PROSPECT-D : vers la modélisation des propriétés optiques foliaires durant l'ensemble du cycle de vie

International audienceLeaf pigments provide valuable information about plant physiology. High resolution monitoring of their dynamics will give access to better understanding of processes occurring at different scales, and will be particularly important for ecologists, farmers, and decision makers to assess the influence of climate change on plant functions, and the adaptation of forest, crop, and other plant canopies. In this article, we present a new version of the widely-used PROSPECT model, hereafter named PROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids, the two plant pigments in the current version. We describe the evolution and improvements of PROSPECT-D compared to the previous versions, and perform a validation on various experimental datasets. Our results show that PROSPECT-D outperforms all the previous versions. Model prediction uncertainty is decreased and photosynthetic pigments are better retrieved. This is particularly the case for leaf carotenoids, the estimation of which is particularly challenging. PROSPECT-D is also able to simulate realistic leaf optical properties with minimal error in the visible domain, and similar performances to other versions in the near infrared and shortwave infrared domains.Les pigments foliaires fournissent des informations précieuses sur la physiologie des plantes. Le suivi fin de leur dynamique pour permettre de mieux comprendre les processus qui se produisent à différentes échelles et sera particulièrement important pour les écologues, les agriculteurs et les décideurs pour évaluer l'influence du changement climatique sur les fonctions des plantes et l'adaptation des forêts, des cultures, et de la végétation en général. Dans cet article, nous présentons une nouvelle version du modèle PROSPECT largement utilisé, appelé PROSPECT-D, pour Dynamique, qui ajoute les anthocyanes aux chlorophylles et aux caroténoïdes, les deux pigments végétaux pris en compte jusqu'à la version présente. Nous décrivons l'évolution et les améliorations de PROSPECT-D par rapport aux versions précédentes, et réalisons une validation sur différents jeux de données expérimentales. Nos résultats montrent que PROSPECT-D surpasse toutes les versions précédentes. L'incertitude de prédiction du modèle est diminuée et les pigments photosynthétiques sont mieux récupérés. C'est particulièrement le cas des caroténoïdes foliaires, dont l'estimation est particulièrement difficile. PROSPECT-D est également capable de simuler des propriétés optiques réalistes de la feuille avec une erreur minimale dans le domaine visible et des performances similaires aux précédentes versions dans le domaine infrarouge

Un modèle physique pour l'estimation de la biochimie foliaire et de l'orientation de la feuille à partir d'imagerie hyperspectrale de proxi-détection

International audienceRadiative transfer models have long been used to characterize the foliar content at the leaf and canopy levels. However, they still do not apply well to close-range imaging spectroscopy, especially because directional effects are usually not taken into account. For this purpose, we introduce a physical approach to describe and simulate the variation in leaf reflectance observed at this scale. Two parameters are thus introduced to represent (1) specular reflection at the leaf surface and (2) local leaf orientation. The model, called COSINE (ClOse-range Spectral ImagiNg of lEaves), can be coupled with a directional-hemispherical reflectance model of leaf optical properties to relate the measured reflectance to the foliar content. In this study, we show that, when combining COSINE with the PROSPECT model, the overall PROCOSINE model allows for a robust sub-millimeter retrieval of foliar content based on numerical inversion and pseudo bidirectional reflectance factor hyperspectral measurements.The relevance of the added parameters is first shown through a sensitivity analysis performed in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) ranges. PROCOSINE is then validated based on VNIR and SWIR hyperspectral images of various leaf species exhibiting different surface properties. Introducing these two parameters within the inversion allows us to obtain accurate maps of PROSPECT parameters, e.g., the chlorophyll content in the VNIR range, and the equivalent water thickness and leaf mass per area in the SWIR range. Through the estimation of light incident angle, the PROCOSINE inversion also provides information on leaf orientation, which is a critical parameter in vegetation remote sensing

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Not AvailableThe Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and (iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.Not Availabl

Fusion of hyperspectral imaging and LiDAR for forest monitoring

Effective strategies for forest characterization and monitoring are important to support sustainable management. Recent advances in remote sensing, like hyperspectral and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. Hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits, while LiDAR data offer alternatives for analyzing structural properties of canopy. The fusion of these two data sources can improve forest characterization. The method to use for the data fusion should be chosen according to the variables to predict. This work presents a literature review on the integration of hyperspectral imaging and LiDAR data by considering applications related to forest monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion: low level or observation level, medium level or feature level, and high level or decision level. This review examines the relationship between the three levels of fusion and the methods used in each considered approac

The spectral species concept at wide geographical scales: estimating ecosystem alpha- and beta-diversity by remote sensing

There is an increasing need to rapidly assess the biodiversity of ecosystems, due to the widely acknowledged trend towards biodiversity loss. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. In particular, if an investigation area is large, it is challenging to collect data providing reliable information. Observer bias, restrictions in accessibility, funding and workload limitations, missing expert knowledge, dynamics in emergence and phenology are just some examples that limit the collection of representative in-situ information in Earth observation. Some of these restrictions in Earth observation can be avoided through using remote sensing approaches. Remote sensing is efficient and cost-effective. Modern sensors allow the identification of biodiversity patterns in vegetation over large areas and provide sensitive information on the dynamics of their biodiversity. Obviously, shortcomings are also present such as sub-canopy diversity, limited representation of very steep slope surfaces, very different sizes (diameter) of plant species, similar spectral traits between species, or ecosystems that are constantly covered by clouds (e.g. laurel forest). Different works have estimated biodiversity on the basis of the Spectral Variation Hypothesis; according to this hypothesis, spectral heterogeneity over the different pixels reflects a higher niche heterogeneity, allowing more organisms to coexist. From this assumption, the concept of spectral species has been derived, following the consideration that the spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. At a local scale, with the use of high resolution remote sensing data, the different subspaces can be identified as different 'spectral species'. This has been done using an unsupervised method based on the clustering of the different subsets. From the distribution of these spectral species (and the derivation of alpha- and beta-diversity) is then possible to approximate the diversity of the species living in an area. Our approach derives from this concept and extends it at a wide spatial extent. We have been able to apply this method to MODIS imagery data, producing a map of the distribution of the spectral species over all Europe and an estimate of the European alpha- and beta-diversity. In this case, the diversity is not evaluated at species level, but at community level, introducing the concept of spectral community. We propose to apply the method on a stack of images of the months of the year, in order to cluster niches taking also into account their response to different seasons, and therefore to elaborate multi-temporal and multi-dimensional data