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.
Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter-to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level. Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various VIs computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected VIs included some classical VIs from the literature as well as optimal combinations of spectral bands, including simple ratio (), modified
a b s t r a c tThis article presents a method for crop row structure characterization that is adapted to phenotypingrelated issues. In the proposed method, a crop row 3D model is built and serves as a basis for retrieving plant structural parameters. This model is computed using Structure from Motion with RGB images acquired by translating a single camera along the row. Then, to estimate plant height and leaf area, plant and background are discriminated by a robust method that uses both color and height information in order to handle low-contrasted regions. The 3D model is scaled and the plant surface is finally approximated using a triangular mesh.The efficacy of our method was assessed with two data sets collected under outdoor conditions. We also evaluated its robustness against various plant structures, sensors, acquisition techniques and lighting conditions. The crop row 3D models were accurate and led to satisfactory height estimation results, since both the average error and reference measurement error were similar. Strong correlations and low errors were also obtained for leaf area estimation. Thanks to its ease of use, estimation accuracy and robustness under outdoor conditions, our method provides an operational tool for phenotyping applications.
International audienceLaboratory Visible-Near Infrared (Vis-NIR) spectroscopy is a good alternative to costly physical and chemical soil analysis to estimate a wide range of soil properties. Various statistical methods relate soil Vis-NIR spectra to soil properties including partial least-squares regression (PLSR), the most common multivariate statistical technique in soil science. Most efforts are generally dedicated to the comparison of methodologies and their optimization for the estimation of soil properties. Instead, the focus of this paper is to assess the prediction of soil properties from laboratory Vis-NIR spectroscopy data in regards to spectral degradation. Consecutively, both spectra quality and PLSR models quality are analyzed across the definition of different spectral configurations, each one characterized by three parameters: the number of spectral bands, the spectral resolution and the spectral sampling interval. The originality of this work is to perform this study on four soil properties with different spectral absorption features due to their various physico-chemical interactions with soil substrate, namely: clay, free iron oxides, calcium carbonate (CaCO3) and pH. The initial database is composed of 1961 spectral bands, spectral resolutions of 3 and 10 nm in the 400'1000 nm and 1000'2500 nm ranges, respectively, with a resampled spectral interval of 1 nm. Seven degraded spectral configurations were built from this reference database with a number of spectral bands decreasing from 328 to 10, a spectral resolution decreasing from 3 nm to 200 nm, and a spectral sampling interval equaling the spectral resolution (i.e., uniform interval sampling). All of these databases were composed of 148 soil samples collected at a Mediterranean site. PLSR predicted the four selected soil properties, and the results were as follows: (1) the prediction performances of the PLSR models were accurate and globally stable with a spectral resolution between 3 and 60 nm regardless of the soil properties (R2 decreased from 0.8 to 0.77 for clay, from 0.88 to 0.84 for CaCO3, from 0.66 to 0.58 for pH and remained constant at 0.78 for iron), (2) the prediction performances decreased, but remained acceptable for clay, iron oxides and CaCO3 at spectral resolutions between 60 and 200 nm (R2 > 0.7), (3) the sensitivity of a given soil property to spectral configurations depended on its spectral features and correlations with other soil properties
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