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Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes
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The kernel-driven model's scale-invariant assumption may not always hold.
“the scale-invariant assumption may not always hold (Chen et al., 2014; Ghosh and Joshi, 2014; Jeganathan et al., 2011; Zhou et al., 2016)”
From PGDM: Physically guided diffusion model for land surface temperature downscaling (2025) · cites this paper as Ghosh and Joshi, 2014
Such misestimations or noise of linear models have also been observed by previous studies, indicating the instability of this model.
“Such misestimations or noise of linear models have also been observed by previous studies (Ghosh and Joshi, 2014; Hu et al., 2024), indicating the instability of this model.”
From PGDM: Physically guided diffusion model for land surface temperature downscaling (2025) · cites this paper as Ghosh and Joshi, 2014
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Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788
Association between climate variability and malaria epidemics in the East African highlands
Zhou, G., Minakawa, N., Githeko, A. K., & Yan, G. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308714100 · Cited by 498
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Research works printed but not linked: 59
- Agam, 2007, A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sens. Environ., 107, 547, 10.1016/j.rse.2006.10.006 · DOI
- Agam, 2007, Utility of thermal sharpening over Texas high plains irrigated agricultural fields, J. Geophys. Res., 112, D19110, 10.1029/2007JD008407 · DOI
- Agam, 2008, Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions, Geophys. Res. Lett., 35, L02402, 10.1029/2007GL032195 · DOI
- Anderson, 2008, A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales, Remote Sens. Environ., 112, 4227, 10.1016/j.rse.2008.07.009 · DOI
- 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
- Borel, 2008, Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data, Int. J. Remote Sens., 29, 5029, 10.1080/01431160802036540 · DOI
- Deng, 2013, Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach, Remote Sens. Environ., 131, 262, 10.1016/j.rse.2012.12.020 · DOI
- Dennison, 2011, Comparison of fire temperature and fractional area modeled from SWIR, MIR, and TIR multispectral and SWIR hyperspectral airborne data, Remote Sens. Environ., 115, 876, 10.1016/j.rse.2010.11.015 · DOI
- Dennison, 2006, Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., 100, 212, 10.1016/j.rse.2005.10.007 · DOI
- Dousset, 2003, Satellite multi-sensor data analysis of urban surface temperatures and landcover, ISPRS J. Photogr. Remote Sens., 58, 43, 10.1016/S0924-2716(03)00016-9 · DOI
- Durbha, 2007, Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer, Remote Sens. Environ., 107, 348, 10.1016/j.rse.2006.09.031 · DOI
- Elith, 2008, A working guide to boosted regression trees, J. Anim. Ecol., 77, 802, 10.1111/j.1365-2656.2008.01390.x · DOI · Paper page
- Essa, 2012, Evaluation of the DisTrad thermal sharpening methodology for urban areas, Int. J. Appl. Earth Obs. Geoinf., 19, 163, 10.1016/j.jag.2012.05.010 · DOI
- Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189, 10.1214/aos/1013203451 · DOI · Paper page
- Gao, 2012, A data mining approach for sharpening thermal satellite imagery over land, Remote Sens., 4, 3287, 10.3390/rs4113287 · DOI
- Gleason, 2012, Forest biomass estimation from airborne LiDAR data using machine learning approaches, Remote Sens. Environ., 125, 80, 10.1016/j.rse.2012.07.006 · DOI
- Gustafson, W.T., Handcock, R., Gillespie, A.R., Tonooka, H., 2003. An image sharpening method to recover stream temperatures from ASTER images. In: Ehlers, M., (Ed.), Proceedings: Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II. SPIE Conference, vol. 4886. · DOI
- Hansen, 2003, Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression, Remote Sens. Environ., 86, 542, 10.1016/S0034-4257(03)00131-7 · DOI
- Inamdar, 2009, Disaggregation of GOES land surface temperatures using surface emissivity, Geophys. Res. Lett., 36, L02408, 10.1029/2008GL036544 · DOI
- Jeganathan, 2011, Evaluating a thermal image sharpening model over a mixed agricultural landscape in India, Int. J. Appl. Earth Obs. Geoinf., 13, 178, 10.1016/j.jag.2010.11.001 · DOI
- Jing, 2010, A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data, Int. J. Remote Sens., 31, 6459, 10.1080/01431160903439841 · DOI
- 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
- Karatzoglou, 2004, Kernlab – An S4 package for kernel methods in R, J. Stat. Softw., 11, 1, 10.18637/jss.v011.i09 · DOI
- Karnieli, 2006, Comments on the use of the vegetation health index over Mongolia, Int. J. Remote Sens., 27, 2017, 10.1080/01431160500121727 · DOI
- Knudby, 2010, Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine learning techniques, Remote Sens. Environ., 114, 1230, 10.1016/j.rse.2010.01.007 · DOI · Paper page
- Kuhn, 2008, Building predictive models in R using caret package, J. Stat. Softw., 28, 1, 10.18637/jss.v028.i05 · DOI · Paper page
- 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
- Latifi, 2012, Forest structure modeling with combined airborne hyperspectral and LiDAR data, Remote Sens. Environ., 121, 10, 10.1016/j.rse.2012.01.015 · DOI
- Li, 2011, Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China, Remote Sens. Environ., 115, 3249, 10.1016/j.rse.2011.07.008 · DOI
- Li, 2013, Satellite-derived land surface temperature: current status and perspectives, Remote Sens. Environ., 131, 37
