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.

The Jordan River Basin, and its seven sub-catchments of the Central Wasatch Mountains immediately east of Salt Lake City, UT, are home to an array of research infrastructrure that collectively form the Wasatch Environmental Observatory (WEO). Each sub-catchment is comprised of a wildland to urban land use gradient that spans an elevation range of over 2000 m in a linear distance of ~25km. Geology varies across the sub-catchments, ranging from granitic, intrusive to mixed sedimentary rocks in uplands that drain to the alluvial or colluvial sediments of the former Lake Bonneville. Vegetation varies by elevation, aspect, distance to stream channels, and land use.  The sharp elevation gradient results in a range of precipitation from 700 to 1200 mm/yr (roughly 2/3 as snow) and mean annual temperature from 3.5 o to 6.8o C. Spring snowmelt dominates annual discharge. Although climate is relatively similar across the catchments, annual water yield varies spatially by more than a factor of 3, ranging from 0.18 to 0.63. With historical strengths in ecohydrology, water supply, and social-ecological research, current infrastructure supports both basic and applied research in meteorology, climate, atmospheric chemistry, hydrology, ecology, biogeochemistry, resource management, sustainable systems, and urban redesign. Climate and discharge data span over a century for the seven sub-catchments of the larger basin. These data sets, combined with multiple decades of hydrochemistry, isotopes, ecological data sets, social survey data sets, and high-resolution LiDAR topography and vegetation structure, provide a baseline for long-term data collected by NEON, public agencies, and individual research projects. The combination of long-term data with active state of the art observing facilities allows WEO to serve as a unique natural laboratory for addressing research questions facing rapidly growing, seasonally snow-covered, semi-arid regions worldwide and an excellent facility for providing student education and research training.