Survivorship bias refers to the fact that conclusions are drawn from a non-representative sample limited to cases that have survived a selection process. This article shows that this bias affects scientific literature, which tends to select successful experiments and hide failures. Remote sensing, like other data-driven sciences, is affected by survivorship bias, making it difficult to have a clear idea of the data's and methods' actual potential and limitations. A typology of failure causes is proposed to encourage critical reading of the bibliography, and perspectives are outlined to overcome survivorship bias by improving practices within the academic and industrial remote sensing communities.