving the disaggregation of MODIS land surface temperatures in an urban environment: a statistical downscaling approach using high-resolution emissivity, Int. J. Rem. Sens., 40, 5261, 10.1080/01431161.2019.1579386 · DOI
  3. Amazirh, 2019, Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data, ISPRS J. Photogrammetry Remote Sens., 150, 11, 10.1016/j.isprsjprs.2019.02.004 · DOI
  4. Anderson, 2012, Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources, Remote Sens. Environ., 122, 50, 10.1016/j.rse.2011.08.025 · DOI
  5. Bai, 2019, Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land, Water Resour. Res., 55, 1105, 10.1029/2018WR024162 · DOI
  6. Bala, 2020, Thermal sharpening of MODIS land surface temperature using statistical downscaling technique in urban areas, Theor. Appl. Climatol., 141, 935, 10.1007/s00704-020-03253-w · DOI
  7. Bartkowiak, 2019, Downscaling land surface temperature from MODIS dataset with random forest approach over alpine vegetated areas, Rem. Sens., 11, 1319, 10.3390/rs11111319 · DOI
  8. Bechtel, 2012, Downscaling land surface temperature in an urban area: a case study for Hamburg, Germany, Rem. Sens., 4, 3184, 10.3390/rs4103184 · DOI
  9. Bindhu, 2013, 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, Remote Sens. Environ., 135, 118, 10.1016/j.rse.2013.03.023 · DOI
  10. Bisquert, 2016, Evaluation of disaggregation methods for downscaling MODIS land surface temperature to Landsat spatial resolution in Barrax test site, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 9, 1430, 10.1109/JSTARS.2016.2519099 · DOI
  11. Bonafoni, 2016, Downscaling of landsat and MODIS land surface temperature over the heterogeneous urban area of milan, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 9, 2019, 10.1109/JSTARS.2016.2514367 · DOI
  12. Bonafoni, 2017, Downscaling of land surface temperature using airborne high-resolution data: a case study on Aprilia, Italy, Geosci. Rem. Sens. Lett. IEEE, 14, 107, 10.1109/LGRS.2016.2630798 · DOI
  13. Bonafoni, 2016, Downscaling Landsat land surface temperature over the urban area of Florence, Eur. J. Rem. Sens., 49, 553, 10.5721/EuJRS20164929 · DOI
  14. Chen, 2012, Scale effect of vegetation-index-based spatial sharpening for thermal imagery: a simulation study by ASTER data, Geosci. Rem. Sens. Lett. IEEE, 9, 549, 10.1109/LGRS.2011.2174453 · DOI
  15. Chen, 2014, A combination of TsHARP and thin plate spline interpolation for spatial sharpening of thermal imagery, Rem. Sens., 6, 2845, 10.3390/rs6042845 · DOI
  16. Chen, 2014, A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference, Geo Spatial Inf. Sci., 17, 54, 10.1080/10095020.2014.889546 · DOI · Paper page
  17. Chybicki, 2017, Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk, Open Geosci., 9, 419, 10.1515/geo-2017-0032 · DOI
  18. Coll, 2005, Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data, Rem. Sens. Environ., 97, 288, 10.1016/j.rse.2005.05.007 · DOI
  19. Deng, 2013, Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach, Rem. Sens. Environ., 131, 262, 10.1016/j.rse.2012.12.020 · DOI
  20. Deng, 2013, Estimating very high resolution urban surface temperature using a spectral unmixing and thermal mixing approach, Int. J. Appl. Earth Obs. Geoinf., 23, 155
  21. Dominguez, 2011, High-resolution urban thermal sharpener (HUTS), Rem. Sens. Environ., 115, 1772, 10.1016/j.rse.2011.03.008 · DOI · Paper page
  22. Dozier, 1981, A method for satellite identification of surface temperature fields of subpixel resolution, Rem. Sens. Environ., 11, 221, 10.1016/0034-4257(81)90021-3 · DOI
  23. Duan, 2016, Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: case study in Northern China, IEEE Trans. Geosci. Rem. Sens., 54, 6458, 10.1109/TGRS.2016.2585198 · DOI
