Cited by 52Urban Heat Island MitigationAdvanced Image Fusion TechniquesRemote Sensing in Agriculture

The DOI matches its Crossref record.

Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data

Dong, Pan, Gao, Lun, Zhan, Wenfeng, Liu, Zihan, Li, Jiufeng, Lai, Jiameng, Li, Hua, Huang, Fan, Tamang, Sagar K., Zhao, Limin·2020·ISPRS Journal of Photogrammetry and Remote Sensing

Overview

Integrity signals

Registry

Crossref · 10.1016/j.isprsjprs.2020.08.018

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?

  • Current LST products are constrained by the trade-off between spatial and temporal resolutions imposed by sensor fabrication limitations, which severely limits their practical applications.

    “current LST products are often constrained by the trade-off between spatial and temporal resolutions.”
  • For both the kernel-driven and DCF models, the accuracy discrepancy between the linear and nonlinear variants is not significant.

    “For both the kernel-driven and DCF models, the accuracy discrepancy between the linear and nonlinear variants is not significant, which is consistent with previous studies (Dong et al., 2020; Hutengs and Vohland, 2016).”

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 96

  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. Agam, 2008, Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions, Geophys. Res. Lett., 35, 10.1029/2007GL032195 · DOI
  3. Agathangelidis, 2019, Improving the disaggregation of MODIS land surface temperatures in an urban environment: A statistical downscaling approach using high-resolution emissivity, Int. J. Remote Sens., 40, 5261, 10.1080/01431161.2019.1579386 · DOI
  4. Amazirh, 2019, Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data, ISPRS-J. Photogramm. Remote Sens., 150, 11, 10.1016/j.isprsjprs.2019.02.004 · DOI
  5. Bartkowiak, 2019, Downscaling land surface temperature from MODIS dataset with random forest approach over alpine vegetated areas, Remote Sens., 11, 1319, 10.3390/rs11111319 · DOI
  6. Bastiaanssen, 1998, A remote sensing surface energy balance algorithm for land (SEBAL). 1, Formulation. J. Hydrol., 212, 198, 10.1016/S0022-1694(98)00253-4 · DOI
  7. Bechtel, 2012, Downscaling land surface temperature in an urban area: A case study for Hamburg, Germany. Remote Sens., 4, 3184, 10.3390/rs4103184 · DOI
  8. Bisquert, 2016, Estimating high resolution evapotranspiration from disaggregated thermal images, Remote Sens. Environ., 187, 423, 10.1016/j.rse.2016.10.049 · DOI
  9. Bonafoni, 2016, Downscaling of Landsat and MODIS land surface temperature over the heterogeneous urban area of Milan, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 9, 2019, 10.1109/JSTARS.2016.2514367 · DOI
  10. Bonafoni, 2016, Downscaling of land surface temperature using airborne high-resolution data: A case study on Aprilia, Italy. IEEE Geosci. Remote Sens. Lett., 14, 107, 10.1109/LGRS.2016.2630798 · DOI
  11. Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  12. Chen, 2011, Scale effect of vegetation-index-based spatial sharpening for thermal imagery: A simulation study by ASTER data, IEEE Geosci. Remote Sens. Lett., 9, 549, 10.1109/LGRS.2011.2174453 · DOI
  13. Chen, 2014, Disaggregation of remotely sensed land surface temperature: A generalized paradigm, IEEE Trans. Geosci. Remote Sens., 52, 5952, 10.1109/TGRS.2013.2294031 · DOI
  14. Cho, 2018, Disaggregation of Landsat-8 thermal data using guided SWIR imagery on the scene of a wildfire, Remote Sens., 10, 105, 10.3390/rs10010105 · DOI
  15. Choe, 2019, Improving accuracy of land surface temperature prediction model based on deep-learning, Spat. Inf. Res., 28, 377, 10.1007/s41324-019-00299-5 · DOI
  16. Cracknell, 1998, Synergy in remote sensing-what's in a pixel?, Int. J. Remote Sens., 19, 2025, 10.1080/014311698214848 · DOI
  17. Dominguez, 2011, High-resolution urban thermal sharpener (HUTS), Remote Sens. Environ., 115, 1772, 10.1016/j.rse.2011.03.008 · DOI · Paper page
  18. Duan, 2016, Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: Case study in northern China, IEEE Trans. Geosci. Remote Sens., 54, 6458, 10.1109/TGRS.2016.2585198 · DOI
  19. 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
  20. Essa, 2012, Evaluation of the DisTrad thermal sharpening methodology for urban areas, Int. J. Appl. Earth Obs. Geoinf., 19, 163
