Wong Christopher Y S, Jones Taylor, McHugh Devin P, Gilbert Matthew E, Gepts Paul, Palkovic Antonia, Buckley Thomas N, Magney Troy S
Department of Plant Sciences, University of California, Davis, Davis, CA, 95616, USA.
Department of Earth & Environment, Boston University, Boston, MA, 02215, USA.
Plant Methods. 2023 Mar 28;19(1):29. doi: 10.1186/s13007-023-01001-5.
Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).
We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.
TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
遥感仪器能够跨尺度对植物性状和胁迫恢复力进行高通量表型分析。空间（手持设备、高塔、无人机、航空和卫星）和时间（连续或间歇）方面的权衡可能会促进或限制植物科学应用。在此，我们描述了TSWIFT（用于研究频繁时间序列的轮式高塔光谱仪）的技术细节，这是一种基于移动高塔的高光谱遥感系统，用于连续监测可见 - 近红外区域的光谱反射率，并能够解析太阳诱导荧光（SIF）。
我们展示了TSWIFT在高通量表型分析应用中监测植被短期（昼夜）和长期（季节）变化的潜在应用。我们将TSWIFT部署在一个对300个普通豆基因型的田间试验中，该试验有两种处理方式：对照（灌溉）和干旱（终末期干旱）。我们评估了归一化植被指数（NDVI）、光化学反射指数（PRI）和SIF，以及可见 - 近红外光谱范围（400至900纳米）内的变异系数（CV）。在生长季节早期，随着植物的初始生长和发育，NDVI追踪了结构变化。PRI和SIF更具动态性，呈现出昼夜和季节变化，能够量化基因型在干旱条件下生理反应的变异。除了植被指数外，高光谱反射率的CV在可见和红边光谱区域的基因型、处理方式和时间上表现出最大的变异性。
TSWIFT能够对高光谱反射率进行连续和自动化监测，以在高空间和时间分辨率下评估植物结构和功能的变异，用于高通量表型分析。像这样基于移动高塔的系统可以提供短期和长期数据集，以评估基因型和/或管理对环境的反应，并最终实现对资源利用效率、胁迫恢复力、生产力和产量的光谱预测。