  24. Ebrahimy, 2019, Downscaling MODIS land surface temperature over a heterogeneous area: an investigation of machine learning techniques, feature selection, and impacts of mixed pixels, Comput. Geosci., 124, 93, 10.1016/j.cageo.2019.01.004 · DOI
  25. Essa, 2012, Evaluation of the DisTrad thermal sharpening methodology for urban areas, Int. J. Appl. Earth Obs. Geoinf., 19, 163
  26. Eswar, 2016, Disaggregation of LST over India: comparative analysis of different vegetation indices, Int. J. Rem. Sens., 37, 1035, 10.1080/01431161.2016.1145363 · DOI
  27. Fassnacht, 1997, Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper, Rem. Sens. Environ., 61, 229, 10.1016/S0034-4257(97)00005-9 · DOI
  28. Gao, 2006, On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance, IEEE Trans. Geosci. Rem. Sens., 44, 2207, 10.1109/TGRS.2006.872081 · DOI
  29. Gao, 2012, A data mining approach for sharpening thermal satellite imagery over land, Rem. Sens., 4, 3287, 10.3390/rs4113287 · DOI
  30. Gao, 2017, Localization or globalization? Determination of the optimal regression window for disaggregation of land surface temperature, IEEE Trans. Geosci. Rem. Sens., 55, 477, 10.1109/TGRS.2016.2608987 · DOI
  31. Garrigues, 2006, Quantifying spatial heterogeneity at the landscape scale using variogram models, Rem. Sens. Environ., 103, 81, 10.1016/j.rse.2006.03.013 · DOI
  32. Garrigues, 2006, Influence of the spatial heterogeneity on the nonlinear estimation of Leaf Area Index from moderate resolution remote sensing data, Rem. Sens. Environ., 106, 286, 10.1016/j.rse.2006.07.013 · DOI
  33. Garrigues, 2008, Multivariate quantification of landscape spatial heterogeneity using variogram models, Rem. Sens. Environ., 112, 216, 10.1016/j.rse.2007.04.017 · DOI
  34. Ghosh, 2014, Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes, ISPRS J. Photogrammetry Remote Sens., 96, 76, 10.1016/j.isprsjprs.2014.07.003 · DOI · Paper page
  35. Guillevic, 2012, Land Surface Temperature product validation using NOAA's surface climate observation networks-Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS), Rem. Sens. Environ., 124, 282, 10.1016/j.rse.2012.05.004 · DOI
  36. Guo, 1998, Pixel block intensity modulation: adding spatial detail to TM band 6 thermal imagery, Int. J. Rem. Sens., 19, 2477, 10.1080/014311698214578 · DOI
  37. Guo, 2017, A weighted normalized difference water index for water extraction using landsat imagery, Int. J. Rem. Sens., 38, 5430, 10.1080/01431161.2017.1341667 · DOI
  38. Gustafson, 2003, An image-sharpening method to recover stream temperatures from ASTER images. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, Proc. SPIE, 4886, 72, 10.1117/12.462325 · DOI
  39. He, 2010, Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach, Remote Sensing Letters, 1, 213, 10.1080/01431161.2010.481681 · DOI
  40. Herrero-Huerta, 2019, Generating high-temporal and spatial resolution TIR image data, Int. J. Appl. Earth Obs. Geoinf., 78, 149
  41. Hu, 2021, Subpixel temperature estimation by information transfer with adaptive ensemble extreme learning machine (IT-AEELM), IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 14, 6743, 10.1109/JSTARS.2021.3091125 · DOI
  42. Huang, 2013, Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring, Geosci. Rem. Sens. Lett. IEEE, 10, 1011, 10.1109/LGRS.2012.2227930 · DOI
  43. Huete, 1988, A soil adjusted vegetation index (SAVI), Rem. Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X · DOI
  44. Hutengs, 2016, Downscaling land surface temperatures at regional scales with random forest regression, Rem. Sens. Environ., 178, 127, 10.1016/j.rse.2016.03.006 · DOI · Paper page
