High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel

作者：Yufeng Ge, Abbas Atefi, Huichun Zhang, Chenyong Miao, Raghuprakash Kastoori Ramamurthy, Brandi Sigmon, Jinliang Yang, James C. Schnable · 发表于：Plant Methods · 年份：2019 · DOI：10.1186/s13007-019-0450-8 · 被引用次数：194 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Spectroscopy and Chemometric Analyses

Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR–SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS–NIR–SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties. Some VIs were able to estimate CHL and N (R 2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R 2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicte...