E. Gorham, Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecological 570 Applications, pp.182-195, 1991.

Z. Yu, J. Loisel, D. P. Brosseau, D. W. Beilman, and S. J. Hunt, Global peatland dynamics since the Last Glacial Maximum, Geophysical Research Letters, vol.184, issue.13, p.572
DOI : 10.1029/2008GM000822

H. Geophysical-research-letters-rydin and J. Jeglum, The Biology of Peatlands, 2010.

M. Kent and P. Coker, Vegetation Description and Analysis: A Practical Approach, 1992.

K. Schmidt and A. Skidmore, Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of 576 Environment, pp.92-108, 2003.

E. Adam and O. Mutanga, Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.6, pp.612-620, 2009.
DOI : 10.1016/j.isprsjprs.2009.04.004

J. S. Seher and P. Tueller, Color aerial photos for marshland, Photogrammetric Engineering, vol.9, pp.489-499, 1973.

E. Adam, O. Mutanga, and D. Rugege, Multispectral and hyperspectral remote sensing for identification and mapping 581 of wetland vegetation: a review, pp.281-296, 2010.

G. Guyot, Optical properties of vegetation canopies. Applications of Remote Sensing in Agriculture, pp.19-43, 1990.

L. Yuan and L. Zhang, Identification of the spectral characteristics of submerged plant Vallisneria spiralis. Acta Ecologica 584 Sinica, pp.1005-1010, 2006.

E. L. Hestir, S. Khanna, M. E. Andrew, M. J. Santos, J. H. Viers et al., 586 Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem, pp.4034-4047, 2008.

N. Torbick, B. Becker, J. Qi, and D. Lusch, Characterizing field-level hyperspectral measurements for identifying 589 wetland invasive plant species, Invasive Species: Detection, Impact and Control, pp.590-97, 2009.

Y. Hamada, D. A. Stow, L. L. Coulter, J. C. Jafolla, and L. W. Hendricks, Detecting Tamarisk species (Tamarix spp.) in 592 riparian habitats of Southern California using high spatial resolution hyperspectral imagery. Remote Sensing of 593 Environment, pp.237-248, 2007.
DOI : 10.1016/j.rse.2007.01.003

C. Vaiphasa, A. K. Skidmore, W. F. De-boer, and T. Vaiphasa, A hyperspectral band selector for plant species 595 discrimination, ISPRS Journal of Photogrammetry and Remote Sensing, vol.594, issue.62, pp.225-235, 2007.

M. Jia, Y. Zhang, Z. Wang, and K. Song, Mapping the distribution of mangrove species in the Core Zone of Mai 597

K. Prospere, K. Mclaren, and B. Wilson, Plant Species Discrimination in a Tropical Wetland Using In Situ Hyperspectral Data, Remote Sensing, vol.47, issue.9, pp.8494-8523, 2014.
DOI : 10.1007/s11273-009-9169-z

O. Krankina, D. Pflugmacher, M. Friedl, W. Cohen, P. Nelson et al., Meeting the challenge of mapping peatlands with remotely sensed data, Biogeosciences, vol.5, issue.6, pp.1809-1820, 2008.
DOI : 10.5194/bg-5-1809-2008

URL : https://hal.archives-ouvertes.fr/hal-00298006

L. Hubert-moy, B. Clément, M. Lennon, T. Houet, and E. Lefeuvre, Etude de zones humides de fond de vallées à partir 604 d'images hyperspectrales CASI: Application à un bassin versant de la région de Pleine-Fougères

V. Thomas, P. Treitz, D. Jelinski, J. Miller, P. Lafleur et al., Image classification of a northern peatland 607 complex using spectral and plant community data. Remote Sensing of Environment, pp.83-99, 2003.

C. Knoth, B. Klein, T. Prinz, and T. Kleinebecker, Unmanned aerial vehicles as innovative remote sensing platforms for 609 high-resolution infrared imagery to support restoration monitoring in cut-over bogs Applied Vegetation Science, pp.610-626, 2013.

