Abstract
Spatially and temporally resolved observations of near-surface air temperatures (Ta, 1.5–2 m above ground) are essential for understanding hydrothermal circulation at the land–atmosphere interface. However, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Several studies have attempted to retrieve spatially continuous Ta from remotely sensed and continuously monitored Land Surface Temperature (LST). However, the topographical control of the relationship between LST and Ta in regions with complex topographies and highly variable weather station densities is poorly understood. The aim of this study is to improve the accuracy of Ta estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST via parameterization of the physiographic variables according to the terrain relief. The performances of both Terra and Aqua MODIS LST in estimating Ta have been explored in China. The results indicated that the best agreement was found between Terra nighttime LST (LSTmodn) and the observed Ta in China. In flat terrain areas, the LSTmodn product is significantly linearly correlated with Ta (R2 > 0.80), while, in mountainous areas, the LSTmodn-Ta relationship differed significantly from simple linear correlation. By taking the physiographic features into account, including the seasonal vegetation cover (NDVI), the altitudinal gradient (RDLS), and the ambient absolute humidity (AH), the accuracy of the estimation was substantially improved. The study results indicated that the relevant environmental factors must be considered when interpreting the spatiotemporal variation of the surface energy flux over complex topography.
Highlights
The near-surface air temperature (Ta), which is traditionally recorded at 1.5–2 m above ground, is an indispensable parameter that drives the energy and water exchanges among the hydrosphere, atmosphere and biosphere
The fact that the Moderate Resolution Imaging Spectroradiometer (MODIS) nighttime land surface temperature (LST) products result in better predicted Ta accuracies of Tmax), and the root mean square error (RMSE) validation errors are 6.6 °C and 2.7 °C for Tmean and Tmin, respectively is verified by the work of [14,53]
We assessed the potential of MODIS LST for the estimation of monthly Ta through linear regression analysis of LST and observed the Ta from 688 meteorological stations in China
Summary
The near-surface air temperature (Ta), which is traditionally recorded at 1.5–2 m above ground, is an indispensable parameter that drives the energy and water exchanges among the hydrosphere, atmosphere and biosphere. The energy exchange in terms of transpiration and/or evaporation from the canopy and soil via sensible and latent heat fluxes is mainly due to the vertical temperature and humidity gradients between the surface and the atmosphere [1,2,3]. Many studies have used spatial interpolations of Ta [5,6] and atmospheric reanalysis datasets [7,8] to monitor climate change and to derive ecological models. The reanalysis climate variable is too spatially coarse to adequately represent the regional climate patterns
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