Parameterization of leaf growth in rice (Oryza sativa L.) utilizing a plant canopy analyzer

Hirooka, Yoshihiro; Homma, Koki; Shiraiwa, Tatsuhiko; Kuwada, Mitsuo

doi:10.1016/j.fcr.2015.11.002

Cited by 20 publications

(24 citation statements)

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“…However, the simulated canopy reflectance will not vary if LAD and plant height do not change. The reflectance difference might be caused by a measurement error for LAI, which was estimated to be 30% in the paddy using the plant canopy analyzer LAI-2200 [36]. The fewer parameters set in the simulation may also explain this difference.…”

Section: Discussionmentioning

confidence: 99%

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

et al. 2019

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Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers’ paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.

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Section: Discussionmentioning

confidence: 99%

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

et al. 2019

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“…The LAI-2000 is among the most widely used advanced canopy LAI analyzers for many crops, such as cotton, soybean, and maize (Hirooka et al, 2016); in particular, it can be employed to measure leaf growth and perform LAI estimation in different rice cultivars under varying N fertilization regimes.…”

Section: Methodsmentioning

confidence: 99%

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

et al. 2017

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Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.

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“…For rice, the data were collected from field experiments at Iwate, Akita and Kyoto, Japan. The data from Kyoto were obtained from Hirooka et al (). For soybean, the datasets were obtained from soybean performance tests at Akita, Japan, and from growth chamber experiments at Iwate, Japan (Otera et al ).…”

Section: Methodsmentioning

confidence: 99%

Finlay–Wilkinson's regression coefficient as a pre‐screening criterion for yield responsiveness to elevated atmospheric CO₂ concentration in crops

Kumagai

Homma

Kuroda

et al. 2016

Physiologia Plantarum

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The rising atmospheric CO concentration ([CO ]) can increase crop productivity, but there are likely to be intraspecific variations in the response. To meet future world food demand, screening for genotypes with high [CO ] responsiveness will be a useful option, but there is no criterion for high [CO ] responsiveness. We hypothesized that the Finlay-Wilkinson regression coefficient (RC) (for the relationship between a genotype's yield versus the mean yield of all genotypes in a specific environment) could serve as a pre-screening criterion for identifying genotypes that respond strongly to elevated [CO ]. We collected datasets on the yield of 6 rice and 10 soybean genotypes along environmental gradients and compared their responsiveness to elevated [CO ] based on the regression coefficients (i.e. the increases of yield per 100 µmol mol [CO ]) identified in previous reports. We found significant positive correlations between the RCs and the responsiveness of yield to elevated [CO ] in both rice and soybean. This result raises the possibility that the coefficient of the Finlay-Wilkinson relationship could be used as a pre-screening criterion for [CO ] responsiveness.

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Parameterization of leaf growth in rice (Oryza sativa L.) utilizing a plant canopy analyzer

Cited by 20 publications

References 20 publications

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Finlay–Wilkinson's regression coefficient as a pre‐screening criterion for yield responsiveness to elevated atmospheric CO₂ concentration in crops

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Parameterization of leaf growth in rice (Oryza sativa L.) utilizing a plant canopy analyzer

Cited by 20 publications

References 20 publications

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Finlay–Wilkinson's regression coefficient as a pre‐screening criterion for yield responsiveness to elevated atmospheric CO2 concentration in crops

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Finlay–Wilkinson's regression coefficient as a pre‐screening criterion for yield responsiveness to elevated atmospheric CO₂ concentration in crops