Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO₂ and mitigating global climate change.

从光谱反射率数据得出的经验植被指数广泛用于生物圈的遥感，因为它们代表了冠层结构、叶片色素含量以及随后的植物光合潜力的强大代理。在这里，我们通过利用所涉及的光谱通道之间的所有高阶关系来概括常用的植被指数的广泛系列。这导致对植被生物物理和生理参数的更高敏感性。著名的归一化差异植被指数 (NDVI) 的非线性推广不断提高监测关键参数的准确性，例如叶面积指数、总初级生产力和太阳诱导的叶绿素荧光。结果表明，统计方法最大限度地利用了光谱信息，并解决了陆地生物圈卫星地球观测中长期存在的问题。非线性 NDVI 将允许更准确地测量陆地碳源/汇动态和稳定大气 CO 的潜力₂减缓全球气候变化。