[Retrieval of spectral characteristics of hyperspectral sensor and retrieval of reflectance spectra].

On-orbit spectral calibration of hyperspectral imaging data is a key step for quantitatively analyzing them. Like the atmospheric correction, accurate spectral calibration is very necessary for improved studies of land or ocean surface properties. Based on the previous literatures, a new method which coupled an optimization algorithm was developed to simultaneously retrieve the central wavelength and the full width at half maximum (FWHM) of the hyperspectral sensor without needing the in situ reflectance spectra. Firstly, the Hyperion data set simulated using MODTRAN4 with the Hyperion spectral specification was used to test the new method, and the results indicated that the maximum error was less than 0.1 and 0.7 nm for central wavelength and FWHM respectively when the spectral shift is 5 nm. Then the algorithm was applied to the Hyperion data acquired on May 20, 2008 over Heihe River Basin and it was iteratively performed for each detector of the two spectrometers of Hyperion. The results showed that the VNIR of Hyperion had a pronounced smile effect, and the shift in on-orbit calibration with respect to the laboratory was from -2 to +2 nm, while the SWIR has essentially no smile effect, the wavelength correction was relatively flat over all sample with an approximately constant value of 3 nm. The FWHM in VNIR could range from -0.2 to 0.5 nm as a function of sample number of the spectrometer, and in SWIR it ranged from -2 to -3 nm. So for both the VNIR and SWIR, the original spectral calibration should be updated. These results showed good agreement with previous research findings, and which also proved the feasibility of the new method. Finally, with the updated spectral calibration characteristics, the sample reflectances of desert and vegetation target in our study site were reconstructed by applying a further atmospheric correction, and as expected, the strong spikes around the typical atmospheric absorption were almost disappeared.

[Crop geometry identification based on inversion of semiempirical BRDF models].

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.