Scalable Mapping of Urban Green Cooling Services Using AlphaEarth Foundations

Under the combined influence of global warming and rapid urbanization, extreme heat has become a major challenge for urban resilience and public health. Urban green infrastructure provides important cooling benefits through evapotranspiration and shading, yet spatially explicit assessment of these services remains challenging. Existing approaches rely heavily on computationally expensive physical models and dense input data, which limits their applicability beyond well-studied regions. In this study, we present a scalable approach for mapping urban green cooling services by combining Earth observation foundation models with insights from process-based modeling. We use the Green Cooling Services Index (GCoS) as the core metric, which is derived from simulations of the Soil-Canopy-Observation of Photosynthesis and Energy Fluxes (SCOPE) model. To enable large-scale applications, we build a surrogate model that maps annual multimodal satellite embedding vectors from AlphaEarth Foundations to GCoS reference data. These embeddings integrate multisource Earth observation information across the full year, capturing key vegetation phenology and climate dynamics. The analysis covers 14 Functional Urban Areas across Europe and surrounding regions. Model performance is evaluated through three complementary experiments: a continent-scale assessment, a leave-one-city-out test, and stratified error analyses in representative cities. Results show that the surrogate approach can reproduce vegetation cooling effects with high accuracy while requiring substantially fewer data and computational resources than conventional physical models. Importantly, the model maintains stable performance when applied to cities not included in training. This framework addresses a key scalability gap in urban heat assessments and enables consistent mapping of green cooling services in data limited regions.