Downscaling of Satellite Land Surface Temperature Data Over Urban Environments

Vaculik, Anna F.; Bah, A. R.; Norouzi, Hamid; Beale, Christopher; Valentine, M.; Ginchereau, J.; Blake, Reginald

doi:10.1109/igarss.2019.8898193

Cited by 1 publication

(1 citation statement)

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“…In addition, Anthony et al [29] developed a high-resolution urban thermal fusion (HUTS) technique to downscale Landsat TIRS to 30 m based on NDVI and surface albedo. Lacerda et al [30] used the TsHARP model to downscale the MODIS LST to a high spatial resolution of 10 m. Vaculik et al [31] downscaled the GOES-R LST with the resolution of 2000 m to 30 m by establishing a linear relationship between NDVI and LST. J.M.…”

Section: Research Reviewmentioning

confidence: 99%

Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters

Zhang

Zhu

et al. 2022

Remote Sensing

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Land surface temperature (LST) is one of the most important parameters in urban thermal environmental studies. Compared to natural surfaces, the surface of urban areas is more complex, and the spatial variability of LST is higher. Therefore, it is important to obtain a high-spatial-resolution LST for urban thermal environmental research. At present, downscaling studies are mostly performed from a low spatial resolution directly to another high resolution, which often results in lower accuracy with a larger scale span. First, a step-by-step random forest downscaling LST model (SSRFD) is proposed in this study. In our work, the 900-m resolution Sentinel-3 LST was sequentially downscaled to 450 m, 150 m and 30 m by SSRFD. Then, urban spatial morphological parameters were introduced into SSRFD, abbreviated as SSRFD-M, to compensate for the deficiency of remote-sensing indices as driving factors in urban downscaling LST. The results showed that the RMSE value of the SSRFD results was reduced from 2.6 °C to 1.66 °C compared to the direct random forest downscaling model (DRFD); the RMSE value of the SSRFD-M results in built-up areas, such as Gulou and Qinhuai District, was reduced by approximately 0.5 °C. We also found that the underestimation of LST caused by considering only remote-sensing indices in places such as flowerbeds and streets was improved in the SSRFD-M results.

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Section: Research Reviewmentioning

confidence: 99%