N. Mapper-algorithm-using and . Avhrr, Advances in Space Research, pp.1686-1693, 2014.

E. M. Bahri, D. Haboudane, A. Bannari, F. Bonn, and L. Chillasse, Essai de cartographie des espèces forestières 614 dominantes dans le moyen atlas (Maroc) à l'aide des données Aster. Revue Télédétection, pp.283-301, 2007.
DOI : 10.1522/030009920

M. L. Clark, D. A. Roberts, and D. B. Clark, Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales, Remote Sensing of Environment, vol.96, issue.3-4, pp.375-398, 2005.
DOI : 10.1016/j.rse.2005.03.009

R. L. Lawrence, S. D. Wood, and R. L. Sheley, Mapping invasive plants using hyperspectral imagery and Breiman Cutler 620 classifications (RandomForest) Remote Sensing of Environment, pp.356-362, 2006.
DOI : 10.1016/j.rse.2005.10.014

URL : https://naldc.nal.usda.gov/naldc/download.xhtml?id=37537&content=PDF

M. Dalponte, H. O. Ørka, T. Gobakken, D. Gianelle, and E. Naesset, Tree Species Classification in Boreal Forests With Hyperspectral Data, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.5, pp.2632-2645, 2013.
DOI : 10.1109/TGRS.2012.2216272

P. Pant, V. Heikkinen, I. Korpela, M. Hauta-kasari, and T. Tokola, Logistic regression-based spectral band selection for 626 tree species classification: effects of spatial scale and balance in training samples, IEEE Geoscience and Remote Sensing, vol.627, issue.11, pp.1604-1608, 2014.

M. Pal, Multinomial logistic regression-based feature selection for hyperspectral data, International Journal of Applied Earth Observation and Geoinformation, vol.14, issue.1, pp.214-220, 2012.
DOI : 10.1016/j.jag.2011.09.014

G. P. Asner, Biophysical and Biochemical Sources of Variability in Canopy Reflectance, Remote Sensing of Environment, vol.64, issue.3, pp.234-253, 1998.
DOI : 10.1016/S0034-4257(98)00014-5

A. Savitzky and M. J. Golay, Smoothing and differentiation of data by simplified least squares procedures. Analytical 635 Chemistry, pp.1627-1639, 1964.

H. Feilhauer, G. P. Asner, R. E. Martin, and S. Schmidtlein, Brightness-normalized partial least squares regression for 637 hyperspectral data, Journal of Quantitative Spectroscopy and Radiative Transfer, vol.636, issue.111, pp.1947-1957, 2010.
DOI : 10.1016/j.jqsrt.2010.03.007

F. Tsai and W. Philpot, Derivative analysis of hyperspectral data. Remote Sensing of Environment, pp.41-51, 1998.
DOI : 10.1016/s0034-4257(98)00032-7

L. Serrano, J. Peñuelas, and S. L. Ustin, Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS 640 data: Decomposing biochemical from structural signals. Remote Sensing of Environment, pp.355-364, 2002.

R. N. Clark and T. L. Roush, Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications, Journal of Geophysical Research: Solid Earth, vol.34, issue.B7, p.642
DOI : 10.1016/0019-1035(78)90125-2

R. F. Kokaly and R. N. Clark, Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption 644 features and stepwise multiple linear regression. Remote Sensing of the Environment, pp.267-287, 1999.

O. Mutanga, A. K. Skidmore, and H. Prins, Predicting in situ pasture quality in the Kruger National Park, South Africa, 646 using continuum-removed absorption features. Remote Sensing of Environment, pp.393-408, 2004.

B. Hu, J. Lévesque, and J. P. Ardouin, Vegetation Species Identification Using Hyperspectral Imagery, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium
DOI : 10.1109/IGARSS.2008.4778987

A. Ghiyamat, H. Z. Shafri, G. A. Mahdiraji, A. R. Shariff, and S. Mansor, Hyperspectral discrimination of tree 650 species with different classifications using single-and multiple-endmember, International Journal of Applied Earth, vol.651, issue.23, pp.177-191

C. I. Chang and H. Ren, An experiment-based quantitative and comparative analysis of target detection and image 653 classification algorithms for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.654, pp.38-1044, 2000.

