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

Abstract

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.

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

“…However, data mining of hyperspectral data is limited by the number of ground‐truth photosynthetic measurements (in particular, the number of samples is less than the dimension of hyperspectral data). Second, spectral reflectance captured by a hyperspectral sensor at the canopy level is more complex and composited by multisource variability, such as those associated with plant geometry and architecture, leaf scattering properties, and background soil (Jay et al, ; Mohd Asaari et al, ). Thus, spurious spectral variations are introduced in the recorded signals, blurring spectral signatures associated with target photosynthetic traits.…”

“…RTMs such as PROSPECT (Jacquemoud & Baret, ) and PROSAIL (Jacquemoud et al, ) have been used to characterize structural and biochemical parameters, for example, leaf area index (LAI), chlorophyll content, and dry matter content (Clevers & Kooistra, ; Darvishzadeh et al, ; Duan et al, ; Si et al, ). Jay et al () showed that the PROSAIL model, evaluated over 14 sugar beet cultivars, could well estimate LAI and chlorophyll content with root mean square error (RMSE) ≤10%. Given the close relationship between leaf characteristics (e.g., leaf pigments, structure, water, and dry mass content) and photosynthetic traits (Ceccato et al, ; Jacquemoud & Baret, ; Lobato et al, ), the reduction of hyperspectral reflectance into several meaningful biophysical parameters through RTMs may thus help identify subtle differences in photosynthetic traits among different cultivars.…”

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

“…At canopy level, the VIs based on VNIR bands could also be used to quantify N accumulation because it is highly related to crop biomass (Zheng et al, 2018a). Moreover, previous studies proved that the estimation of foliar biochemistry was affected by the crop canopy structure and sun-sensor geometry (Jay et al, 2017b). However, most studies still used canopy reflectance data from nadir observations, so that only the spectral information from the top layer of canopy could be considered.…”

Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture

“…For instance, Aparicio et al [12] revealed that the performance of VIs to predict wheat yield varied with the sensor view angle. Jay et al [13] also found that the retrieval accuracy of structural and biochemical sugar beet plant traits from VIs were strongly dependent on sun-view geometry. Danner et al [14] found anisotropic effects of sensor view angle on reflectance were relatively strong for early growth stages of the winter wheat canopy, which also influenced the retrieval of the LAI based on PROSAIL model.…”