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Recent advances in remote sensing of biodiversity and biodiversity-related products have significantly enhanced our capacity to monitor and understand biodiversity. Typical remote sensing products directly related to biodiversity are spectral features and plant traits, and their diversity in space, i.e., spectral diversity and functional diversity. Hence, remote sensing of biodiversity involves measuring biophysical quantities from signals recorded by a sensor in response to radiation reflected from the Earth’s surface. As for any other measurements, the biodiversity quantities estimated via remote sensing are inherently uncertain. Starting from the digital numbers recorded by the detector, the processing to obtain surface reflectance products, to the final biodiversity output, various sources of uncertainty can arise. Failing to account for such uncertainties may lead to over- or underestimates of diversity, with downstream repercussions on management strategies and policy making. Nevertheless, uncertainties are rarely quantified in remotely sensed biodiversity products, limiting our understanding of biodiversity processes and their detection. Sparse quantification of uncertainties is further exacerbated by the confusion arising from the inconsistent and improper use of uncertainty terms. Here, we clarify the concept of uncertainty by defining what it is and what it is not, outlining its typologies, highlighting sources of uncertainty and providing examples of uncertainty estimation and propagation. Our examples are based on spaceborne imaging spectroscopy data to propagate surface reflectance uncertainties into vegetation indices, principal components, plant traits, and spectral diversity metrics. By raising awareness of the magnitude and implications of uncertainty, establishing a shared terminology, and proposing a practical framework for uncertainty estimation, we contribute toward more transparent, interpretable, and ultimately more reliable remotely sensed biodiversity products.