Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat grown in simulated saline field conditions

To overcome the salinity threats to crop production in arid conditions, wheat cultivars should be developed with better performance with regard to key physiological traits. Although different chlorophyll fluorescence (ChlF) parameters, such as maximum quantum PSII photochemical efficiency (Fv/Fm), quantum yield of PSII (Φ_PSII), and non-photochemical quenching (NPQ) have been proven to be key physiological traits to improve salt tolerance, their evaluation is time-consuming. In this study, hyperspectral canopy reflectance was used to assess ChlF parameters and grain yield (GY) of two wheat cultivars growing in simulated saline field conditions and exposed to three salinity levels (control, 6.0 dS m^-1, and 12.0 dS m^-1). Different spectral reflectance indices (SRIs) were formulated as ratios based on contour maps and tested for their relationship with ChlF parameters. The performance of individual SRIs and partial least squares regression (PLSR) models based on ChlF parameters, all examined SRIs, or data fusion of combined ChlF and SRIs to estimate the GY was considered. All examined SRIs failed to assess Φ_PSII and NPQ under control condition, but most of them showed a moderate to strong relationship with both parameters under the salinity levels of 6.0 and 12.0 dS m^-1. The examined SRIs showed a moderate and strong relationship with Fv/Fm under conditions of 6.0 and 12.0 dS m^-1, respectively. Most SRIs correlated better with the three ChlF parameters for the salt-sensitive cultivar Sakha 61 than for the salt-tolerant cultivar Sakha 93. Several SRIs exhibited strong relationships with GY under the salinity levels of 6.0 and 12.0 dS m^-1 and for both cultivars. Overall, the PLSR models exhibited additional improvements for estimating and predicting GY in both calibration and validation datasets over that using individual SRIs. The PLSR model based on data fusion was the best model to accurately estimate GY in the validation model even under control conditions. This study, of a type rarely conducted in simulated saline field conditions, indicates that the ChlF parameters could be linked to hyperspectral reflectance data for the rapid and non-destructive assessment of photosynthetic status and prediction of wheat production under salt stress field conditions.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Effects of selenite on chlorophyll fluorescence, starch content and fatty acid in the duckweed Landoltia punctata

Developing a Se-enriched feed for animal has become a considerable effort. In this study, Landoltia punctata 7449 was grown over a 12 day period under concentrations of selenite (Na2SeO3) from 0 to 80 μmol L(-1). The growth rate, the chlorophyll fluorescence, the starch content and fatty acid were measured. Se at low concentrations of ≤20 μmol L(-1) had positive effects also on growth rate, fatty acid content and yield of the L. punctata. The appropriate Se treatment enhanced the activity of the photosynthetic system by increasing Fv, Fm, Fv/Fm and Fv/Fo and decreasing Fo. However, negative impact to the L. punctata was observed when the duckweed was exposed to high Se concentrations (≥40 μmol L(-1)). Significant increases in starch content in the duckweed were observed after Se application. The present study suggests that the changes in growth rate, the photosynthetic system, the starch content and the fatty acid were closely associated with the application of Se. An increased Se concentration (0-20 μmol L(-1)) in duckweed could positively induce photosynthesis, thereby increasing the yield of L. punctata and could be a resource for high nutritive quality Se-enrich feed.