The Earth generates hundreds of petabytes of satellite data annually, creating unprecedented opportunities to monitor planetary-scale environmental changes. Geospatial foundation models based on satellite data are now demonstrating capabilities from wildfire mapping to biodiversity monitoring that could transform climate adaptation and policy. However, these powerful models remain largely inaccessible or impractical for domain experts. Ecologists, urban planners, disaster managers, and environmental scientists typically lack the machine learning expertise required to leverage foundation model capabilities. This accessibility gap represents a barrier to addressing planetary-scale challenges. We analyze the systematic user experience failures that prevent domain expert adoption and outline a research agenda for making geospatial AI models more accessible to practitioners.