Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies

Winterhalter, Loïc; Mistele, Bodo; Schmidhalter, Urs

doi:10.1016/j.fcr.2012.01.007

Cited by 49 publications

(28 citation statements)

References 18 publications

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“…The vertical distribution of vegetation biophysical and biochemical properties and leaf angle greatly affects the amount of reflected light, and in turn affects the estimation of vegetation properties. Studies have found that a maize canopy has bell-shaped vertical distribution of LAI [32], chlorophyll content [33], leaf nitrogen content and biomass [34]. The study showed that the red-edge Chlorophyll Index (CIred edge) sensed the chlorophyll content of the upper seven to nine leaf layers in a maize canopy [33].…”

Section: Introductionmentioning

confidence: 96%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.

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Section: Introductionmentioning

confidence: 96%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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“…Immediately following spectral reflectance measurement, LCC and leaf fresh weight (LFW) were measured. LCC was represented by values obtained by SPAD-502 (Minolta Camera Co., Osaka, Japan), due to the good correlation with the extractable chlorophyll content [22]. Fifteen to Twenty points were selected randomly on the leaf; their average value was considered as the LCC.…”

Section: Leaf Biochemical Parameter Measurementmentioning

confidence: 99%

Nondestructive Evaluation of Inoculation Effects of AMF and Bradyrhizobium japonicum on Soybean under Drought Stress From Reflectance Spectroscopy

Kong¹,

Bi²,

Huang³

et al. 2020

Soybean for Human Consumption and Animal Feed

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Precise estimation of leaf chlorophyll content (LCC) and leaf water content (LWC) of soybean, using remote sensing technology, provides a new avenue for the nondestructive evaluation of inoculation effects of arbuscular mycorrhizal fungi (AMF) and Bradyrhizobium japonicum (BJ) on soybean growth condition. In this study, a series of pot experiments were conducted in the greenhouse, soybean inoculated with Glomus intraradices (G.i, one of AMF species), G.i and BJ, and non-inoculation were planted under drought stress (DS) and normal irrigation (NI) conditions. Leaf spectra and LCC and LWC were measured on the 28th and 56th days after inoculation. Two new simple ratio (SR) indices, derived from the first derivative spectral reflectance at λ1 nm (D λ1) and the raw spectral reflectance at λ2 nm (R λ2), were developed to estimate LCC and LWC. The results indicate that under DS, plants inoculated with G.i had higher LCC and LWC than the non-inoculated plants, followed by the counterparts co-inoculated with G.i and BJ. Linear estimation models, established by the D 650 /R red edge and D 1680 /R 680 , achieved great improved accuracy for quantifying LCC and LWC of soybean under inoculation and drought stress treatments, with determination of coefficient of 0.63 and 0.76, respectively.

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“…Considerable research also showed that the canopy architecture has an effect on the estimation of biomass, the correlation coefficient between LAI and biomass was 0.96 [156] , and the vertical biomass distribution of maize is a bell shape. Hence, considering the canopy parameters is necessary to estimate plant biomass [151] .…”

Section: Biomass Measurementsmentioning

confidence: 99%

“…NIR regions were applied to measure nitrogen and estimate biomass. SPAD and ASD FieldSpec were widely used, and prediction models were developed based on the hyperspectral reflectance of plant canopy [149][150][151] . Gnyp et al [152,153] proposed a vegetation index with the use of a multi-band combination in the NIR and short-wave infrared domains to develop biomass model, which can improve the estimation of above ground biomass.…”

Section: Biomass Measurementsmentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

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Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies

Cited by 49 publications

References 18 publications

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Nondestructive Evaluation of Inoculation Effects of AMF and Bradyrhizobium japonicum on Soybean under Drought Stress From Reflectance Spectroscopy

Sensors for measuring plant phenotyping: A review

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