Sylvain Jay

• PhD
• Research engineer at French National Institute for Agriculture, Food, and Environment (INRAE)

The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for re...

We present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently fo...

Accurate estimation of leafchlorophyll content (Cab) from remote sensing is of tremendous significance to mon- itor the physiological status ofvegetation 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 t...

Remote sensing has gained much attention for agronomic applications such as crop management or yield esti- mation. Crop phenotyping under field conditions has recently become another important application that re- quires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype...

Hyperspectral remote sensing is now an established tool to determine shallow water properties over large areas, usually by inverting a semi-analytical model of water reflectance. However, various sources of error may make the observed subsurface remote-sensing reflectance deviate from the model, resulting in an increased retrieval error when invert...

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in the sense that it requires a prior estimation of various parameters of the mixing model, which is co...

Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the...

Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired...

Monitoring of coastal areas by remote sensing is an important issue. The interest of using an unmixing method to determine the seabed composition from hyperspectral aerial images of coastal areas is investigated. Unmixing provides both seabed abundances and endmember reflectances. A sub-surface mixing model is presented, based on a recently propose...

In a very recent paper, we introduced (i) a specific hyper-spectral mixing model for the sea bottom, based on a detailed physical analysis which includes the adjacency effect, and (ii) an associated unmixing method, which is not blind in the sense that it requires a prior estimation of various parameters of that mixing model. We here proceed much f...

Hyperspectral sensors have a limited spatial resolution so that, when observing the Earth, each pixel of a hyperspectral image corresponds to a surface on Earth which is often composed of different pure materials. The radiance or reflectance spectrum of such a pixel is then a mixture of the spectra of the corresponding pure materials. In particular...

Hardware Software Structural traits Leaf traits Dynamics of traits Conclusion High resolution RGB camera + 6000 x 4000 pixels + Pixel size: 3.93 µm + Focal length: 30-60 mm + Weight: 500 g with lens Footprint 4,2mm Footprint 8mm AIRPHEN multispectral camera + 1080 x 960 pixels + 6 configurable bands + Pixel size: 3.04 µm + Focal length: 4.2-8 mm +...

The estimation of the bathymetry and the detection of targets located on the seabed of shallow waters using remote sensing techniques is of great interest for many environmental applications in coastal areas such as benthic habitat mapping, monitoring of seabed aquatic plants and the subsequent management of littoral zones. For that purpose, knowle...

Geological and archaeological analysis of stone masonries in standing structures helps reveal information about use of natural resources. At the same time, the study of historical materials is useful for conservators and cultural heritage management. Geochemical and petrographic analysis of building material types is usually done through destructiv...

Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-i...

The detection of plant diseases, including fungi, is a major challenge for reducing yield gaps of crops across the world. We explored the potential of the PROCOSINE radiative transfer model to assess the effect of the fungus Pseudocercospora fjiensis on leaf tissues using laboratory-acquired submillimetre-scale hyperspectral images in the visible a...

These MATLAB scripts/functions run MILE (MaxImum Likelihood estimation including Environmental noise) and MILEBI (MaxImum Likelihood estimation including Environmental noise and Bottom Intra-class variability) (Jay et al., 2017) in forward and inverse modes. MILE and MILEBI enable one (1) to simulate realistic (i.e., as measured from a remote-sensi...

For further information, please see the following journal paper: Jay, S., Guillaume, M., Chami, M., Minghelli, A., Deville, Y., Lafrance, B., Serfaty, V. (2018). Predicting minimum uncertainties in the inversion of ocean color geophysical parameters based on Cramer-Rao bounds. Optics Express, 26(2), A1-A18.

For further information, please see the following journal paper: S. Jay, N. Gorretta, J. Morel, F. Maupas, R. Bendoula, G. Rabatel, D. Dutartre, A. Comar, F. Baret: Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment, 2017; 198:173-186.

For further information, please refer to the following journal paper: Jay, S., Guillaume, M., Minghelli, A., Deville, Y., Chami, M., Lafrance, B., Serfaty , V. Hyperspectral remote sensing of shallow waters: Considering environmental noise and bottom intra-class variability for modeling and inversion of water reflectance. Remote Sens. Environ. 200,...

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and method...

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and method...

A comprehensive study has been launched in the medieval fortress of Carcassonne involving a cooperation between the universities of Umea and Rennes, and the research institute of IRSTEA of Montpell ...

The archive contains all the matlab codes needed to run PROSPECT+COSINE (in forward and inverse modes), as well as 5 reduced hyperspectral images (matlab format) of various leaves exhibiting different pigment contents and surface properties. For more details about the model and data acquisition, see: S. Jay, R. Bendoula, X. Hadoux, J.-B. Féret, N....

The nitrogen status of crops, using optical remote sensing has been followed for phenotyping applications.

Coastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the targeted parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example, depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. S...

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and method...

For more details, see: S. Jay, R. Bendoula, X. Hadoux, J.-B. Féret, N. Gorretta: ”A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy”. Remote Sensing of Environment, 2016; 177: 220–236. https://www.researchgate.net/publication/296561398_A_physically-based_model_for_retrieving_foli...

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we p...

In order to be independent from light source and atmospheric conditions, radiance values extracted from a remote hyperspectral image have to be converted into reflectance values before data processing. Several methods have been proposed in the literature but they require that the lighting/and or atmospheric conditions to be estimated. In the framew...

Bathymetry and Water column constituent estimation is a challenging task for the study of coastal zones. Although most extensively used methods are based on the pixel wise inversion of semi empirical models, we have recently proposed a Maximum Likelihood (ML) method to retrieve such parameters. One limitation of the method is that the seabed reflec...

This article presents a method for crop row structure characterization that is adapted to phenotyping-related 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...

This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated...

Leaf nitrogen content (LNC) is one of the most important limiting key nutrients in sugar beet crops, so plant nitrogen status has to be carefully monitored throughout the plant life. In this study, close-range hyperspectral imaging was used to infer LNC from reflectance spectra in a non-destructive way and under in-field conditions. First, after ac...

This article presents a novel statistical method for mapping water column properties from hyperspectral remote-sensing data. Usual inversion methods are based on a pixel-by-pixel approach. Therefore, they do not consider the spatial correlation between neighboring pixels, even though such pixels are often affected by the same water column if the sp...

Cette thèse aborde des problématiques d'estimation et de détection supervisée en imagerie hyperspectrale, appliquées ici aux environnements côtiers. Des modèles bathymétriques de réflectance sont utilisés afin de représenter l'influence de la colonne d'eau sur la lumière incidente. Différents paramètres sont dits optiquement actifs et agissent sur...

In this paper, we present various bathymetric filters, based on the well-known matched filter (MF), adaptive MF, and adaptive cosine/coherence estimator detectors, for underwater target detection from hyperspectral remote-sensing data. In the case of unknown water characteristics, we also propose the GLRT-based bathymetric filter, which is a genera...

In this article, we use a well-known reflectance model of a water column for estimating the model parameters (depth and concentrations of different water constituents) with a maximum likelihood approach. Tested on simulated data, the method performs well, especially for depths between a few meters and about 10m, and a SNR greater than 10dB. Moreove...

This paper presents a new way of detecting underwater targets with hyperspectral remote-sensing data. The idea is to use a bathymetric model of subsurface reflectance to correct the spectral distortions due to water crossing. Then we derive the Matched filter (MF) from the Likelihood Ratio Test (LRT) built to decide whether the target is present or...