H. Chauhan and B. K. Mohan, Effectiveness of spectral similarity measures to develop precise crop spectra for 656 hyperspectral data analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.83-90, 2014.

G. N. Lance and W. Williams, Computer Programs for Hierarchical Polythetic Classification ("Similarity Analyses"), The Computer Journal, vol.9, issue.1, pp.60-64, 1966.
DOI : 10.1093/comjnl/9.1.60

F. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro et al., The spectral image processing 661 system (SIPS)?interactive visualization and analysis of imaging spectrometer data, Remote Sensing of Environment, vol.662, issue.44, pp.145-163, 1993.

C. I. Chang, An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis, IEEE Transactions on Information Theory, vol.46, issue.5, pp.1927-1932, 2000.
DOI : 10.1109/18.857802

C. I. Chang, H. Ren, C. C. Chang, J. O. Jensen, and F. M. Amico, New hyperspectral discrimination measure for spectral characterization, Optical Engineering, vol.43, issue.8, pp.1777-1786, 2004.
DOI : 10.1117/1.1766301

URL : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA460090

F. Meer and W. Bakker, Cross correlogram spectral matching: application to surface mineralogical mapping by 668 using AVIRIS data from Cuprite, Nevada. Remote Sensing of Environment, pp.371-382, 1997.

J. Farifteh, F. Van-der-meer, and E. Carranza, Similarity measures for spectral discrimination of salt???affected soils, International Journal of Remote Sensing, vol.27, issue.23, pp.5273-5293, 2007.
DOI : 10.1016/j.geoderma.2004.07.007

O. A. De-carvalho-jr and P. Meneses, Spectral correlation mapper (SCM): an improvement on the spectral angle 672 mapper (SAM) Summaries of the Ninth JPL Airborne Earth Science Workshop; Jet Propulsion Laboratory, National 673 Aeronautics and Space Administration An analysis of spectral metrics for hyperspectral image processing, p.675, 2000.

F. Boochs, G. Kupfer, K. Dockter, and W. Kühbauch, Shape of the red edge as vitality indicator for plants. Remote 680 Sensing, pp.1741-1753, 1990.

P. Nagler, C. Daughtry, and S. Goward, Plant litter and soil reflectance. Remote Sensing of Environment, pp.207-215, 2000.
DOI : 10.1016/s0034-4257(99)00082-6

URL : https://naldc.nal.usda.gov/naldc/download.xhtml?id=12953&content=PDF

M. S. Kim, C. S. Daughtry, E. Chappelle, J. Mcmurtrey, and C. L. Walthall, The use of high spectral resolution bands for 683 estimating absorbed photosynthetically active radiation (A par), Proceedings of 6th International Symposium on 684

P. J. Zarco-tejada, J. Pushnik, S. Dobrowski, and S. Ustin, Steady-state chlorophyll a fluorescence detection from canopy 686 derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, pp.283-294, 2003.
DOI : 10.1016/s0034-4257(02)00113-x

D. A. Sims, H. Luo, S. Hastings, W. C. Oechel, A. F. Rahman et al., Parallel adjustments in vegetation 688 greenness and ecosystem CO 2 exchange in response to drought in a Southern California chaparral ecosystem, pp.289-303, 2006.

G. A. Carter and R. L. Miller, Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands, Remote Sensing of Environment, vol.50, issue.3, p.696
DOI : 10.1016/0034-4257(94)90079-5

A. A. Gitelson, Y. Gritz, and M. N. Merzlyak, Relationships between leaf chlorophyll content and spectral reflectance 698 and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, vol.699, pp.160-271, 2003.
DOI : 10.1078/0176-1617-00887

A. A. Gitelson, G. P. Keydan, and M. N. Merzlyak, Three-band model for noninvasive estimation of chlorophyll, 701 carotenoids, and anthocyanin contents in higher plant leaves Datt, B. A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus 703 leaves, L11402. 702 61, pp.30-36, 1999.

