Excess tree mortality in the wake of climate extremes has been observed globally. However, we still lack precise data on mortality at global scale to understand respective drivers and spatiotemporal dynamics. The Sentinel-2 satellite fleet, equipped with the MultiSpectral Instrument (MSI), covers the entire earth on average every five days at spatial resolutions ranging from 10 m to 60 m. Mapping tree mortality from Sentinel-2 globally in diverse ecosystems requires equally diverse reference data. Using globally distributed high-resolution aerial orthoimagery reference data and artificial intelligence methods, we can translate spectral signatures of remote sensing into deadwood. Specifically, in this study we show how to predict the share of standing deadwood for a 10 m pixel in a specific year. The method takes into account temporal patterns, spatial context, as well as all Sentinel-2 spectral bands. This will enable us to map tree mortality globally at a new level of precision.