Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature
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- @Article{wen:2023:RS,
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author = "Zitong Wen and Lu Zhuo and Qin Wang and Jiao Wang and
Ying (Emily) Liu and Sichan Du and Ahmed Abdelhalim and
Dawei Han",
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title = "Data Fusion for Estimating High-Resolution Urban
Heatwave Air Temperature",
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journal = "Remote Sensing",
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year = "2023",
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volume = "15",
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number = "16",
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pages = "Article No. 3921",
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month = "8 " # aug,
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note = "Special Issue Geo-Information and Integration for
Smart and Friendly Cities",
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keywords = "genetic algorithms, genetic programming, air
temperature, data fusion, downscaling, ERA5-Land,
MODIS",
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ISSN = "2072-4292",
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URL = "https://www.mdpi.com/2072-4292/15/16/3921",
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DOI = "doi:10.3390/rs15163921",
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size = "22 pages",
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abstract = "High-resolution air temperature data is indispensable
for analysing heatwave-related non-accidental
mortality. However, the limited number of weather
stations in urban areas makes obtaining such data
challenging. Multi-source data fusion has been proposed
as a countermeasure to tackle such challenges.
Satellite products often offered high spatial
resolution but suffered from being temporally
discontinuous due to weather conditions. The
characteristics of the data from reanalysis models were
the opposite. However, few studies have explored the
fusion of these datasets. This study is the first
attempt to integrate satellite and reanalysis datasets
by developing a two-step downscaling model to generate
hourly air temperature data during heatwaves in London
at 1 km resolution. Specifically, MODIS land surface
temperature (LST) and other satellite-based local
variables, including normalised difference vegetation
index (NDVI), normalised difference water index (NDWI),
modified normalised difference water index (MNDWI),
elevation, surface emissivity, and ERA5-Land hourly air
temperature were used. The model employed genetic
programming (GP) algorithm to fuse multi-source data
and generate statistical models and evaluated using
ground measurements from six weather stations. The
results showed that our model achieved promising
performance with the RMSE of 0.335 degree C, R-squared
of 0.949, MAE of 1.115 degree Celsius, and NSE of
0.924. Elevation was indicated to be the most effective
explanatory variable. The developed model provided
continuous, hourly 1 km estimations and accurately
described the temporal and spatial patterns of air
temperature in London. Furthermore, it effectively
captured the temporal variation of air temperature in
urban areas during heatwaves, providing valuable
insights for assessing the impact on human health.",
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notes = "also known as \cite{rs15163921}",
- }
Genetic Programming entries for
Zitong Wen
Lu Zhuo
Qin Wang
Jiao Wang
Ying (Emily) Liu
Sichan Du
Ahmed Abdelhalim
Dawei Han
Citations