Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment

Thapa, Sushil; Rudd, Jackie C.; Xue, Qingwu; Bhandari, Mahendra; Reddy, Srirama Krishna; Jessup, Kirk E.; Liu, Shuyu; Devkota, Ravindra N.; Baker, John O.; Baker, Shannon

doi:10.1080/15427528.2019.1648348

Cited by 39 publications

(29 citation statements)

References 38 publications

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“…Like dryland environment, the UAS-based features obtained after heading had stronger correlations with grain yield. This result agrees with Thapa et al [85] in which they showed a positive relationship between yield and Green Seeker-based NDVI data obtained in the same location using different sets of genotypes in the same year. Positive correlation between spectral indices and grain yield under irrigated environment was found in several other studies in wheat [36].…”

Section: Association Between Uas-based Canopy Traits and Grain Yieldsupporting

confidence: 92%

“…Naser et al [49] found a positive association between wheat grain yield and NDVI measured at anthesis, and during grain filling period under dryland environment. Thapa et al [85] also found a significant correlation between yield and NDVI measured after the jointing stage of wheat. However, they cautioned about using NDVI for screening genotypes for yield under extreme drought conditions.…”

Section: Association Between Uas-based Canopy Traits and Grain Yieldmentioning

confidence: 86%

“…The relationship between spectral VIs and grain yield under dryland has been established in several previous studies [78][79][80][81]. Canopy features and vegetation indices determined by the amount and intensity of the green canopy has been used to estimate above-ground biomass in wheat [82][83][84] and the biomass is correlated with grain yield [85]. This leads to the association of canopy features with grain yield.…”

Section: Association Between Uas-based Canopy Traits and Grain Yieldmentioning

confidence: 99%

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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

et al. 2021

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Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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Section: Association Between Uas-based Canopy Traits and Grain Yieldsupporting

confidence: 92%

Section: Association Between Uas-based Canopy Traits and Grain Yieldmentioning

confidence: 86%

Section: Association Between Uas-based Canopy Traits and Grain Yieldmentioning

confidence: 99%

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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

et al. 2021

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“…Plant genotypes respond differently to climate, soil type, irrigation, and other management practices, thus we need as many studies as possible to complement the existing literature. As the field of high throughput plant phenotyping (HTPP) for plant breeding and selection, is still in its infancy [30][31][32], this study is a valuable addition to this rapidly growing field [16,[33][34][35][36]. Use of several genotypes in this study adds breadth to the relationship between spectral measurements and yield in winter wheat.…”

Section: Introductionmentioning

confidence: 99%

“…Use of several genotypes in this study adds breadth to the relationship between spectral measurements and yield in winter wheat. Moreover, relatively few studies [36,37] have been conducted in the semi-arid climate to assess the use of spectral measurements for wheat genotype productivity estimation and classification. This study also includes two irrigation conditions, which provides more useful information for a diverse group of scientists and producers.…”

Section: Introductionmentioning

confidence: 99%

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

et al. 2020

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Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

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Monitoring winter wheat growth at different heights using aerial imagery

Miller

Adkins

2021

Agronomy Journal

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Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment

Cited by 39 publications

References 38 publications

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Monitoring winter wheat growth at different heights using aerial imagery

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