Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features
AbstractSpatially 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. View Full-Text
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Lin, X.; Zhang, W.; Huang, Y.; Sun, W.; Han, P.; Yu, L.; Sun, F. Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features. Remote Sens. 2016, 8, 629.
Lin X, Zhang W, Huang Y, Sun W, Han P, Yu L, Sun F. Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features. Remote Sensing. 2016; 8(8):629.Chicago/Turabian Style
Lin, Xiaohui; Zhang, Wen; Huang, Yao; Sun, Wenjuan; Han, Pengfei; Yu, Lingfei; Sun, Feifei. 2016. "Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features." Remote Sens. 8, no. 8: 629.
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