  45. Inamdar, 2009, Disaggregation of GOES land surface temperatures using surface emissivity, Geophys. Res. Lett., 36, 10.1029/2008GL036544 · DOI
  46. Jeganathan, 2011, Evaluating a thermal image sharpening model over a mixed agricultural landscape in India, Int. J. Appl. Earth Obs. Geoinf., 13, 178
  47. Jiang, 2013, Estimating LST using a vegetation-cover-based thermal sharpening technique, Geosci. Rem. Sens. Lett. IEEE, 10, 1249, 10.1109/LGRS.2013.2257667 · DOI
  48. Jiang, 2015, Downscaling GOES land surface temperature for assessing heat wave health risks, Geosci. Rem. Sens. Lett. IEEE, 12, 1605, 10.1109/LGRS.2015.2414897 · DOI
  49. Kallel, 2013, Surface temperature downscaling from multiresolution instruments based on markov models, IEEE Trans. Geosci. Rem. Sens., 51, 1588, 10.1109/TGRS.2012.2207461 · DOI
  50. Keramitsoglou, 2013, Downscaling geostationary land surface temperature imagery for urban analysis, Geosci. Rem. Sens. Lett., 10, 1253, 10.1109/LGRS.2013.2257668 · DOI
  51. Kustas, 2003, Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship, Remote Sens. Environ., 85, 429, 10.1016/S0034-4257(03)00036-1 · DOI
  52. Li, 2013, Satellite-derived land surface temperature: current status and perspectives, Rem. Sens. Environ., 131, 14, 10.1016/j.rse.2012.12.008 · DOI
  53. Li, 2017, Estimating subpixel surface heat fluxes through applying temperature-sharpening methods to MODIS data, Rem. Sens., 9, 836, 10.3390/rs9080836 · DOI
  54. Li, 2019, Selection of predictor variables in downscaling land surface temperature using random forest algorithm, IGARSS, 2019, 1817
  55. Li, 2021, Landsat-8 and Gaofen-1 image-based inversion method for the downscaled land surface temperature of rare earth mining areas, Infrared Phys. Technol., 113, 10.1016/j.infrared.2021.103658 · DOI
  56. Li, 2022, Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China, Rem. Sens., 14, 2390, 10.3390/rs14102390 · DOI
  57. Liang, 2021, An auto-adjusted kernel method for thermal sharpening with local and object-based window strategies, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 14, 3659, 10.1109/JSTARS.2021.3067349 · DOI
  58. Lillo-Saavedra, 2018, TS2uRF: a new method for sharpening thermal infrared satellite imagery, Rem. Sens., 10, 249, 10.3390/rs10020249 · DOI
  59. Liu, 2008, Downscaling thermal infrared radiance for subpixel land surface temperature retrieval, Sensors, 8, 2695, 10.3390/s8042695 · DOI
  60. Liu, 2018, Scaling effect of fused ASTER-MODIS land surface temperature in an urban environment, Sensors, 18, 4058, 10.3390/s18114058 · DOI
  61. Liu, 2012, An enhanced physical method for downscaling thermal infrared radiance, Geosci. Rem. Sens. Lett. IEEE, 9, 690, 10.1109/LGRS.2011.2178814 · DOI
  62. Liu, 2016, Estimating high-resolution urban surface temperature using a hyperspectral thermal mixing (HTM) approach, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 9, 804, 10.1109/JSTARS.2015.2459375 · DOI
  63. Liu, 2018, A thermal disaggregation model based on trapezoid interpolation, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 11, 808, 10.1109/JSTARS.2018.2790002 · DOI
  64. Liu, 2018, Assessing a scheme of spatial-temporal thermal remote-sensing sharpening for estimating regional evapotranspiration, Int. J. Rem. Sens., 39, 3111, 10.1080/01431161.2018.1434326 · DOI
  65. Liu, 2019, Spatially disaggregating satellite land surface temperature with a nonlinear model across agricultural areas, J. Geophys. Res.: Biogeosciences, 124, 3232, 10.1029/2019JG005227 · DOI
  66. Luo, 2021, Spatial downscaling of MODIS land surface temperature based on a geographically and temporally weighted autoregressive model, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 14, 7637, 10.1109/JSTARS.2021.3094184 · DOI
