2020
DOI: 10.1155/2020/3427321
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Evaluation of the Spatial Pattern of the Resolution-Enhanced Thermal Data for Urban Area

Abstract: With the development of urbanization, land surface temperature (LST), as a vital variable for the urban environment, is highly demanded by urban-related studies, especially the LST with both fine temporal and spatial resolutions. Thermal sharpening methods have been developed just under this demand. Until now, there are some thermal sharpening methods proposed especially for urban surface. However, the evaluation of their accuracy still stopped at the level that only considers the statistical aspect, but no sp… Show more

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Cited by 3 publications
(3 citation statements)
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References 42 publications
(78 reference statements)
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“…For example, we identify trips between neighborhoods, migration flows between municipalities or freight shipments between states. Therefore, through these metrics, we can obtain several important human mobility patterns [4,5].…”
Section: Human Mobility Metricsmentioning
confidence: 99%
“…For example, we identify trips between neighborhoods, migration flows between municipalities or freight shipments between states. Therefore, through these metrics, we can obtain several important human mobility patterns [4,5].…”
Section: Human Mobility Metricsmentioning
confidence: 99%
“…For each community researchers retrieve all the cells associated to its nodes and join them in a cluster, i.e. a geometric representation of the area covered by the community [9]. Areas corresponding to different communities are rendered with different colours.…”
Section: Spatial Resolution Of Categorisation and Aggregationmentioning
confidence: 99%
“…The successful implementation of downscaling methods over complex land cover surfaces is still limited by the selection of an appropriate environmental predictor, which is considered an important task (Govil et al, 2019;Bechtel et al, 2012). Regardless of the exact statistical downscaling method used, the accuracy of thermal downscaling relies on the robustness of the LST -sharpening predictors relationship (Feng et al, 2020). Spectral indices are commonly used as a robust LST sharpening predictor in different land-cover types, which possess a robust representation of the land cover and a high correlation with LST for successful downscaling LST procedures.…”
Section: Introductionmentioning
confidence: 99%