  21. Essa, 2017, Improved DisTrad for downscaling thermal MODIS imagery over urban areas, Remote Sens., 9, 1243, 10.3390/rs9121243 · DOI
  22. Eswar, 2016, Disaggregation of LST over India: Comparative analysis of different vegetation indices, Int. J. Remote Sens., 37, 1035, 10.1080/01431161.2016.1145363 · DOI
  23. Fu, 2016, Consistent land surface temperature data generation from irregularly spaced Landsat imagery, Remote Sens. Environ., 184, 175, 10.1016/j.rse.2016.06.019 · DOI
  24. Gan, 2019, An optimized two source energy balance model based on complementary concept and canopy conductance, Remote Sens. Environ., 223, 243, 10.1016/j.rse.2019.01.020 · DOI
  25. Gao, 2012, A data mining approach for sharpening thermal satellite imagery over land, Remote Sens., 4, 3287, 10.3390/rs4113287 · DOI
  26. Gao, 2016, Localization or globalization? Determination of the optimal regression window for disaggregation of land surface temperature, IEEE Trans. Geosci. Remote Sens., 55, 477, 10.1109/TGRS.2016.2608987 · DOI
  27. Ghosh, 2014, Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes, ISPRS-J. Photogramm. Remote Sens., 96, 76, 10.1016/j.isprsjprs.2014.07.003 · DOI · Paper page
  28. Gong, 2013, Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607, 10.1080/01431161.2012.748992 · DOI
  29. Govil, 2019, Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city, Heliyon, 5, 10.1016/j.heliyon.2019.e01923 · DOI
  30. Granero-Belinchon, 2019, Multi-resolution study of thermal unmixing techniques over Madrid urban area: Case study of TRISHNA mission, Remote Sens., 11, 1251, 10.3390/rs11101251 · DOI
  31. Hawkins, 2003, Assessing model fit by cross-validation, J. Chem. Inf. Comput. Sci., 43, 579, 10.1021/ci025626i · DOI
  32. Hazaymeh, 2015, Fusion of MODIS and Landsat-8 surface temperature images: A new approach, PLoS One., 10, 10.1371/journal.pone.0117755 · DOI
  33. Huang, 2013, Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring, IEEE Geosci. Remote Sens. Lett., 10, 1011, 10.1109/LGRS.2012.2227930 · DOI
  34. Huryna, 2019, Evaluation of TsHARP utility for thermal sharpening of Sentinel-3 satellite images using Sentinel-2 visual imagery, Remote Sens., 11, 2304, 10.3390/rs11192304 · DOI
  35. Hutengs, 2016, Downscaling land surface temperatures at regional scales with random forest regression, Remote Sens. Environ., 178, 127, 10.1016/j.rse.2016.03.006 · DOI · Paper page
  36. Jeganathan, 2011, Evaluating a thermal image sharpening model over a mixed agricultural landscape in India, Int. J. Appl. Earth Obs. Geoinf., 13, 178
  37. Jiang, 2015, Downscaling GOES land surface temperature for assessing heat wave health risks, IEEE Geosci. Remote Sens. Lett., 12, 1605, 10.1109/LGRS.2015.2414897 · DOI
  38. Kalma, 2008, Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surv. Geophys., 29, 421, 10.1007/s10712-008-9037-z · DOI
  39. Keramitsoglou, 2013, Downscaling geostationary land surface temperature imagery for urban analysis, IEEE Geosci. Remote Sens. Lett., 10, 1253, 10.1109/LGRS.2013.2257668 · DOI
  40. Kolios, 2013, Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks, Int. J. Remote Sens., 34, 7706, 10.1080/01431161.2013.825384 · DOI
  41. Kottek, 2006, World map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259, 10.1127/0941-2948/2006/0130 · DOI · Paper page
  42. Kurkova, 1992, Kolmogorov's theorem and multilayer neural networks, Neural Netw., 5, 501, 10.1016/0893-6080(92)90012-8 · DOI
  43. 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
  44. Li, 2014, Land surface temperature retrieval from Tiangong-1 data and its applications in urban heat island effect, J. Remote Sens, 18, 133
  45. Li, 2019, Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 12, 2299, 10.1109/JSTARS.2019.2896923 · DOI · Paper page
  46. Li, 2013, Satellite-derived land surface temperature: Current status and perspectives, Remote Sens. Environ., 131, 14, 10.1016/j.rse.2012.12.008 · DOI
  47. Lillo-Saavedra, 2018, Ts2urf: A new method for sharpening thermal infrared satellite imagery, Remote Sens., 10, 249, 10.3390/rs10020249 · DOI
  48. Liu, 1989, On the limited memory BFGS method for large scale optimization, Math. Program., 45, 503, 10.1007/BF01589116 · DOI
