Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm

Abstract

The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had littleeffect.Visuallyscoreddelayedsenescencewaspositivelycorrelatedwith GYrangingfromr= 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.

In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral–Temporal Features

“…It seems highly questionable whether the PSRI is particularly sensitive to the Car/Chl ratio in diverse germplasm at the canopy level. Variation in the PSRI seems to arise primarily from differences in canopy structure among genotypes and from canopy structural changes over time (Anderegg et al, 2019, submitted ). The modified chlorophyll absorption ratio index (MCARI2; Haboudane et al, 2004), sensitive to green leaf area index, also showed strongly contrasting dynamic patterns ( Figure 2B ).…”

“…; soil type: eutric cambisol), in the wheat growing season of 2017–2018. A subset of 18 bread wheat ( Triticum aestivum ) genotypes was selected from the genom-analyse in biologischen system pflanze (GABI) wheat panel (Kollers et al, 2013; complemented with Swiss cultivars) for contrasting levels of resistance to STB and for contrasting stay-green properties based on previous experiments at the same location (Anderegg et al, 2019, submitted to this issue ; Karisto et al, 2018). The set comprised morphologically diverse genotypes (e.g., awned and unawned), and there were obvious differences in canopy structural parameters (e.g., flag leaf angle) among the selected genotypes ( Supplementary Figure 1 ).…”

“…BBCH scores within the main growth stages were linearly interpolated between assessment dates. Stay-green was assessed visually as described previously (Anderegg et al, 2019, submitted ), separately for the flag leaf and the whole canopy, following guidelines provided by Pask et al (2012). Flag leaf stay-green was scored based on the portion of green leaf area on a scale from 0 (0% green leaf area) to 10 (100% green leaf area).…”

“…Summarizing H 1 –H 3 , we hypothesized that the analysis of temporal dynamics in hyperspectral reflectance signatures may facilitate a robust detection and quantification of STB across diverse wheat genotypes under field conditions. To evaluate these hypotheses, we condensed the hyperspectral time series into time series of SVIs, similar to the procedure described previously (Anderegg et al, 2019, submitted ). Thereby, we obtained a comprehensive summary representation of the hyperspectral dataset collected over time, interpretable in terms of plant physiology and canopy characteristics.…”

“…As parametric models could not be fitted, the time point when interpolated values decreased below 50% of their initial value (t 50 ) was extracted as an equivalent to the M parameter of the Gompertz model. For more details on datasets and experiments, we refer to Karisto et al (2018) and Anderegg et al (2019, submitted ).…”