Accurate estimation of chlorophyll content is important for diagnosing the physiological and phenological status of vegetation. Establishing the relationship between vegetation indices (VIs) and leaf chlorophyll content using remote sensing is crucial for large-scale earth observation. However, existing VIs for estimating chlorophyll content generally suffer from the saturation effect or depend on specific scenarios, resulting in insufficient estimation accuracy. Based on the physical mechanism of light-vegetation interaction, this study innovatively proposes the absorption triangle and reflectance triangle in the spectral space to construct the chlorophyll-sensitive Sentinel-2 Triangular Vegetation Index (STVI). The STVI uses the Sentinel-2 multispectral instrument (MSI) bands to improve the accuracy of chlorophyll content retrieval by enhancing the relationship with the chlorophyll content and mitigating the saturation effect. Simulated data, measured data, and open data sets were used to test the accuracy and stability of the STVI and 11 classical VIs for retrieving the chlorophyll content using different spectral data types, different winter wheat growth stages, and different vegetation coverages. The results showed that the STVI was more sensitive to the chlorophyll content than the classical VIs and provided the best goodness-of-fit in multiple scenarios. The STVI represents a powerful tool for large-extent chlorophyll content retrieval and a novel approach for scientific research in related fields.