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.

准确估计叶绿素含量对于诊断植被的生理和物候状态具有重要意义。利用遥感建立植被指数 (VIs) 和叶片叶绿素含量之间的关系对于大规模地球观测至关重要。然而，现有的叶绿素含量估算VI普遍存在饱和效应或依赖于特定场景，导致估算精度不足。本研究基于光-植被相互作用的物理机制，创新性地提出光谱空间的吸收三角和反射三角，构建叶绿素敏感的Sentinel-2三角植被指数（STVI）。STVI 使用 Sentinel-2 多光谱仪器 (MSI) 波段通过增强与叶绿素含量的关系并减轻饱和效应来提高叶绿素含量反演的准确性。模拟数据、实测数据和开放数据集用于测试 STVI 和 11 个经典 VI 在不同光谱数据类型、不同冬小麦生长阶段和不同植被覆盖度下反演叶绿素含量的准确性和稳定性。结果表明，STVI 比经典 VI 对叶绿素含量更敏感，并且在多种情况下提供了最佳拟合优度。STVI 代表了大规模叶绿素含量检索的强大工具和相关领域科学研究的新方法。