  67. Ma, 2016, An improved spatial and temporal reflectance unmixing model to synthesize time series of Landsat-like images, Rem. Sens., 10, 1388, 10.3390/rs10091388 · DOI
  68. Maeda, 2014, Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information, Int. J. Rem. Sens., 35, 3094, 10.1080/01431161.2014.903442 · DOI
  69. Mahour, 2017, A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration, ISPRS J. Photogrammetry Remote Sens., 126, 56, 10.1016/j.isprsjprs.2017.02.004 · DOI
  70. Mao, 2021, Resolution enhancement of remotely sensed land surface temperature: current status and perspectives, Rem. Sens., 13, 1306, 10.3390/rs13071306 · DOI · Paper page
  71. Matson, 1981, Identification of subresolution high temperature sources using a thermal IR sensor, Photogramm. Eng. Rem. Sens., 47, 1311
  72. McFeeters, 1996, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Rem. Sens., 17, 1425, 10.1080/01431169608948714 · DOI
  73. Mechri, 2014, Genetic particle filter application to land surface temperature downscaling, J. Geophys. Res. Atmos., 119, 2131, 10.1002/2013JD020354 · DOI
  74. Mechri, 2016, Downscaling Meteosat land surface temperature over a heterogeneous landscape using a data assimilation approach, Rem. Sens., 8, 586, 10.3390/rs8070586 · DOI
  75. Mitraka, 2015, Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations, Rem. Sens., 7, 4139, 10.3390/rs70404139 · DOI
  76. Mohamadi, 2019, Normalized method for land surface temperature monitoring on coastal reclaimed areas, Sensors, 19, 4836, 10.3390/s19224836 · DOI
  77. Moosavi, 2015, A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature, Rem. Sens. Environ., 169, 243, 10.1016/j.rse.2015.08.015 · DOI
  78. Mukherjee, 2014, A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape, Adv. Space Res., 54, 655, 10.1016/j.asr.2014.04.013 · DOI
  79. Mukherjee, 2015, Regression-kriging technique to downscale satellite-derived land surface temperature in heterogeneous agricultural landscape, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 8, 1245, 10.1109/JSTARS.2015.2396032 · DOI
  80. Nichol, 2009, An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis, Photogramm. Eng. Rem. Sens., 75, 547, 10.14358/PERS.75.5.547 · DOI
  81. Nishii, 1996, Enhancement of low spatial resolution image based on high resolution bands, IEEE Trans. Geosci. Rem. Sens., 34, 1151, 10.1109/36.536531 · DOI
  82. Njuki, 2020, An improved approach for downscaling coarse-resolution thermal data by minimizing the spatial averaging biases in random forest, Rem. Sens., 12, 3507, 10.3390/rs12213507 · DOI
  83. Olivera-Guerra, 2017, An operational method for the disaggregation of land surface temperature to estimate actual evapotranspiration in the arid region of Chile, ISPRS J. Photogrammetry Remote Sens., 128, 170, 10.1016/j.isprsjprs.2017.03.014 · DOI
  84. Peng, 2019, A geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions, IEEE Trans. Geosci. Rem. Sens., 57, 5012, 10.1109/TGRS.2019.2895351 · DOI
  85. Pereira, 2018, Downscaling of ASTER thermal images based on geographically weighted regression kriging, Rem. Sens., 10, 633, 10.3390/rs10040633 · DOI
  86. Pu, 2021, Assessing scaling effect in downscaling land surface temperature in a heterogenous urban environment, Int. J. Appl. Earth Obs. Geoinf., 96
  87. Pu, 2021, Reducing scaling effect on downscaled land surface temperature maps in heterogenous urban environments, Rem. Sens., 13, 5044, 10.3390/rs13245044 · DOI
  88. Pu, 2022, Correcting scaling effect in downscaling surface temperature at high resolutions with a multiple regional correction approach, Geosci. Rem. Sens. Lett. IEEE, 19, 1