P. Chen, D. Haboudane, N. Tremblay, J. Wang, P. Vigneault et al., New spectral indicator assessing the efficiency 710 of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, pp.1987-1997, 2010.

G. Le-maire, C. François, and E. Dufrêne, Towards universal broad leaf chlorophyll indices using PROSPECT simulated 712 database and hyperspectral reflectance measurements. Remote Sensing of Environment, pp.1-28, 2004.

I. Filella and J. Peñuelas, The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status., International Journal of Remote Sensing, vol.27, issue.7, pp.1459-1470, 1994.
DOI : 10.1017/CBO9780511752308.006

A. Huete, H. Liu, K. Batchily, and W. Van-leeuwen, A comparison of vegetation indices over a global set of TM images 719 for EOS-MODIS. Remote Sensing of Environment, pp.440-451, 1997.

J. Peñuelas, J. Gamon, A. Fredeen, J. Merino, and C. Field, Reflectance indices associated with physiological changes in 721 nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment, pp.135-146, 1994.

B. Pinty and M. Verstraete, GEMI: a non-linear index to monitor global vegetation from satellites, Vegetatio, vol.91, issue.1, pp.723-101, 1992.
DOI : 10.1007/BF00031911

R. Smith, J. Adams, D. Stephens, and P. Hick, Forecasting wheat yield in a Mediterranean-type environment from the 725 NOAA satellite, Crop and Pasture Science, vol.724, issue.46, pp.113-125, 1995.

A. A. Gitelson, C. Buschmann, and H. K. Lichtenthaler, The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants, Remote Sensing of Environment, vol.69, issue.3, pp.296-302, 1999.
DOI : 10.1016/S0034-4257(99)00023-1

A. A. Gitelson and M. N. Merzlyak, Remote estimation of chlorophyll content in higher plant leaves, International Journal of Remote Sensing, vol.18, issue.12, pp.2691-2697, 1997.
DOI : 10.1080/014311697217558

A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, Use of a green channel in remote sensing of global vegetation from 731 EOS-MODIS. Remote Sensing of Environment, pp.289-298, 1996.

A. Maccioni, G. Agati, and P. Mazzinghi, New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra, Journal of Photochemistry and Photobiology B: Biology, vol.61, issue.1-2, pp.52-61, 2001.
DOI : 10.1016/S1011-1344(01)00145-2

A. A. Gitelson, O. B. Chivkunova, and M. N. Merzlyak, Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves, American Journal of Botany, vol.96, issue.10, pp.1861-1868, 2009.
DOI : 10.3732/ajb.0800395

C. Daughtry, C. Walthall, M. Kim, E. B. De-colstoun, and J. Mcmurtrey, Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance, Remote Sensing of Environment, vol.74, issue.2, pp.229-239, 2000.
DOI : 10.1016/S0034-4257(00)00113-9

URL : https://naldc.nal.usda.gov/naldc/download.xhtml?id=26536&content=PDF

C. Wu, Z. Niu, Q. Tang, and W. Huang, Estimating chlorophyll content from hyperspectral vegetation indices: Modeling 741 and validation. Agricultural and Forest Meteorology, pp.1230-1241, 2008.
DOI : 10.1016/j.agrformet.2008.03.005

D. Haboudane, J. R. Miller, N. Tremblay, P. J. Zarco-tejada, and L. Dextraze, Integrated narrow-band vegetation indices 743 for prediction of crop chlorophyll content for application to precision agriculture, Remote Sensing of Environment, vol.744, issue.81, pp.416-426, 2002.

J. Eitel, D. Long, P. Gessler, and A. Smith, satellite series for prediction of wheat nitrogen status, International Journal of Remote Sensing, vol.25, issue.18, pp.4183-4190, 2007.
DOI : 10.2134/agronj2005.0253

D. A. Sims and J. A. Gamon, Relationships between leaf pigment content and spectral reflectance across a wide range of 748 species, leaf structures and developmental stages, Remote Sensing of Environment, vol.747, issue.81, pp.337-354, 2002.

