A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy

Jay, Sylvain; Bendoula, Ryad; Hadoux, Xavier; Féret, Jean‐Baptiste; Gorretta, Nathalie

doi:10.1016/j.rse.2016.02.029

Cited by 141 publications

(74 citation statements)

References 48 publications

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“…Based on the PROSPECT model version 5b (Feret et al, ), the newly developed close‐range RTM allows for direct and accurate estimations of foliar content from millimetre hyperspectral imagery using numerical inversions. As reported in Jay et al (), the PROCOSINE model can retrieve leaf biochemical parameters with an R 2 of ~0.9 and an RMSE of less than 10% in laboratory conditions. Further technical details of the close‐range RTM can be found in Jay et al ().…”

Section: Analysis Techniquesmentioning

confidence: 55%

“…An RTM, called PROCOSINE and developed to describe and simulate leaf reflectance for close‐range imaging spectroscopy (Jay et al, ) on a per‐pixel basis, was used as the third approach to estimate photosynthetic variables. Based on the PROSPECT model version 5b (Feret et al, ), the newly developed close‐range RTM allows for direct and accurate estimations of foliar content from millimetre hyperspectral imagery using numerical inversions.…”

Section: Analysis Techniquesmentioning

confidence: 99%

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Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

Meacham-Hensold

Guan

et al. 2020

Plant Cell & Environment

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The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here, we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R 2 of~0.8 for predicting V cmax and J max , higher than an R 2 of~0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V cmax (R 2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 μmol m −2 s −1 ) while a similar performance for J max (R 2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 μmol m −2 s −1 ). Further analysis on spectral resampling revealed that V cmax and J max could be predicted with 10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales. K E Y W O R D S earth system models, global carbon cycles, high-throughput mapping, hyperspectral imaging, machine learning, photosynthesis, plant breeding

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Section: Analysis Techniquesmentioning

confidence: 55%

Section: Analysis Techniquesmentioning

confidence: 99%

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

Meacham-Hensold

Guan

et al. 2020

Plant Cell & Environment

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“…This leafclip enables transmittance-based Cab measurements characterized by an initial accuracy of around 5 µg/cm² (Cerovic et al, 2012). As observed in previous studies (Cerovic et al, 2012;Jay et al, 2016), Two methods were used for LAI measurements. In 2015, each subplot was harvested to measure leaf area.…”

Section: Reference Measurementsmentioning

confidence: 99%

“…Ratios of reflectance difference such as the Modified Red-edge Ratio ( ) (Sims and Gamon, 2002) and MERIS Terrestrial Chlorophyll Index ( ) (Dash and Curran, 2004) decrease the two above influences, as such effects can, respectively, be approximated by multiplicative and additive perturbations under the bi-directional hypothesis in the visible domain (Jay et al, 2016). The recently proposed by Jay et al (2017) using millimeter- to centimeter-scale imagery of sugar beet canopies was also included in this study because it has proven to offer a strong sensitivity to Cab when the soil influence is low.…”

Section: Vegetation Index Based Approachmentioning

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

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203

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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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“…The spectral characteristics of leaves are influenced by not only pigment concentration but also by angular optical properties (Ballaré, Sánchez, Scopel, Casal, & Ghersa, 1987;Jay, Bendoula, Hadoux, Féret, & Gorretta, 2016;Roosjen et al, 2018). For example, the ratio of red to near infrared is dependent on the angular reflection of a leaf (Breece & Holmes, 1971).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor

Sun

et al. 2019

Plant Cell & Environment

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Chlorophyll is one of the primary pigments of plant leaves, and changes in its content can be used to characterize the physiological status of plants. Spectral indices have been devised and validated for estimating leaf chlorophyll content (LCC). However, most of the existing spectral indices do not consider the influence of angular reflection on the accuracy of the LCC estimation. In this study, the spectral reflectance factors of leaves from three plant species were measured from several observations in the principal plane. The relationship between the existing spectral indices and the LCC from different directions suggests that the directional reflection of a leaf surface impacts the accuracy of its LCC estimation. Subsequently, the ratio of reflectance differences, that is, the modified Datt index, was tested to reduce the directional reflection effect when predicting LCC. Our results indicated that the modified Datt index not only estimated LCC with high accuracy for all observation directions and plant species but also consistently predicted the LCC of each species in individual observation directions. Our method opens the possibility for optical detection of LCC using multiangular spectral reflection, which is convenient for plant science studies focused on the variation in LCC.

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A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy

Cited by 141 publications

References 48 publications

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

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

Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor

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