  89. Qian, 2008, Relationship between normalized difference moisture index and land surface temperature, Geogr. Res., 27, 1358
  90. Qiu, 2018, A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: a case study in Southern China, Int. J. Rem. Sens., 39, 2105, 10.1080/01431161.2017.1420929 · DOI
  91. Quan, 2018, An integrated model for generating hourly landsat-like land surface temperatures over heterogeneous landscapes, Remote Sens. Environ., 206, 403, 10.1016/j.rse.2017.12.003 · DOI
  92. Rouse, 1973, Monitoring vegetation systems in the great plains with ERTS, Proc. Third ERTS Symp., 1, 48
  93. Sattari, 2018, Thermal sharpening of land surface temperature maps based on the impervious surface index with the TsHARP method to ASTER satellite data: a case study from the metropolitan Kuala Lumpur, Malaysia, Measurement, 125, 262, 10.1016/j.measurement.2018.04.092 · DOI
  94. Sismanidis, 2015, Evaluating the operational retrieval and downscaling of urban land surface temperatures, Geosci. Rem. Sens. Lett. IEEE, 12, 1312, 10.1109/LGRS.2015.2397450 · DOI
  95. Sismanidis, 2016, Assessing the capability of a downscaled urban land surface temperature time series to reproduce the spatiotemporal features of the original data, Rem. Sens., 8, 274, 10.3390/rs8040274 · DOI
  96. Sismanidis, 2017, Improving the downscaling of diurnal land surface temperatures using the annual cycle parameters as disaggregation kernels, Rem. Sens., 9, 23, 10.3390/rs9010023 · DOI
  97. Snyder, 1997, Requirements for satellite land surface temperature validation using a silt playa, Rem. Sens. Environ., 61, 279, 10.1016/S0034-4257(97)00044-8 · DOI
  98. Sobrino, 2004, Land surface temperature retrieval from MSG1-SEVIRI data, Rem. Sens. Environ., 92, 247, 10.1016/j.rse.2004.06.009 · DOI
  99. Sobrino, 2012, Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects, Remote Sens. Environ., 117, 50, 10.1016/j.rse.2011.04.042 · DOI
  100. Stathopoulou, 2009, Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation, Rem. Sens. Environ., 112, 2592, 10.1016/j.rse.2009.07.017 · DOI
  101. Tom, 1985, Spatial sharpening of Thematic Mapper data using a multiband approach, Opt. Eng., 24, 1026, 10.1117/12.7973623 · DOI
  102. Tuia, 2009, Classification of very high spatial resolution imagery using mathematical morphology and support vector machines, IEEE Trans. Geosci. Rem. Sens., 47, 3866, 10.1109/TGRS.2009.2027895 · DOI
  103. Vaculik, 2019, Downscaling of satellite land surface temperature data over urban environments, IGARSS
  104. Van Leeuwen, 1996, Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices, Rem. Sens. Environ., 55, 123, 10.1016/0034-4257(95)00198-0 · DOI
  105. Wan, 1996, A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Trans. Geosci. Rem. Sens., 34, 892, 10.1109/36.508406 · DOI
  106. Wang, 2007, NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing, Geophys. Res. Lett., 34, 10.1029/2007GL031021 · DOI
  107. Wang, 2020, Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis, ISPRS J. Photogrammetry Remote Sens., 161, 76, 10.1016/j.isprsjprs.2020.01.014 · DOI
  108. Wang, 2020, Downscale MODIS land surface temperature based on three different models to analyze surface urban heat island: a case study of Hangzhou, Rem. Sens., 12, 2134, 10.3390/rs12132134 · DOI
  109. Wang, 2020, A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat, Int. J. Rem. Sens., 41, 2986, 10.1080/01431161.2019.1697009 · DOI
  110. Weng, 2014, Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery, ISPRS J. Photogrammetry Remote Sens., 97, 78, 10.1016/j.isprsjprs.2014.08.009 · DOI
  111. Weng, 2014, Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data, Rem. Sens. Environ., 145, 55, 10.1016/j.rse.2014.02.003 · DOI