  49. Liu, 2018, A thermal disaggregation model based on trapezoid interpolation, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 11, 808, 10.1109/JSTARS.2018.2790002 · DOI
  50. Liu, 2019, Spatially disaggregating satellite land surface temperature with a nonlinear model across agricultural areas, J. Geophys. Res.-Biogeosci., 124, 3232, 10.1029/2019JG005227 · DOI
  51. Liu, 2020, The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region, Int. J. Remote Sens., 41, 1907, 10.1080/01431161.2019.1677969 · DOI
  52. Luo, R., Zhou, J., Yang, J.J., Ai, L.J., Feng, Y.L., 2019. Downscaling of Tiangong-2 land surface temperature. In: Proceedings of the Tiangong-2 Remote Sensing Application Conference, vol. 541. Springer, pp. 170-179. · DOI
  53. Maeda, 2014, Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information, Int. J. Remote Sens., 35, 3094, 10.1080/01431161.2014.903442 · DOI
  54. Merlin, 2011, Multidimensional disaggregation of land surface temperature using high-resolution red, near-infrared, shortwave-infrared, and microwave-L bands, IEEE Trans. Geosci. Remote Sens., 50, 1864, 10.1109/TGRS.2011.2169802 · DOI
  55. Mukherjee, 2015, Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India, Int. J. Remote Sens., 36, 2503, 10.1080/01431161.2015.1041175 · DOI
  56. Nguyen, 2005, Stopping criteria for ensemble of evolutionary artificial neural networks, Appl. Soft. Comput., 6, 100, 10.1016/j.asoc.2004.12.005 · DOI
  57. 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. Photogramm. Remote Sens., 128, 170, 10.1016/j.isprsjprs.2017.03.014 · DOI
  58. Pan, 2018, Applicability of downscaling land surface temperature by using normalized difference sand index, Sci. Rep., 8, 1, 10.1038/s41598-018-27905-0 · DOI
  59. Pedregosa, 2011, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825
  60. Peng, 2019, A geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions, IEEE Trans. Geosci. Remote Sens., 57, 5012, 10.1109/TGRS.2019.2895351 · DOI
  61. Pereira, 2018, Downscaling of ASTER thermal images based on geographically weighted regression kriging, Remote Sens., 10, 633, 10.3390/rs10040633 · DOI
  62. Qi, 2016, Sharpening method of satellite thermal image based on the geographical statistical model, J. Appl. Remote Sens., 10, 10.1117/1.JRS.10.025013 · DOI
  63. 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
  64. Quattrochi, 1999, Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications, Landsc. Ecol., 14, 577, 10.1023/A:1008168910634 · DOI
  65. Shen, 2016, 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, Remote Sens. Environ., 172, 109, 10.1016/j.rse.2015.11.005 · DOI
  66. Sismanidis, 2016, Assessing the capability of a downscaled urban land surface temperature time series to reproduce the spatiotemporal features of the original data, Remote Sens., 8, 274, 10.3390/rs8040274 · DOI
  67. Sismanidis, 2017, Improving the downscaling of diurnal land surface temperatures using the annual cycle parameters as disaggregation kernels, Remote Sens., 9, 23, 10.3390/rs9010023 · DOI
  68. Smola, 2004, A tutorial on support vector regression, Stat. Comput., 14, 199, 10.1023/B:STCO.0000035301.49549.88 · DOI
  69. Valor, 1996, Mapping land surface emissivity from NDVI: Application to European, African, and South American areas, Remote Sens. Environ., 57, 167, 10.1016/0034-4257(96)00039-9 · DOI
  70. Voogt, 2003, Thermal remote sensing of urban climates, Remote Sens. Environ., 86, 370, 10.1016/S0034-4257(03)00079-8 · DOI
  71. Wang, 2020, Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis, ISPRS-J. Photogramm. Remote Sens., 161, 76, 10.1016/j.isprsjprs.2020.01.014 · DOI
  72. Wang, 2020, A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat, Int. J. Remote Sens., 41, 2986, 10.1080/01431161.2019.1697009 · DOI
  73. Weng, 2009, Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends, ISPRS-J. Photogramm. Remote Sens., 64, 335, 10.1016/j.isprsjprs.2009.03.007 · DOI
  74. Weng, 2014, Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data, Remote Sens. Environ., 145, 55, 10.1016/j.rse.2014.02.003 · DOI
  75. Weng, 2014, Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery, ISPRS-J. Photogramm. Remote Sens., 97, 78, 10.1016/j.isprsjprs.2014.08.009 · DOI
  76. 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
