Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.

Heterogeneous snow accumulation in the mountains introduces uncertainty to water-supply forecasting in much of the world. Water managers’ awareness of the challenge may account for forecast errors in management decisions. We assess the impact of uncertainty in seasonal-water-supply forecasts on reservoir management using the western slope of the Sierra Nevada of California as a case study. We find that higher forecast uncertainty decreases the volume of water released from reservoirs between April and July, suggesting that water managers hedge against the possibility of lower-than-expected runoff. We modeled April-July water releases as a function of corresponding runoff forecasts, their reported uncertainty, and available storage capacity. An unbalanced (n=416) panel data model with fixed effects suggests that if uncertainty goes up by 10 units, water managers reduce releases by about 6 units, even holding the mean forecast constant. The forecast volume, its uncertainty, available storage capacity, and the interaction between forecasted volume and uncertainty were all statistically significant predictors (p < 0.005) of releases. Increased forecast uncertainty and increased available storage were significantly and inversely associated with April-July release volume, whereas forecast volume and the interaction between forecast uncertainty and forecast volume were significantly and positively associated with release volume. These results support the hypothesis that water managers behave as if they are risk-averse with respect to the possibility of less runoff than forecasted. Thus, reducing operational forecast uncertainty may result in more water being released, without the need for direct coordination with water managers.

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.