- Mann, 2008, Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, PNAS, 105, 13252, 10.1073/pnas.0805721105 · DOI
- Mansor, 1994, Monitoring of underground coal fires using thermal infrared data, Int. J. Remote Sens., 158, 1675, 10.1080/01431169408954199 · DOI
- Merlin, 2010, Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images, Remote Sens. Environ., 114, 2500, 10.1016/j.rse.2010.05.025 · DOI
- Mevik, 2007, The PLS package: principal component and partial least squares regression in R, J. Stat. Softw., 18, 10.18637/jss.v018.i02 · DOI
- Nichol, 2009, An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis, Photogr. Eng. Remote Sens., 75, 547, 10.14358/PERS.75.5.547 · DOI
- Okujeni, 2013, Support vector regression and synthetically mixed training data for quantifying urban land cover, Remote Sens. Environ., 137, 184, 10.1016/j.rse.2013.06.007 · DOI
- Pardo-Igúzquiza, 2006, Downscaling cokriging for image sharpening, Remote Sens. Environ., 102, 86, 10.1016/j.rse.2006.02.014 · DOI
- Pieri, 2005, ASTER observations of thermal anomalies preceding the April 2003 eruption of Chikurachki volcano, Kurile Islands Russia, Remote Sens. Environ., 99, 84, 10.1016/j.rse.2005.06.012 · DOI
- Pouteau, 2011, Downscaling MODIS-derived maps using GIS and boosted regression trees: the case of frost occurrence over the arid Andean highlands of Bolivia, Remote Sens. Environ., 115, 117, 10.1016/j.rse.2010.08.011 · DOI
- Rodríguez-Galiano, 2012, Downscaling Landsat 7 ETM+thermal imagery using land surface temperature and NDVI images, Int. J. Appl. Earth Obs. Geoinf., 18, 515, 10.1016/j.jag.2011.10.002 · DOI
- Roy, 2010, Web-enabled landsat data (WELD): Landsat ETM+ composited mosaics of the conterminous United States, Remote Sens. Environ., 114, 35, 10.1016/j.rse.2009.08.011 · DOI
- Smola, 2004, A tutorial on support vector regression, Statist. Comput., 4, 199, 10.1023/B:STCO.0000035301.49549.88 · DOI
- Sobrino, 2004, Land surface temperature retrieval from LANDSAT TM 5, Remote Sens. Environ., 90, 434, 10.1016/j.rse.2004.02.003 · DOI
- Stathopoulou, 2009, Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation, Remote Sens. Environ., 112, 2592, 10.1016/j.rse.2009.07.017 · DOI
- Treitz, 2000, Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems, Prog. Phys. Geogr., 23, 359, 10.1177/030913339902300303 · DOI
- Ullah, 2012, Identifying plant species using mid-wave infrared (2.5–6μm) and thermal infrared (8–14μm) emissivity spectra, Remote Sens. Environ., 118, 95, 10.1016/j.rse.2011.11.008 · DOI
- Van, 1993, On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces, Int. J. Remote Sens., 14, 1119, 10.1080/01431169308904400 · DOI
- Verrelst, 2012, Machine learning regression algorithms for biophysical parameter retrieval: opportunities for sentinel-2 and -3, Remote Sens. Environ., 118, 127, 10.1016/j.rse.2011.11.002 · DOI
- Voogt, 2003, Thermal remote sensing of urban climates, Remote Sens. Environ., 86, 370, 10.1016/S0034-4257(03)00079-8 · DOI
- Weng, 2004, Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies, Remote Sens. Environ., 89, 467, 10.1016/j.rse.2003.11.005 · DOI
- Wold, 1984, The collinearity problem in linear regression: the partial least squares (PLS) approach to generalized inverses, SIAM J. Sci. Stat. Comput., 5, 735, 10.1137/0905052 · DOI · Paper page
- Wold, 2001, PLS-regression: a basic tool of chemometrics, Chemometrics Intell. Lab. Syst., 58, 109, 10.1016/S0169-7439(01)00155-1 · DOI · Paper page
- Xie, 2008, Remote sensing imagery in vegetation mapping: a review, J. Plant Ecol., 1, 9, 10.1093/jpe/rtm005 · DOI
- Yuan, 2007, Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sens. Environ., 106, 375, 10.1016/j.rse.2006.09.003 · DOI
- 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
- Zhan, 2011, Sharpening thermal imageries: a generalized theoretical framework from an assimilation perspective, IEEE Trans. Geosci. Remote Sens., 49, 773, 10.1109/TGRS.2010.2060342 · DOI
- Zhan, 2012, Downscaling land surface temperatures with multi-spectral and multi-resolution images, Int. J. Appl. Earth Obs. Geoinf., 18, 23, 10.1016/j.jag.2012.01.003 · DOI
- 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
- Zhu, 2013, Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai, China, Int. J. Remote Sens., 34, 1707, 10.1080/01431161.2012.725957 · DOI
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PGDM: Physically guided diffusion model for land surface temperature downscaling
Zhang, H., Tang, B.-H., Hu, T., Jiang, Y., & Li, Z.-L. (2025) · arXiv · doi:10.48550/arxiv.2511.05964 · Cited by 3
Metadata
- Authors
- Ghosh, A., & Joshi, P. K.
- Affiliations
- TERI University (IN)
- Corresponding author
- Ghosh, Aniruddha
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 96
- Pages
- 76-93
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2023540599
- Cited by
- 69
- References
- 2
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 9 · 2023: 5 · 2022: 9 · 2021: 4 · 2020: 8 · 2019: 7 · 2017: 5 · 2016: 5 · 2015: 6
- FWCI
- 7.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- U.S. Geological Survey
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Keywords
- hyperspectral imagery, EO-1 Hyperion, Landsat thermal bands, regression algorithms, gradient boosting, support vector machine, partial least squares regression, spatial resolution dependence, land use/land cover, feature importance, soil moisture, urban heat island, wildfire monitoring, evapotranspiration estimation
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