Near-surface air temperature estimation from ASTER data based on neural network algorithm
An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM-NN can be used to estimate near surface air temperature from ASTER data.
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Document Type: Research Article
Affiliations: Graduate School of Agriculture, Hokkaido University, N-9, W-9, Kita-ku, Sapporo 060-8589, Japan
Publication date: October 1, 2008