Maize black layer (BL) is the developmental stage where plants reach physiological maturity, which is a desirable trait, but phenotyping this trait is time consuming and costly. Any tool enabling high-throughput phenotyping of BL would be an invaluable asset for developing or maintaining short-season maize hybrids. This thesis investigated the potential of using remote sensing (RS) technologies to detect BL. The spectral signatures of 16 short-season maize hybrids were captured using a ground-based hyperspectral sensor through the late grain filling period (GFP) which were then used to develop and test vegetation indices (VIs) that correlated with BL formation. Two of these VIs, the normalized green red difference index (NGRDI) and the novel VI.5 were highly accurate, specific, and had spectral curves that match the physiology of maize in the late GFP. These findings suggest that high-throughput phenotyping of maize BL is possible using RS technologies.