Since the beginning of space-borne remote sensing less than two decades ago, sensor technologies have greatly advanced. State-of-the-art sensor systems, such as the Earth Observing System (Eos), will have higher spatial, spectral, radiometric resolutions, which are selected together to enhance the capabilities of differentiating surface categories. Multiple, pointable platforms covering different parts of electromagnetic spectrum will circle the earth, detect and monitor terrestrial changes, and measure the essential surface and atmospheric parameters. It is anticipated that sensors of future generations will have even greater spectral, spatial, and radiometric resolutions. However, resolutions cannot increase without bound. Noise of electronic, mechanical, optical, and atmospheric origins limits the effective resolutions of the measurements. In this paper, several aspects of the effects of radiometric resolution on remotely sensed data are examined. It is shown that higher radiometric resolution indeed improves information content. But to improve the utilization of the spectrometer, radiometric sensitivity must also be modified. Using clusters constructed from empirical signatures, it is shown that discriminability between clusters converges beyond 6 bits. It is also shown that the information content of current sensor measurements is not limited by the atmosphere, but by the sensitivity settings of the spectrometers. It is proposed that a spectrometer with variable sensitivity and capable of sampling scene radiance into full dynamic range be used as a means of optimizing information content. If implemented, the same amount of information content currently observed could be measured with fewer bits.