J. Qi, A. Chehbouni, A. Huete, Y. Kerr, and S. Sorooshian, A modified soil adjusted vegetation index. Remote Sensing of 750 Environment, pp.119-126, 1994.
DOI : 10.1016/0034-4257(94)90134-1

URL : https://naldc.nal.usda.gov/naldc/download.xhtml?id=50306&content=PDF

E. R. Hunt and B. N. Rock, Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote 752 Sensing of Environment, pp.43-54, 1989.

J. M. Chen, Evaluation of vegetation indices and a modified simple ratio for boreal applications, Canadian Journal, p.754

J. Dash and P. Curran, The MERIS terrestrial chlorophyll index, International Journal of Remote Sensing, vol.25, issue.23, pp.5403-5413, 2004.
DOI : 10.1080/0143116042000274015

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-tejada, and I. B. Strachan, Hyperspectral vegetation indices and novel 757 algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, p.758

M. A. Hardisky, V. Klemas, and R. M. Smart, The influence of soil salinity, growth form, and leaf moisture on the spectral 760 radiance of Spartina alterniflora canopies, Photogrammetric Engineering and Remote Sensing, vol.49, pp.77-83, 1983.

C. J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of 762 Environment, pp.127-150, 1979.
DOI : 10.1016/0034-4257(79)90013-0

S. Gandia, G. Fernández, J. García, and J. Moreno, Retrieval of vegetation biophysical variables from CHRIS/PROBA 764 data in the SPARC campaign, Proceedings of the 2nd ESA CHRIS/Proba Workshop, p.765

P. Hansen and J. Schjoerring, Reflectance measurement of canopy biomass and nitrogen status in wheat crops using 767 normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, pp.86-542, 2003.

B. C. Gao, NDWI???A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, vol.58, issue.3
DOI : 10.1016/S0034-4257(96)00067-3

K. Uto and Y. Kosugi, Hyperspectral Manipulation for the Water Stress Evaluation of Plants, Contemporary Materials, vol.1, issue.3, pp.18-25
DOI : 10.7251/COM1201018U

J. Peñuelas, J. A. Gamon, K. L. Griffin, and C. B. Field, Assessing community type, plant biomass, pigment composition, 774 and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment, pp.775-821, 1993.

D. J. Walvoort and J. De-baardemaaker, A linear model to predict with a multi-spectral radiometer the 777 amount of nitrogen in winter wheat, International Journal of Remote Sensing, vol.776, issue.27, pp.4159-4179, 2006.

G. Rondeaux, M. Steven, and F. Baret, Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 779, pp.95-107, 1996.
DOI : 10.1016/0034-4257(95)00186-7

J. Gamon, J. Peñuelas, and C. B. Field, A narrow-waveband spectral index that tracks diurnal changes in photosynthetic 781 efficiency. Remote Sensing of environment, pp.35-44, 1992.
DOI : 10.1016/0034-4257(92)90059-s

J. L. Roujean and F. M. Breon, Estimating PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing of Environment, vol.51, issue.3
DOI : 10.1016/0034-4257(94)00114-3

D. Horler, M. Dockray, and J. Barber, The red edge of plant leaf reflectance, International Journal of Remote Sensing, vol.14, issue.2, pp.273-288, 1983.
DOI : 10.1109/TGRS.1983.350530

D. Horler, M. Dockray, J. Barber, and A. Barringer, Red edge measurements for remotely sensing plant chlorophyll content, Advances in Space Research, vol.3, issue.2, pp.273-277, 1983.
DOI : 10.1016/0273-1177(83)90130-8

A. A. Gitelson, A. Vina, V. Ciganda, D. C. Rundquist, and T. J. Arkebauer, Remote estimation of canopy chlorophyll content in crops, Geophysical Research Letters, vol.96, issue.5, p.8403, 2005.
DOI : 10.1016/S0176-1617(96)80285-9

M. A. Cho and A. K. Skidmore, A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method, Remote Sensing of Environment, vol.101, issue.2, pp.181-193, 2006.
DOI : 10.1016/j.rse.2005.12.011

G. Guyot and F. Baret, Utilisation de la haute résolution spectrale pour suivre l'état des couverts végétaux. Signatures 793 spectrales d'objets en télédétection. 4 ème Colloque international.; Agence Spatiale Européenne, pp.794-279, 1988.

