Leaf Chlorophyll Content (LCC) serves as an indicator of vegetation photosynthetic capacity. The quick and accurate characterization of LCC is essential for assessing vegetation productivity and carbon sequestration capacities. Remote sensing technology provides various indicators that are important tools for large-scale LCC monitoring. Among these indicators, vegetation indices (VIs) composed of multiple spectral band reflectance combinations are widely used to characterize LCC variations due to their simplicity and effectiveness. However, current LCC-related VIs are often affected by canopy structure and prone to saturation when LCC exceeds 40 µg/cm², which limits their accuracy and sensitivity in capturing LCC variations. To address these issues, this study proposed a novel red-edge Structure-resistant Chlorophyll Index (SCI). Based on the Global Sensitivity Analysis (GSA, for selecting optimal spectral bands related to LCC) and a three-dimensional radiative transfer model (the LESS model, for guiding the SCI formulation and evaluating VIs performance), SCI utilizes four Sentinel-2 bands (the Red, Red Edge 1, Red Edge 2, and Near-Infrared bands) to minimize canopy structural effects and maintain linearity with LCC, indicating SCI has a strong saturation resistance to LCC. To validate its effectiveness, we evaluated SCI using simulated datasets, ground-based observation datasets, and Sentinel-2 imagery. The results indicated that SCI exhibited a strong linear relationship with LCC in simulated datasets (R² = 0.84) and achieved higher consistency with LCC than other chlorophyll-related VIs in both ground-based observations (R² = 0.66) and Sentinel-2 imagery (R² = 0.40). This study demonstrated that SCI can effectively mitigate canopy structural effects and expand the linear response range of LCC, making it a reliable indicator for enhancing the accuracy and efficiency of LCC monitoring, thereby supporting ecosystem assessment and the global climate change research.