Integrating Earth Observations into Ecosystem Services Models: Challenges and Opportunities from Producers, Users and Decision Makers

The benefits of using Earth Observations (EO) to model and monitor ecosystem services (ES) have been widely articulated, yet EO/ES integration remains limited in practice. Benefits of EO/ES integration include expanding spatial or temporal coverage (to new locations or globally, or over historical periods lacking field data), enhancing the spatial/temporal resolution, advancing representation of ecosystem processes or status, monitoring ES indicators over time, and improving accuracy and consistency of ES model outputs, particularly across regions. While there may be additional context-specific challenges, we have identified several near-universal grand challenges to the integration of EO in ES modeling. First, existing model structures will need to be adapted or even re-imagined to accommodate continuous variables (e.g., % tree cover) instead of discrete (e.g., forest, non-forest). Second, utilizing EO data to drive ES models in new locations may require calibration/validation using local data that is often lacking. Third, relevant EO data products, such as regionally accurate land cover, may not be continuously available in all locations or for the period of interest. Fourth, using EO in ES monitoring is hindered by lack of alignment between ecosystem phenomena of interest and EO data, in part a result of vaguely defined metrics (e.g., what does "degradation" mean?). Fifth, ES model users may not have sufficient advanced knowledge of EO derived products to discern those best fit to their purposes. Sixth, integrating academic and expert advancements using EO in ES modeling into decision support tools used in real decision contexts is often slow.

There is tremendous opportunity and capacity for addressing these challenges within and beyond the EO community. Initiatives and projects that do so successfully will include integrating user and application needs into the concept and design of EO missions; developing and incentivizing private-public partnerships for funding, research, and application; formation of interdisciplinary teams of decision makers, EO producers, ES modelers, and computer scientists; and collaboration on best practices for data design and access. These recommendations are the result of three workshops on integrating EO into ES models supported by NASA.