Institute Technique Of La Betterave

Paris, France

Institute Technique Of La Betterave

Paris, France
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Jay S.,IRSTEA | Jay S.,Aix - Marseille University | Gorretta N.,IRSTEA | Morel J.,IRSTEA | And 6 more authors.
Remote Sensing of Environment | Year: 2017

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 (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red-edge and near-infrared domains. Overall, mNDblue[728, 850] defined as (R440 − R728)/(R440 + R850) and computed over the brightest green pixels obtains the best correlations with Cab for spatial resolutions finer than 8.8 cm with a root mean square error of prediction better than 2.6 μg/cm2. Conversely, mNDblue[728, 850] poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between Cab and these canopy structure variables. As mNDblue[728, 850] can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve Cab from millimeter- to centimeter-scale reflectance imagery. © 2017 Elsevier Inc.


Jay S.,IRSTEA | Jay S.,Aix - Marseille University | Maupas F.,Institute Technique Of La Betterave | Bendoula R.,IRSTEA | Gorretta N.,IRSTEA
Field Crops Research | Year: 2017

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 (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 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 (RMSE = 17%). As expected 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 UAV-embedded multispectral cameras, with which only a few wavebands are available. © 2017 Elsevier B.V.


Pradel M.,UR TSCF | Pacaud T.,UR TSCF | Cariolle M.,Institute Technique Of La Betterave
Waste and Biomass Valorization | Year: 2013

Organic waste land application generates nitrogenous emissions that have impacts on acidification, eutrophication and global warming. To assess these impacts with Life Cycle Assessment, emission factors are commonly used without taking into account neither the type and performance of land application techniques nor the type of organic waste applied. This paper proposes a methodological framework to assess the nitrogenous emissions by coupling technological performances of spreader and biophysical models, focusing on sewage sludge spreading in different weather and soil conditions. The first step consists of creating several spreading scenarios by combining a cropping system and a "spreader/sewage sludge" couple. The second step consists of testing the technological spreader performances regarding spatial distribution, application rate and soil compaction with a spreading simulator and the COMPSOIL model. Nitrogenous emissions are then simulated with STICS and DEAC models for different application rates and soil bulk densities. Finally, the simulated nitrogen losses from the models are linked with the real amounts of sewage sludge applied and the compacted soil due to spreader performances. Our approach shows that ammonia emissions during sewage sludge spreading can be directly linked to the spreader performances whereas nitrate leaching depends more on the soil and on the weather conditions. Nitrous oxide emissions mostly depend on the spreader weight and to the soil and the weather conditions. This method paves the way to new approaches: integrating technological performances of machines into biophysical and agricultural models in order to assess environmental impacts of agricultural practices. © Springer Science+Business Media B.V. 2012.


Baey C.,École Centrale Paris | Didier A.,Institute Technique Of La Betterave | Lemaire S.,Institute Technique Of La Betterave | Maupas F.,Institute Technique Of La Betterave | Cournede P.-H.,École Centrale Paris
Ecological Modelling | Year: 2013

A wide range of models have been proposed and developed for modelling sugar beet growth, each of them with different degrees of complexity and modelling assumptions. Many of them are used to predict crop production or yield, even when they were not originally designed for this purpose, and even though their predictive capacity has never been properly evaluated. In this study, we propose the evaluation and comparison of five plant growth models that rely on a similar energetic concept for the production of biomass, but with different levels of description (individual-based or per square meter) and different ways to describe biomass repartition (empirical or via allocation): Greenlab, LNAS, CERES, PILOTE and STICS. The models were all programmed on the same modelling platform, calibrated on a first set of data, and then their predictive capacities were assessed on an independent data set. First, a sensitivity analysis was carried out on each model to identify a subset of parameters to be estimated, to reduce the variability of the models. We were able to reduce the number of parameters from 10 to 4 for Greenlab, and from 16 to 1 for STICS. Three criteria were then used to compare the predictive capacities of the models: the root mean squared error of prediction and the modelling efficiency for the total dry matter production and the dry matter of root, and the yield prediction error. All the models provided good overall predictions, with high values of the modelling efficiency. The use of sensitivity analysis allowed us to reduce the variability of the models and to enhance their predictive capacities. Models based on an empirical harvest index gave good yield predictions, and similar results compared to allocation models for the total dry matter, but the harvest index might not be very robust. The crucial role of initiation was also pointed out, as well as the need for an accurate estimation and modelling of this early phase of growth. © 2013 Elsevier B.V. All rights reserved.


Baey C.,École Centrale Paris | Didier A.,Institute Technique Of La Betterave | Lemaire S.,Institute Technique Of La Betterave | Maupas F.,Institute Technique Of La Betterave | Cournede P.-H.,École Centrale Paris
Ecological Modelling | Year: 2013

Modelling the interindividual variability in plant populations is a key issue to enhance the predictive capacity of plant growth models at the field scale. In the case of sugar beet, this variability is well illustrated by rate of leaf appearance, or by its inverse the phyllochron. Indeed, if the mean phyllochron remains stable among seasons, there is a strong variability between individuals, which is not taken into account when using models based only on mean population values. In this paper, we proposed a nonlinear mixed model to assess the variability of the dynamics of leaf appearance in sugar beet crops. As two linear phases can be observed in the development of new leaves, we used a piecewise-linear mixed model. Four parameters were considered: thermal time of initiation, rate of leaf appearance in the first phase, rupture thermal time, and difference in leaf appearance rates between the two phases. The mean population values as well as the interindividual variabilities (IIV) of the parameters were estimated by the model for a standard population of sugar beet, and we showed that the IIV of the four parameters were significant. Also, the rupture thermal time was found to be non significantly correlated to the other three parameters. We compared our piecewise-linear formulation with other formulations such as sigmoïd or Gompertz models, but they provided higher AIC and BIC.A method to assess the effects of environmental factors on model parameters was also studied and applied to the comparison of three levels of Nitrogen (control, standard and high dose). Taking into account the IIV, our model showed that plants receiving Nitrogen tended to have a later time of initiation, a higher rate of leaf appearance, and an earlier rupture time, but these differences were not dose-dependent (no differences between standard and high dose of Nitrogen). No differences were found on the leaf appearance rate of the second phase between the three treatments. © 2013 Elsevier B.V.

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