Camps-Valls Gustau, Campos-Taberner Manuel, Moreno-Martínez Álvaro, Walther Sophia, Duveiller Grégory, Cescatti Alessandro, Mahecha Miguel D, Muñoz-Marí Jordi, García-Haro Francisco Javier, Guanter Luis, Jung Martin, Gamon John A, Reichstein Markus, Running Steven W
Image Processing Laboratory, Universitat de València, 46980, Paterna, Spain.
Environmental Remote Sensing group (UV‑ERS), Universitat de València, 46100, Burjassot, Spain.
Sci Adv. 2021 Feb 26;7(9). doi: 10.1126/sciadv.abc7447. Print 2021 Feb.
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 CO and mitigating global climate change.
从光谱反射率数据得出的经验性植被指数在生物圈遥感中被广泛应用，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有力替代指标。在此，我们通过利用所涉光谱通道之间的所有高阶关系，对常用植被指数这一广泛类别进行了推广。这导致对植被生物物理和生理参数具有更高的敏感性。所提出的著名归一化差异植被指数（NDVI）的非线性推广在监测关键参数（如叶面积指数、总初级生产力和太阳诱导叶绿素荧光）时持续提高了准确性。结果表明，该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将使人们能够更准确地测量陆地碳源/汇动态以及稳定大气CO和缓解全球气候变化的潜力。
