Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI)

Fitzgerald, Glenn J.; Rodrı́guez, Daniel; O’Leary, G.

doi:10.1016/j.fcr.2010.01.010

Cited by 274 publications

(133 citation statements)

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“…The original publication is available at http://www.sciencedirect.com Doi: 10.1016Doi: 10. /j.fcr.2017 in nitrogen dilution within the foliage (Baret et al 2007;Fitzgerald et al 2010), and to reach a significantly stronger correlation between CNC and CCC ( Fig. 3.c).…”

Section: Discussionmentioning

confidence: 99%

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

198

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Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content ( ≤ 10 %). In addition, VIs show a great potential to retrieve canopy nitrogen content ( = 10 %). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs ( = 17 %). As expectedAuthor-produced version of the article published in Field Crops Research, 2017, N°210, p.33-46. The original publication is available at http://www.sciencedirect.com Doi: 10.1016Doi: 10. /j.fcr.2017 when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively.Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAVembedded multispectral cameras, with which only a few wavebands are available.

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Section: Discussionmentioning

confidence: 99%

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

198

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“…Moreover, the MSR may have improved linearity and mitigated the saturation effect when LAI increased [57]. NDRE had the strongest relationship with CNA (R 2 = 0.794 and RMSE = 37.75 kg N•ha −1 ), and this index was measured with wavebands centered at 720 and 790 nm, which were more sensitive to estimating canopy N per unit area than canopy N concentration [48]. LAI and CNA were individually used as state variables for integrating remote sensing data into the DSSAT-CERES model with the PSO data assimilation method.…”

Section: Discussionmentioning

confidence: 99%

“…Eitel et al [44] MERIS terrestrial chlorophyll index (MTCI) (R750 − R710)/(R710 − R680) Dash et al [45] Standardized LAI Determining Index (sLAIDI) S(R1050 − R1250)/(R1050 + R1250), S = 5 Delalieux et al [46] Enhanced VI (EVI) 2.5(R800 − R660)/(1 + R800 + 2.4R660) Jiang et al [47] Normalized difference red edge index (NDRE) (R790 − R720)/(R790 + R720) Fitzgerald et al [48] Normalized difference chlorophyll index (NDCI) (R708 − R665)/(R708 + R665) Mishra et al [49] Double-peak canopy nitrogen index (DCNII) (R750 − R700)/(R700 − R670)/(R750 − R670 + 0.09) Jin et al [50] Three band water index (TBWI) (R973 − R1720)/R1447 Jin et al [51] Note: # represents vegetation index.…”

Section: Mcari/mtvi2mentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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Abstract:The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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“…Whereas nondestructive methods to estimate %N for pear leaves have been presented (Jie et al, 2014), methods to estimate %N at canopy scales for pear orchards have yet to be developed. Previous research (Fitzgerald et al, 2010;Perry et al, 2012) shows that using remote sensing to estimate N in wheat canopies is effective, and calculation of the Canopy Chlorophyll Content Index (CCCI) from canopy spectral reflectance of wheat showed good correlation with N uptake (kg N/ha). In this research, we extend the previous research on wheat to redblush pears, acquiring and analyzing datasets of remote sensing measurements and corresponding leaf %N from laboratory analysis to determine the feasibility of remote sensing to characterize N status.…”

mentioning

confidence: 99%

Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

Perry¹,

Goodwin²,

Cornwall³

2018

horts

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Abstract. Reflectance measurements at leaf and canopy scales were made in a red-blush pear (Pyrus communis) orchard for two growing seasons. Canopy reflectance measurements were obtained using a multispectral camera flown on board an unmanned aerial vehicle (UAV), and leaf reflectance measurements were undertaken in a laboratory using a portable spectrometer. These measurements were used to compute reflectance indices as surrogates for direct leaf nitrogen (N) concentration measurements. The indices were evaluated against laboratory analysis of leaf N concentration. Regression results for leaf %N on canopy-level measurements with the multispectral camera resulted in the highest R

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Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI)

Cited by 274 publications

References 20 publications

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

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