Y. Zhu, X. Yao, Y. Tian, X. Liu, and W. Cao, Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice, International Journal of Applied Earth Observation and Geoinformation, vol.10, issue.1, pp.1-10, 2008.
DOI : 10.1016/j.jag.2007.02.006

L. Xue, W. Cao, W. Luo, T. Dai, and Y. Zhu, Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance, Agronomy Journal, vol.96, issue.1, pp.135-142, 2004.
DOI : 10.2134/agronj2004.0135

J. Peñuelas, I. Filella, P. Lloret, F. Muñoz, and M. Vilajeliu, Reflectance assessment of mite effects on apple trees, International Journal of Remote Sensing, vol.800, issue.16, pp.2727-2733, 1995.

M. Vincini, E. Frazzi, and P. D-'alessio, Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba 802 data, Proceedings of the 4th ESA CHRIS, 2006.

C. Jordan, Derivation of Leaf-Area Index from Quality of Light on the Forest Floor, Ecology, vol.50, issue.4, pp.663-666, 1969.
DOI : 10.2307/1936256

J. Mcmurtrey, E. Chappelle, M. Kim, J. Meisinger, and L. Corp, Distinguishing nitrogen fertilization levels in field 805 corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of 806 Environment, pp.36-44, 1994.

E. W. Chappelle, M. S. Kim, and J. Mcmurtrey, Ratio analysis of reflectance spectra (RARS): an algorithm for the remote 808 estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of 809 Environment, pp.239-247, 1992.

P. J. Zarco-tejada and J. Miller, Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery, Journal of Geophysical Research: Atmospheres, vol.14, issue.D22, pp.27921-27933, 1984.
DOI : 10.1080/01431169308953986

H. Lichtenthaler, M. Lang, M. Sowinska, F. Heisel, and J. Miehe, Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System, Journal of Plant Physiology, vol.148, issue.5, pp.599-612, 1996.
DOI : 10.1016/S0176-1617(96)80081-2

URL : https://hal.archives-ouvertes.fr/in2p3-00015980

C. D. Elvidge and Z. Chen, Comparison of broad-band and narrow-band red and near-infrared vegetation indices, Remote Sensing of Environment, vol.54, issue.1
DOI : 10.1016/0034-4257(95)00132-K

N. H. Broge and E. Leblanc, Comparing prediction power and stability of broadband and hyperspectral vegetation 817 indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sensing of Environment, vol.818, pp.76-156, 2001.

J. Vogelmann, B. Rock, and D. Moss, Red edge spectral measurements from sugar maple leaves, International Journal of Remote Sensing, vol.1298, issue.8
DOI : 10.1016/0034-4257(87)90075-7

R. Pu, L. Foschi, and P. Gong, ) leaves, International Journal of Remote Sensing, vol.22, issue.20, pp.4267-4286, 2004.
DOI : 10.1007/BF03182831

J. Peñuelas, J. Pinol, and R. Ogaya, Estimation of plant water concentration by the reflectance Water Index WI (R900/R970), International Journal of Remote Sensing, vol.18, issue.13, pp.2869-2875, 1997.
DOI : 10.1080/014311697217396

R. A. Fisher, Statistical methods for research workers, 1925.

H. B. Mann and D. R. Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, The Annals of Mathematical Statistics, vol.18, issue.1
DOI : 10.1214/aoms/1177730491

W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American Statistical, vol.829

J. Jensen, Introductory digital image processing: A remote sensing perspective, Geocarto International, vol.2, issue.1, p.831, 1996.
DOI : 10.1080/10106048709354084

H. W. Gausman, Visible light reflectance, transmittance, and absorptance of differently pigmented cotton leaves, Remote Sensing of Environment, vol.13, issue.3
DOI : 10.1016/0034-4257(83)90041-X

