The retrival algorithms used for optical remote sensing satellite data to estimate Earth’s geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal to noise ratio (SNR) performance to meet science objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8 day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.