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Items: 11

1.

Estimation of Plant and Canopy Architectural Traits Using the Digital Plant Phenotyping Platform.

Liu S, Martre P, Buis S, Abichou M, Andrieu B, Baret F.

Plant Physiol. 2019 Nov;181(3):881-890. doi: 10.1104/pp.19.00554. Epub 2019 Aug 16.

2.

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery.

Blancon J, Dutartre D, Tixier MH, Weiss M, Comar A, Praud S, Baret F.

Front Plant Sci. 2019 Jun 6;10:685. doi: 10.3389/fpls.2019.00685. eCollection 2019.

3.

What is cost-efficient phenotyping? Optimizing costs for different scenarios.

Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J, Tardieu F.

Plant Sci. 2019 May;282:14-22. doi: 10.1016/j.plantsci.2018.06.015. Epub 2018 Jul 26.

PMID:
31003607
4.

Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping.

Baret F, Madec S, Irfan K, Lopez J, Comar A, Hemmerlé M, Dutartre D, Praud S, Tixier MH.

J Exp Bot. 2018 Apr 27;69(10):2705-2716. doi: 10.1093/jxb/ery071.

5.

Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model.

Jiang J, Comar A, Burger P, Bancal P, Weiss M, Baret F.

Plant Methods. 2018 Mar 20;14:23. doi: 10.1186/s13007-018-0291-x. eCollection 2018.

6.

A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring.

Franch B, Vermote EF, Roger JC, Murphy E, Becker-Reshef I, Justice C, Claverie M, Nagol J, Csiszar I, Meyer D, Baret F, Masuoka E, Wolfe R, Devadiga S.

Remote Sens (Basel). 2017 Mar 21;Volume 9(Iss 3). doi: 10.3390/rs9030296.

7.

Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation.

Inoue Y, Guérif M, Baret F, Skidmore A, Gitelson A, Schlerf M, Darvishzadeh R, Olioso A.

Plant Cell Environ. 2016 Dec;39(12):2609-2623. doi: 10.1111/pce.12815. Epub 2016 Sep 21.

8.

Reply to Ollinger et al.: Remote sensing of leaf nitrogen and emergent ecosystem properties.

Knyazikhin Y, Lewis P, Disney MI, Mõttus M, Rautiainen M, Stenberg P, Kaufmann RK, Marshak A, Schull MA, Carmona PL, Vanderbilt V, Davis AB, Baret F, Jacquemoud S, Lyapustin A, Yang Y, Myneni RB.

Proc Natl Acad Sci U S A. 2013 Jul 2;110(27):E2438. No abstract available.

9.

Reply to Townsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry.

Knyazikhin Y, Lewis P, Disney MI, Stenberg P, Mõttus M, Rautiainen M, Kaufmann RK, Marshak A, Schull MA, Latorre Carmona P, Vanderbilt V, Davis AB, Baret F, Jacquemoud S, Lyapustin A, Yang Y, Myneni RB.

Proc Natl Acad Sci U S A. 2013 Mar 19;110(12):E1075. Epub 2013 Mar 5. No abstract available.

10.

Hyperspectral remote sensing of foliar nitrogen content.

Knyazikhin Y, Schull MA, Stenberg P, Mõttus M, Rautiainen M, Yang Y, Marshak A, Latorre Carmona P, Kaufmann RK, Lewis P, Disney MI, Vanderbilt V, Davis AB, Baret F, Jacquemoud S, Lyapustin A, Myneni RB.

Proc Natl Acad Sci U S A. 2013 Jan 15;110(3):E185-92. doi: 10.1073/pnas.1210196109. Epub 2012 Dec 4.

11.

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots.

Lelong CC, Burger P, Jubelin G, Roux B, Labbé S, Baret F.

Sensors (Basel). 2008 May 26;8(5):3557-3585.

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