  112. White-Newsome, 2013, Validating satellite-derived land surface temperature with in situ measurements: a public health perspective, Environ. Health Perspect., 12, 925, 10.1289/ehp.1206176 · DOI
  113. Wu, 2019, Downscaling land surface temperatures using a random forest regression model with multitype predictor variables, IEEE Access, 7, 21904, 10.1109/ACCESS.2019.2896241 · DOI
  114. Wu, 2012, Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model, J. Appl. Remote Sens., 6
  115. Wu, 2015, Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring, Environ. Sci.: Process. Impacts, 17, 1396
  116. Wu, 2019, Downscaling of urban land surface temperature based on multi-factor geographically weighted regression, J. Selected Topics Appl. Earth Observ. Rem. Sens., 12, 2897
  117. Xia, 2018, Regression-then-fusion” or “fusion-then-regression”? a theoretical analysis for generating high spatiotemporal resolution land surface temperatures, Rem. Sens., 10, 1382, 10.3390/rs10091382 · DOI
  118. Xia, 2019, Combining kernel-driven and fusion-based methods to generate daily high spatial resolution land surface temperatures, Rem. Sens. Environ., 224, 259, 10.1016/j.rse.2019.02.006 · DOI
  119. Xia, 2019, Object-based window strategy in thermal sharpening, Rem. Sens., 11, 634, 10.3390/rs11060634 · DOI
  120. Xu, 2008, A new index for delineating built-up land features in satellite imagery, Int. J. Rem. Sens., 29, 4269, 10.1080/01431160802039957 · DOI
  121. Xu, 2020, Downscaling ASTER land surface temperature over urban areas with machine learning-based area-to-point regression kriging, Rem. Sens., 12, 1082, 10.3390/rs12071082 · DOI
  122. Xu, 2021, Spatial downscaling of land surface temperature based on a multi-factor geographically weighted machine learning model, Rem. Sens., 13, 1186, 10.3390/rs13061186 · DOI
  123. Xue, 2019, An unmixing-based Bayesian model for spatio-temporal satellite image fusion in heterogeneous landscapes, Rem. Sens., 11, 324, 10.3390/rs11030324 · DOI
  124. Yang, 2011, Estimation of subpixel land surface temperature using an endmember index based technique: a case examination on ASTER and MODIS temperature products over a heterogeneous area, Rem. Sens. Environ., 115, 1202, 10.1016/j.rse.2011.01.004 · DOI
  125. Yang, 2017, Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression, Rem. Sens., 9, 789, 10.3390/rs9080789 · DOI
  126. Yang, 2019, Downscaling land surface temperature using multiscale geographically weighted regression over heterogeneous landscapes in Wuhan, China, J. Selected Topics Appl. Earth Observ. Rem. Sens., 12, 5213
  127. Yang, 2020, A robust method for generating high-spatiotemporal-resolution surface reflectance by fusing MODIS and Landsat data, Rem. Sens., 12, 2312, 10.3390/rs12142312 · DOI
  128. Yao, 2020, A new approach for surface urban heat island monitoring based on machine learning algorithm and spatiotemporal fusion model, IEEE Access, 164268
  129. Yin, 2021, Spatiotemporal fusion of land surface temperature based on a convolutional neural network, Trans. Geosci. Rem. Sens., 59, 1808, 10.1109/TGRS.2020.2999943 · DOI · Paper page
  130. Yoo, 2020, Spatial downscaling of MODIS land surface temperature: recent research trends, challenges, and future directions, Kor. J. Rem. Sens., 36, 609
  131. Zakšek, 2012, Downscaling land surface temperature for urban heat island diurnal cycle analysis, Rem. Sens. Environ., 117, 114, 10.1016/j.rse.2011.05.027 · DOI
  132. Zakšek, 2009, Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments, ISPRS J. Photogrammetry Remote Sens., 64, 414, 10.1016/j.isprsjprs.2009.02.006 · DOI
  133. Zawadzka, 2020, Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data, Int. J. Digital Earth, 13, 899, 10.1080/17538947.2019.1593527 · DOI
