This invention discloses a method for analyzing the uncertainty of full-spectrum hyperspectral
Earth remote sensing imaging measurements, comprising the following steps: (1) simulating and calculating the
entrance pupil radiance and restored
radiance spectral imaging signals of the
atmosphere using surface reflectance, surface
emissivity, surface temperature, topographic parameters, atmospheric parameters, and imaging
system parameters; (2) evaluating the uncertainty of surface reflectance, surface
emissivity, and surface temperature; (3) evaluating the uncertainty of topographic parameters per pixel and the
correlation coefficient between topographic parameters; (4) evaluating the spectral uncertainty of atmospheric parameters per pixel and the
correlation coefficient between atmospheric parameters; (5) constructing an uncertainty propagation model for the
radiative transfer link and synthesizing the
entrance pupil radiance uncertainty; (6) constructing an uncertainty propagation model for the imaging measurement link and synthesizing the restored radiance uncertainty; (7) analyzing the uncertainty components in the restored radiance and
ranking the uncertainty contributions of each link. This invention constructs an uncertainty propagation model for the forward modeling process of hyperspectral
remote sensing based on analytical formulas for uncertainty modeling and Monte Carlo distribution propagation. This model can quantify the uncertainty contributions of
radiative transfer and imaging measurement, providing
technical support for the quantitative application performance analysis and optimization of full-spectrum hyperspectral
remote sensing.