M
Marie Weiss
Researcher at University of Avignon
Publications - 150
Citations - 12076
Marie Weiss is an academic researcher from University of Avignon. The author has contributed to research in topics: Leaf area index & Computer science. The author has an hindex of 44, co-authored 139 publications receiving 9955 citations. Previous affiliations of Marie Weiss include Lüneburg University & Institut national de la recherche agronomique.
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Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography
TL;DR: It is suggested that the use of a digital camera with high dynamic range has the potential to overcome a number of described technical problems related to indirect LAI estimation.
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Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling
TL;DR: In this paper, the theoretical background of modeling the gap fraction and the leaf inclination distribution is presented and different techniques used to derive leaf area index (LAI) and leaf inclination angle from gap fraction measurements are reviewed.
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LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm
Frédéric Baret,Olivier Hagolle,B. Geiger,P. Bicheron,Bastien Miras,Mireille Huc,Beatrice Berthelot,Fernando Niño,Marie Weiss,Olivier Samain,Jean-Louis Roujean,Marc Leroy +11 more
TL;DR: In this paper, the authors describe the algorithmic principles used to generate LAI, fAPAR and fCover estimates from VEGETATION observations, which are produced globally at 10 days temporal sampling interval under lat-lon projection at 1/112° spatial resolution.
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Remote sensing for agricultural applications: A meta-review
TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
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Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem
B. Combal,Frédéric Baret,Marie Weiss,A Trubuil,D Macé,Agnès Pragnère,Ranga B. Myneni,Yuri Knyazikhin,L.B. Wang +8 more
TL;DR: In this paper, the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process was investigated, and the results showed that the prior information significantly improves the accuracy of the estimation.