Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with “top-down” inversion analyses that assimilate atmospheric CO2 observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System-Flux (CMS-Flux) that allows high resolution atmospheric transport (0.5° × 0.625°) over a North America domain. Relative to a 2018 baseline, we find US Midwest growing season net carbon uptake is reduced by 11-57 TgC (3-16%) for 2019 (inversion mean estimates across experiments). These estimates are found to be consistent with independent “bottom-up” estimates of carbon uptake based on vegetation remote sensing. We then investigate current limitations in tracking regional carbon emissions and removals by ecosystems using “top-down” methods. In a set of observing system simulation experiments, we show that the ability to recover regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space-based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top-down estimates of ecosystem responses to extreme climate events.

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.