J. Clevers, The use of imaging spectrometry for agricultural applications, ISPRS Journal of Photogrammetry and Remote Sensing, vol.54, issue.5-6, pp.299-304, 1999.
DOI : 10.1016/S0924-2716(99)00033-7

O. Mutanga and A. K. Skidmore, Red edge shift and biochemical content in grass canopies. ISPRS Journal of 837 Photogrammetry and Remote Sensing, pp.34-42, 2007.
DOI : 10.1016/j.isprsjprs.2007.02.001

J. Woolley, Reflectance and Transmittance of Light by Leaves, PLANT PHYSIOLOGY, vol.47, issue.5, pp.656-662, 1971.
DOI : 10.1104/pp.47.5.656

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC396745/pdf

M. Boyer, J. Miller, M. Belanger, E. Hare, and J. Wu, Senescence and spectral reflectance in leaves of northern pin oak 840 (Quercus palustris Muenchh.). Remote Sensing of Environment, pp.71-87, 1988.

T. Fourty, F. Baret, S. Jacquemoud, G. Schmuck, and J. Verdebout, Leaf optical properties with explicit description of its 842 biochemical composition: direct and inverse problems. Remote sensing of Environment, pp.104-117, 1996.

V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher et al., Scikit-learn: Machine 845 Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.

L. Breiman, Random forests. Machine learning, pp.5-32, 2001.

M. Belgiu, Dr? agu¸tagu¸t, L. Random forest in remote sensing: A review of applications and future directions, ISPRS, vol.848

V. N. Vapnik, Statistical learning theory, 1998.

G. C. Cawley and N. L. Talbot, Gene selection in cancer classification using sparse logistic regression with Bayesian regularization, Bioinformatics, vol.22, issue.19, pp.2348-2355, 2006.
DOI : 10.1093/bioinformatics/btl386

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.5282

J. Dumont, T. Hirvonen, V. Heikkinen, M. Mistretta, L. Granlund et al., Porali, I.; Hiltunen, 853 J.; Keski-Saari, S.; others. Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening, pp.118-124

S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: a basic tool of chemometrics, Chemometrics and Intelligent Laboratory Systems, vol.58, issue.2
DOI : 10.1016/S0169-7439(01)00155-1

M. Barker and W. Rayens, Partial least squares for discrimination, Journal of Chemometrics, vol.10, issue.3, pp.166-173, 2003.
DOI : 10.1002/0471725293

R. Castillo, M. Otto, J. Freer, and S. Valenzuela, Multivariate strategies for classification of Eucalyptus globulus genotypes 859 using carbohydrates content and NIR spectra for evaluation of their cold resistance, Journal of Chemometrics, vol.860, pp.22-268, 2008.

L. Cao, K. A. Boitard, S. Besse, and P. , Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems, BMC Bioinformatics, vol.12, issue.1, p.253, 2011.
DOI : 10.1111/j.1541-0420.2008.01017.x

URL : https://hal.archives-ouvertes.fr/hal-00959981

K. Y. Peerbhay, O. Mutanga, and R. Ismail, Commercial tree species discrimination using airborne AISA Eagle 864 hyperspectral imagery and partial least squares discriminant analysis, PLS-DA) in KwaZulu?Natal, South Africa. 865 ISPRS Journal of Photogrammetry and Remote Sensing 2013, pp.19-28
DOI : 10.1016/j.isprsjprs.2013.01.013

B. Apolloni, W. Pedrycz, S. Bassis, and D. Malchiodi, The Puzzle of Granular Computing, Studies in Computational, vol.867

E. Szmidt, Distances and Similarities in Intuitionistic Fuzzy Sets, Studies in Fuzziness and Soft Computing, 2013.
DOI : 10.1007/978-3-319-01640-5

C. C. Aggarwal, A. Hinneburg, and D. A. Keim, On the surprising behavior of distance metrics in high dimensional 871 space, International Conference on Database Theory, pp.420-434, 2001.