Estimation of All-Sky Gridded Diurnal Near-Surface Air Temperatures at Regional Scale From FY-4B Measurements
The near-surface air temperature (<inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula>) is a principal variable describing energy exchange and water circulation between the land surface and the atmospheric environment. The estimation...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10767357/ |
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| Summary: | The near-surface air temperature (<inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula>) is a principal variable describing energy exchange and water circulation between the land surface and the atmospheric environment. The estimation of <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> by satellite land surface temperature (LST) is challenging due to the variable magnitude of the difference between <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> and LST in both space and time, as well as the restriction of estimated <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> to clear-sky conditions because of the penetration of infrared wavelengths. Moreover, the estimation suffers from low temporal resolution and primarily focuses on daily minimum, maximum, and two instantaneous <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> per day. This study proposes a method for estimating all-sky gridded diurnal <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> at regional scale from FY-4B/AGRI measurements. The multiscale geographically weighted regression model was investigated to establish the dynamic relationships between ground station observed <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> and satellite LST under clear-sky conditions by employing different spatial values for each explanatory variable in localized regressions. A moving window loop based multiple linear regression was employed to establish the relationship between satellite-derived clear-sky <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> and other variables to extrapolate <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> in cloudy-sky pixels. The results showed that the proposed method captures the trend of <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> variations well in hourly profiles with R values greater than 0.95. RMSE was 1.75 °C, 1.38 °C, 1.95 °C, and 2.19 °C in April, July, October, and January, respectively. The demonstration of heatwave monitoring showed that satellite-estimated <inline-formula><tex-math notation="LaTeX">${{T}_{air}}$</tex-math></inline-formula> provide an excellent representation of the spatial and temporal evolution of the heatwave. |
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| ISSN: | 1939-1404 2151-1535 |