  134. Zha, 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Rem. Sens., 24, 583, 10.1080/01431160304987 · DOI
  135. Zhai, 2020, Generating high resolution LAI based on a modified FSDAF model, Rem. Sens., 12, 150, 10.3390/rs12010150 · DOI
  136. Zhan, 2011, Sharpening thermal imageries: a generalized theoretical framework from an assimilation perspective, Trans. Geosci. Rem. Sens., 49, 773, 10.1109/TGRS.2010.2060342 · DOI
  137. Zhan, 2012, Downscaling land surface temperatures with multi-spectral and multi-resolution images, Int. J. Appl. Earth Obs. Geoinf., 18, 23
  138. Zhan, 2013, Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats, Remote Sens. Environ., 131, 119, 10.1016/j.rse.2012.12.014 · DOI · Paper page
  139. Zhang, 2017, Urban building density estimation from high- resolution imagery using multiple features and support vector regression, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 10, 3265, 10.1109/JSTARS.2017.2669217 · DOI
  140. Zhang, 2019, Spatial downscaling of land surface temperature in combination with TVDI and elevation, Int. J. Rem. Sens., 40, 1875, 10.1080/01431161.2018.1489164 · DOI
  141. Zhang, 2021, Remote sensing image spatiotemporal fusion using a generative adversarial network, Trans. Geosci. Rem. Sens., 59, 4273, 10.1109/TGRS.2020.3010530 · DOI
  142. Zhou, 2021, Ensemble extreme learning machine approach to thermal infrared subpixel temperature estimation, Geosci. Rem. Sens. Lett. IEEE, 18, 920, 10.1109/LGRS.2020.2985500 · DOI
  143. Zhou, 2016, Quantification of the scale effect in downscaling remotely sensed land surface temperature, Rem. Sens., 8, 975, 10.3390/rs8120975 · DOI
  144. Zhou, 2019, Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives, Rem. Sens., 11, 48, 10.3390/rs11010048 · DOI
  145. Zhu, 2010, An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sens. Environ., 114, 2610, 10.1016/j.rse.2010.05.032 · DOI
  146. Zhu, 2016, A flexible spatiotemporal method for fusing satellite images with different resolutions, Remote Sens. Environ., 172, 165, 10.1016/j.rse.2015.11.016 · DOI
  147. Zhu, 2018, Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions, Rem. Sens., 10, 527, 10.3390/rs10040527 · DOI
  148. Zhu, 2018, Monitoring spatio-temporal variance of an extreme heat event using multiple-source remote sensing data, Chin. Geogr. Sci., 28, 744, 10.1007/s11769-018-0989-8 · DOI

Cited by

Sorted by citations

See all 55

Metadata

Authors
Pu, R., & Bonafoni, S.
Affiliations
  • University of South Florida (US)
  • University of Perugia (IT)
Corresponding author
Bonafoni, Stefania
ORCID
Venue
Remote Sensing Applications: Society and Environment
Publisher
Elsevier BV
Published
2023-01
Volume
29
Pages
100921
ISSN
2352-9385 (ISSN-L)
DOI
10.1016/j.rsase.2023.100921
OpenAlex
W4319997908
Cited by
55
References
0
Citations by year
2026: 6 · 2025: 19 · 2024: 24 · 2023: 6
FWCI
5.63
Type
journal article
Language
en
Open access
Licence
Locations
Remote Sensing Applications Society and Environment (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
land surface temperature, thermal infrared remote sensing, MODIS, spatiotemporal fusion, machine learning, polar-orbiting satellites, spatial resolution, temporal resolution, scale factor

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Thermal infrared remote sensing data downscaling investigations: An overview on current status and perspectives. https://app.cochat.ai/papers/10.1016/j.rsase.2023.100921
The paper
Pu, R., & Bonafoni, S. (2023). Thermal infrared remote sensing data downscaling investigations: An overview on current status and perspectives. Remote Sensing Applications: Society and Environment. https://doi.org/10.1016/j.rsase.2023.100921