Aerosol optical thickness is a very important parameters in the atmospheric correction of the hyperspectral data. In this study, an improved dense dark vegetation (DDV) based algorithm is introduced to estimate the AOT@550nm from hyperspectral remote sensing data. A correction relationship between TOA and land surface reflectance at short wavelength near 2.13μm was introduced in order to reduce the assumption of the traditional DDV that the TOA reflectance is equal to the land surface reflectance at short wavelength near 2.13μm. Simulated hyperspectral data of Hyperion sensor were applied to the improved DDV algorithm. The retrieved AOT @550nm show a well correlation with the actual values and the correlation coefficients is larger than 0.99.
This work addressed the simultaneous retrieval of Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from time-series thermal infrared data. On basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST and LSE from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by algorithm assumption, instrument noise, initial emissivity, etc. The modeling errors of the proposed method from the simulated data are less than 0.04 K for temperature and less than 6.76E-4 for emissivity. The proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the RMSE of LST is observed to be only 0.13K, and that of LSE is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods.
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