Abstract Flash drought has garnered major attention due to its devastating impact on both agricultural and ecological systems impacting production and income for producers, due to its rapid onset nature, and poor predictability. Solar‐induced fluorescence (SIF) is an increasingly well‐known and reliable indicator of vegetation health which captures rapid changes in how plants absorb and use light under stress. Recent efforts leveraging machine learning trained on spaceborne SIF and vegetation data from other sensors are producing seamless maps of SIF at spatial resolutions (∼5 km) aligned with land management needs. Three recent studies, Mohammadi et al. (2022, https://doi.org/10.1073/pnas.2202767119 ), Parazoo et al. (2024, https://doi.org/10.1029/2024GL108310 ), and Behera et al. (2025, https://doi.org/10.1029/2024GL113419 ), directly show the value of high‐resolution SIF mapping for flash drought early warning. Through advanced studies and calibration, we contend that SIF‐based data products are ready for immediate use by the drought monitoring community including the U.S. Drought Monitor.