Green area index from an unmanned aerial system over wheat and rapeseed crops

Verger, Aleixandre; Vigneau, Nathalie; Chéron, Corentin; Gilliot, Jean-Marc; Comar, Alexis; Baret, Frédéric

doi:10.1016/j.rse.2014.06.006

Cited by 160 publications

(113 citation statements)

References 39 publications

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“…PRO4SAIL (simply referred to as PROSAIL hereafter) was inverted for each viewing angle independently based on the widely used Look-Up Table (LUT) approach (Baret and Buis 2008;Darvishzadeh et al 2011;Dorigo 2012;Duan et al 2014;Verger et al 2014;Verrelst et al 2015;Vohland et al 2010). This consists in (1) generating a synthetic data set from multiple runs of the canopy reflectance model based on different sets of input variables, and (2) calculating the solution of the inverse problem based on the LUT entries whose simulated spectra are the closest to the measured one.…”

Section: Prosail Based Approachmentioning

confidence: 99%

“…These distributions were bounded by the minimum and maximum values presented in Table 4 and determined according to the literature (Darvishzadeh et al 2011;Dorigo 2012;Duan et al 2014;Jacquemoud et al 1995;Verger et al 2014) and the reference measurements displayed in Table 2. its specific distribution (which is uniform in this case).…”

Section: Prosail Based Approachmentioning

confidence: 99%

“…For both parameters, we used Gaussian distributions with means and standard deviations obtained from reference measurements (Table 2), similarly to Verger et al (2014). This allows each LUT to have more entries corresponding to the most frequently observed LAI and Cab values.…”

Section: Prosail Based Approachmentioning

confidence: 99%

“…PROSAIL (Baret et al 1992;Jacquemoud et al 2009) is one of the most widely used models, especially because it offers a good compromise between realism and simplicity (Verger et al, 2014) and because it is freely available to the community. PROSAIL simulates the canopy reflectance by combining the PROSPECT (Leaf Optical Properties Spectra) model of leaf optical properties (Jacquemoud and Baret 1990) and the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model (Verhoef 1984;Verhoef 1985).…”

Section: Introductionmentioning

confidence: 99%

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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

202

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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: Prosail Based Approachmentioning

confidence: 99%

Section: Prosail Based Approachmentioning

confidence: 99%

Section: Prosail Based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

202

View full text Add to dashboard Cite

show abstract

“…The fast emergence and development of unmanned aerial platforms and lightweight optical sensors has recently boosted the availability of new techniques for acquiring spectral data [25]. The relatively low operation height of UAVs results in derived products with a small spatial pixel size [26], which are typically interesting for small scale precision agriculture applications [27][28][29]. UAVs have already become common platforms for use in remote sensing [30], but the number of studies that use UAVs to perform multi-angular measurements for investigation of reflectance anisotropy is still small.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

et al. 2016

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Abstract:Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30 • view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450-915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements.

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Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data

Morota

Jarquín

Campbell

et al. 2022

Methods in Molecular Biology

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The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.

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Green area index from an unmanned aerial system over wheat and rapeseed crops

Cited by 160 publications

References 39 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

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data

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