Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates

Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

“…A reasonable solution to address these issues is tracking the changes of these parameters using narrow-band visible (VIS)-to-shortwave infrared (SWIR) hyperspectral sensing. This allows to simultaneously monitor diverse and multiple specific alterations that could be induced by osmotic and ionic stresses of salinity such as changes in the internal leaf structure, plant water status, photosynthetic pigments and nutrient contents, photosynthetic potential, biomass accumulation, chlorophyll fluorescence, and more 5–10 . Consequently, alterations in these specific variables result in substantial variations in the absorption of specific wavebands in the VIS-SWIR domains of the spectrum.…”

Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes

“…The spectral signatures reflected from the plant canopy at specific wavelengths provide various types of cumulative information on the substantial and gradual changes that occur in specific plant characteristics or tolerance levels. These spectral signatures are closely associated with drought-induced changes that take place in several biochemical and biophysical plant characteristics, such as plant pigment concentrations, photosynthetic efficiency, internal leaf structures, green biomass, vegetative vigour, and plant water status (Gutierrez et al, 2010; Erdle et al, 2013; Lobos et al, 2014; Becker and Schmidhalter, 2017; Silva-Perez et al, 2018; El-Hendawy et al, 2019a; Lobos et al, 2019). Such changes in biochemical and biophysical plant characteristics, which can be related to genotypic differences and drought stress levels, can be detected through the substantial changes that tack place in the spectral signatures of the canopy measured in the visible (400–700 nm), near-infrared (700–1300 nm), and shortwave-infrared (1300–2500 nm) regions.…”

“…Several published SRIs have been used to successfully estimate different parameters such as aboveground biomass and water content, leaf area index, gas exchange and transpiration rates, stomatal conductance, ion and pigment contents, carbon isotope discrimination, yield components, and grain yield in several field crops under either normal or abiotic stress conditions (Erdle et al, 2013; Li et al, 2014; Lobos et al, 2014; El-Hendawy et al, 2015; Bayat et al, 2016; Becker and Schmidhalter, 2017; Garriga et al, 2017; Kawamura et al, 2018; El-Hendawy et al, 2019a; El-Hendawy et al, 2019b). For example, in diverse studies, several SRIs, which are related to plant biomass, plant water status, and plant photosynthetic efficiency, such as the green normalized difference vegetation index (GNDVI), normalized difference vegetation indices (NDVIs), SRIs related to normalized water indices (NWI-1, NWI-2, NWI-3, and NWI-4), and normalized difference moisture index (NDMI: 2200; 1100) showed significant correlation with final grain yield and explained more than 70% of yield variability under contrasting water irrigation regimes (Shanahan et al, 2001; Aparicio et al, 2002; Prasad et al, 2007; Lobos et al, 2014; Elazab et al, 2015; El-Hendawy et al, 2017a).…”

“…For example, in diverse studies, several SRIs, which are related to plant biomass, plant water status, and plant photosynthetic efficiency, such as the green normalized difference vegetation index (GNDVI), normalized difference vegetation indices (NDVIs), SRIs related to normalized water indices (NWI-1, NWI-2, NWI-3, and NWI-4), and normalized difference moisture index (NDMI: 2200; 1100) showed significant correlation with final grain yield and explained more than 70% of yield variability under contrasting water irrigation regimes (Shanahan et al, 2001; Aparicio et al, 2002; Prasad et al, 2007; Lobos et al, 2014; Elazab et al, 2015; El-Hendawy et al, 2017a). In addition, several studies have also reported that the SRIs formulated based on NIR wavelengths such as different SRIs related to normalized water indices (NWIs), SRIs that incorporate a combination of SWIR/VIS wavelengths such as the water band index (WABI) and SWIR/NIR wavelengths such as the normalized difference water index-1640 (NDWI-1640), normalized difference moisture index (NDMI), and three-band index (SRI (860, 1640, 2130)), red edge/NIR/SWIR wavelengths such as the three-band index (SRI (690, 905, 1550)) or VIS/NIR/SWIR wavelengths such as the three-band index (SRI (974, 518, 1392) and SRI (762, 518, 1930)) were found to be effective for tracking changes in plant water status under various water treatments (Gutierrez et al, 2010; Rischbeck et al, 2014; Yao et al, 2014; Junttila et al, 2016; Elsayed et al, 2017; Rapaport et al, 2017; El-Hendawy et al, 2019a). These indicate that we can deal with different SRIs as indirect selection traits like the traditional physiological traits related to photosynthesis efficiency (photosynthesis rate, stomatal conductance, and transpiration rate) or those related to plant or leaf water status like relative water content, leaf water potential, and equivalent water thickness.…”

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

“…Based on the mass of the leaves at different dehydration stages, the water content is expressed as Fuel Moisture Content (FMC) with freshf -or dry basisd - [39,[52][53][54] and Equivalent Water Thickness (EWT) [52,53,[55][56][57][58]. The former depends only on the leaf mass (Equations (1) and (2)), while the latter also requires its area (Equation (3)):…”