Since commercial image detectors, such as charge-coupled device (CCD) cameras, have a limited dynamic range, it is difficult to obtain images that really are unsaturated, as a result of which the reflectance parameters may be inaccurately estimated. To solve this problem, we describe a method to estimate reflectance parameters from saturated spectral images. We separate reflection data into diffuse and specular components at 5-nm intervals between 380nm and 780nm for each pixel of the spectral images, which are captured at different incident angles, and estimate the diffuse reflectance parameters by applying the Lambertian model to the diffuse components. To estimate the specular reflectance parameters from the specular components, we transform the Torrance-Sparrow equation to a linear form, assuming Fresnel reflectance is constant. We then estimate specular parameters for intensity of the specular reflection and standard deviation of the Gaussian distribution, using the least squares method from unsaturated values of the specular components. Since Fresnel reflectance contributes to the physically based Torrance-Sparrow model in computer graphics and vision, we estimate both the Fresnel reflectance in terms of the Fresnel equation for the incident angle and the refractive index of the surface for dielectric materials, which varies with wavelength. We carried out experiments with measured data, and with simulated specular components at different saturation levels, generated according to the Torrance-Sparrow model. Our experimental results reveal that the diffuse and specular reflectance parameters are estimated with high quality.