  77. Wu, 2019, Downscaling of urban land surface temperature based on multi-factor geographically weighted regression, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 12, 2897, 10.1109/JSTARS.2019.2919936 · DOI
  78. Wu, 2013, Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion, Int. J. Digit. Earth, 6, 113, 10.1080/17538947.2013.783131 · DOI
  79. Wu, 2015, Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature, Remote Sens. Environ., 156, 169, 10.1016/j.rse.2014.09.013 · DOI
  80. Wulder, 2019, Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127, 10.1016/j.rse.2019.02.015 · DOI
  81. Xia, 2018, “Regression-then-fusion” or “fusion-then-regression”? A theoretical analysis for generating high spatiotemporal resolution land surface temperatures, Remote Sens., 10, 1382, 10.3390/rs10091382 · DOI
  82. Xia, 2019, Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures, Remote Sens. Environ., 224, 259, 10.1016/j.rse.2019.02.006 · DOI
  83. Xia, 2019, Object-based window strategy in thermal sharpening, Remote Sens., 11, 634, 10.3390/rs11060634 · DOI
  84. Yang, 2019, Downscaling land surface temperature using multiscale geographically weighted regression over heterogeneous landscapes in Wuhan, China, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 12, 5213, 10.1109/JSTARS.2019.2955551 · DOI
  85. Yang, 2017, Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression, Remote Sens., 9, 789, 10.3390/rs9080789 · DOI
  86. Yang, 2017, Downscaling land surface temperature in complex regions by using multiple scale factors with adaptive thresholds, Sensors, 17, 744, 10.3390/s17040744 · DOI
  87. Yu, 2014, Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method, Remote Sens., 6, 9829, 10.3390/rs6109829 · DOI
  88. Zakšek, 2012, Downscaling land surface temperature for urban heat island diurnal cycle analysis, Remote Sens. Environ., 117, 114, 10.1016/j.rse.2011.05.027 · DOI
  89. 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. Digit. Earth, 13, 899, 10.1080/17538947.2019.1593527 · DOI
  90. Zhan, 2010, Sharpening thermal imageries: A generalized theoretical framework from an assimilation perspective, IEEE Trans. Geosci. Remote Sens., 49, 773, 10.1109/TGRS.2010.2060342 · DOI
  91. 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
  92. Zhan, 2016, Disaggregation of remotely sensed land surface temperature: A new dynamic methodology, J. Geophys. Res.-Atmos., 121, 10538, 10.1002/2016JD024891 · DOI
  93. Zhang, X.Y., Zhao, H., Yang, J.J., 2019. Spatial downscaling of land surface temperature in combination with TVDI and elevation. Int. J. Remote Sens. 40 (5-6), 1875-1886. · DOI
  94. Zhang, 2018, The impact of thermal image spatial enhancement on the estimation of the urban green cooling effect, Infrared Phys. Technol., 88, 206, 10.1016/j.infrared.2017.11.027 · DOI
  95. Zhou, 2019, Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives, Remote Sens., 11, 48, 10.3390/rs11010048 · DOI
  96. Zhou, 2016, Quantification of the scale effect in downscaling remotely sensed land surface temperature, Remote Sens., 8, 975, 10.3390/rs8120975 · DOI

Cited by

Sorted by citations

Metadata

Authors
Dong, P., Gao, L., Zhan, W., Liu, Z., Li, J., Lai, J., Li, H., Huang, F., Tamang, S. K., & Zhao, L.
Affiliations
  • Nanjing University (CN)
  • University of Minnesota (US)
  • Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
Zhan, Wenfeng
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2020-11
Volume
169
Pages
44-56
ISSN
0924-2716 (ISSN-L)
OpenAlex
W3086090559
Cited by
52
References
0
Citations by year
2026: 10 · 2025: 16 · 2024: 7 · 2023: 6 · 2022: 9 · 2021: 4
FWCI
1.66
Type
journal article
Language
en
Open access
closed
Locations
ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
Funders
  • National Key Research and Development Program of China (2016YFA0600201, 2016YFC1400903)
  • Jiangsu Provincial Natural Science Foundation (BK20180009)
  • National Natural Science Foundation of China (41671420)
  • Fundamental Research Funds for the Central Universities (090414380024)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
statistical downscaling, land surface temperature, Landsat 8, scale factor, regression models, random forest, support vector regression, artificial neural networks, partial least squares regression, shortwave infrared bands, huts

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