Evaluation of wavelengths and spectral reflectance indices for high-throughput assessment of growth, water relations and ion contents of wheat irrigated with saline water

El-Hendawy, Salah; Al-Suhaibani, Nasser; Hassan, Wael M.; Dewir, Yaser Hassan; Elsayed, Salah; Al-Ashkar, Ibrahim; Abdella, Kamel A.; Schmidhalter, Urs

doi:10.1016/j.agwat.2018.09.009

Cited by 49 publications

(41 citation statements)

References 74 publications

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“…Hackl et al [36] found that the relationships between SRIs and phenotypic parameters under simulated close-to-field conditions were more robust compared with the results of pot-grown plants conditions. El-Hendawy et al [38] also found that the hyperspectral data were successfully used for indirect tracking changes in ion content and leaf water relations of two wheat genotypes differing in their salt tolerance under SWRT-growth platform. Therefore, the growth platform of the SWRT technique, which their advantages have been mentioned in the introduction section, may be able to test the performance of published and modified SRIs for tracking changes in growth and photosynthetic efficiency of wheat under saline field conditions.…”

Section: Discussionmentioning

confidence: 99%

Ability of Modified Spectral Reflectance Indices for Estimating Growth and Photosynthetic Efficiency of Wheat under Saline Field Conditions

et al. 2019

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Hyperspectral sensing offers a quick and non-destructive alternative for assessing phenotypic parameters of plant physiological status and salt stress tolerance. This study compares the performance of published and modified spectral reflectance indices (SRIs) for estimating and predicting the growth and photosynthetic efficiency of two wheat cultivars exposed to three salinity levels (control, 6.0, and 12.0 dS m−1). Results show that individual SRIs based on visible- and near-infrared (VIS/VIS, NIR/VIS, and NIR/NIR) estimate and predict measured parameters considerably more efficiently than those based on shortwave-infrared (SWIR/VIS and SWIR/NIR), with the exception of some modified indices (the water balance index (WABI-1(1550, 482), WABI-2(1640, 482), and WABI-3(1650, 531)), normalized difference moisture index (NDMI(1660, 1742)), and dry matter content index (DMCI(1550, 2305)), which show moderate to strong relationships with measured parameters. Overall results indicate that modified SRIs can serve as rapid and non-destructive high-throughput alternative approaches for tracking growth and photosynthetic efficiency of wheat under salt stress field conditions.

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Section: Discussionmentioning

confidence: 99%

Ability of Modified Spectral Reflectance Indices for Estimating Growth and Photosynthetic Efficiency of Wheat under Saline Field Conditions

et al. 2019

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“…Several studies reported that the importance of PLSR lies in identifying the very best waveband regions from the full spectrum which are directly and indirectly linked to the measured parameters 8,12,16,43,48,49 . The importance in projection (VIP) is a main factor in the PLSR model and provides insight into the importance of each wavelength in the full spectrum when removing wavelengths with low predictive power.…”

Section: Discussionmentioning

confidence: 99%

“…El-Hendawy et al . 12 reported that changes in leaf water relations and ion content in wheat plants grown under salinity stress conditions influence the spectral reflectance in the VIS (434–488 nm and 503–632 nm), red-edge (701–743 nm), near-infrared (NIR; 1100–1300 nm), and SWIR (1706–1898 nm) regions.…”

Section: Introductionmentioning

confidence: 99%

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

El-Hendawy

Al-Suhaibani

Alotaibi

et al. 2019

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The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

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“…With the rapid development of proximal remote sensing techniques and multivariate data analysis, it is possible to conduct a simultaneous indirect assessment of multiple agronomic parameters in a rapid, efficient, and non-destructive manner [ 23–27 ]. To achieve this, it is important to determine the relationship between different agronomic parameters and their hyperspectral reflectance properties.…”

Section: Introductionmentioning

confidence: 99%

Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices

et al. 2019

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Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D 1 : 150, D 2 : 250, D 3 : 350, D 4 : 450, and D 5 : 550 seeds m -2 ) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D 3 or D 4 with 1.00 ET, while the combination of 0.75 ET and D 3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D 2 , D 3 , or D 4 or when the 0.50 ET was combined with D 2 or D 3 , as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.

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Evaluation of wavelengths and spectral reflectance indices for high-throughput assessment of growth, water relations and ion contents of wheat irrigated with saline water

Cited by 49 publications

References 74 publications

Ability of Modified Spectral Reflectance Indices for Estimating Growth and Photosynthetic Efficiency of Wheat under Saline Field Conditions

Ability of Modified Spectral Reflectance Indices for Estimating Growth and Photosynthetic Efficiency of Wheat under Saline Field Conditions

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

Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices

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