Feasibility of unmanned aerial vehicle optical imagery for early detection and severity assessment of late blight in Potato

Remote Sensing

Volumn 11, Issue 3, 2019, Pages

  Franceschini, Marston Héracles Domingues ^a   Bartholomeus, Harm ^a   van Apeldoorn, Dirk Frederik ^a   Suomalainen, Juha ^b   Kooistra, Lammert ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

Author keywords

Crop monitoring; Disease assessment; Hyperspectral sensing; Very high resolution imagery

Indexed keywords

AIRCRAFT DETECTION; ANTENNAS; CROPS; ECONOMIC AND SOCIAL EFFECTS; IMAGE RESOLUTION; INFRARED DEVICES; PLANTS (BOTANY); SPECTROSCOPY; UNMANNED AERIAL VEHICLES (UAV);

AGGREGATED INFORMATION; CROP MONITORING; HYPERSPECTRAL SENSING; LOG LIKELIHOOD RATIOS (LLR); PHYTOPHTHORA INFESTANS; SIMPLEX VOLUME MAXIMIZATIONS; VERY HIGH RESOLUTION; VISIBLE AND NEAR INFRARED;

DISEASES;

EID: 85061357547     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs11030224     Document Type: Article

References (114)

1. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371
2. Kuska, M.T.; Mahlein, A.-K. Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. Eur. J. Plant Pathol. 2018, 152, 987-992
3. Aasen, H.; Bolten, A. Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers-From theory to application. Remote Sens. Environ. 2018, 205, 374-389
4. Capolupo, A.; Kooistra, L.; Berendonk, C.; Boccia, L.; Suomalainen, J. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Inf. 2015, 4, 2792-2820
5. Roosjen, P.P.J.; Brede, B.; Suomalainen, J.M.; Bartholomeus, H.M.; Kooistra, L.; Clevers, J.G.P.W. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data-potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Obs. Geoinf. 2018, 66, 14-26
6. Bock, C.H.; Poole, G.H.; Parker, P.E.; Gottwald, T.R. Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. Crit. Rev. Plant Sci. 2010, 29, 59-107
7. Kuska, M.T.; Brugger, A.; Thomas, S.;Wahabzada, M.; Kersting, K.; Oerke, E.-C.; Steiner, U.; Mahlein, A.-K. Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei. Phytopathology 2017, 107, 1388-1398
8. Wahabzada, M.; Mahlein, A.-K.; Bauckhage, C.; Steiner, U.; Oerke, E.-C.; Kersting, K. Metro Maps of Plant Disease Dynamics-Automated Mining of Differences Using Hyperspectral Images. PLoS ONE 2015, 10, e0116902
9. Wahabzada, M.; Mahlein, A.-K.; Bauckhage, C.; Steiner, U.; Oerke, E.-C.; Kersting, K. Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. Sci. Rep. 2016, 6, srep22482
10. Mahlein, A.-K.; Rumpf, T.; Welke, P.; Dehne, H.-W.; Plümer, L.; Steiner, U.; Oerke, E.-C. Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ. 2013, 128, 21-30
11. Thomas, S.; Behmann, J.; Steier, A.; Kraska, T.; Muller, O.; Rascher, U.; Mahlein, A.-K. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 2018, 14, 45
12. Behmann, J.; Acebron, K.; Emin, D.; Bennertz, S.; Matsubara, S.; Thomas, S.; Bohnenkamp, D.; Kuska, M.T.; Jussila, J.; Salo, H.; et al. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors 2018, 18, 441
13. Garcia-Ruiz, F.; Sankaran, S.; Maja, J.M.; Lee, W.S.; Rasmussen, J.; Ehsani, R. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput. Electron. Agric. 2013, 91, 106-115
14. Calderón, R.; Navas-Cortés, J.; Zarco-Tejada, P. Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sens. 2015, 7, 5584-5610
15. Thomas, S.; Kuska, M.T.; Bohnenkamp, D.; Brugger, A.; Alisaac, E.; Wahabzada, M.; Behmann, J.; Mahlein, A.-K. Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. J. Plant Dis. Prot. 2017, 125, 5-20
16. Behmann, J.; Steinrücken, J.; Plümer, L. Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote Sens. 2014, 93, 98-111
17. Albetis, J.; Duthoit, S.; Guttler, F.; Jacquin, A.; Goulard, M.; Poilvé, H.; Féret, J.-B.; Dedieu, G. Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens. 2017, 9, 308
18. Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 2013, 139, 231-245
19. Li, X.; Lee, W.S.; Li, M.; Ehsani, R.; Mishra, A.R.; Yang, C.; Mangan, R.L. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Comput. Electron. Agric. 2012, 83, 32-46
20. López-López, M.; Calderón, R.; González-Dugo, V.; Zarco-Tejada, P.J.; Fereres, E. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sens. 2016, 8, 276
21. MacDonald, S.L.; Staid, M.; Staid, M.; Cooper, M.L. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards. Comput. Electron. Agric. 2016, 130, 109-117
22. Zarco-Tejada, P.J.; Camino, C.; Beck, P.S.A.; Calderon, R.; Hornero, A.; Hernández-Clemente, R.; Kattenborn, T.; Montes-Borrego, M.; Susca, L.; Morelli, M.; et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 2018, 4, 432-439
23. Huang, W.; Lamb, D.W.; Niu, Z.; Zhang, Y.; Liu, L.; Wang, J. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis. Agric. 2007, 8, 187-197
24. Mewes, T.; Franke, J.; Menz, G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precis. Agric. 2011, 12, 795-812
25. Calderón, R.; Montes-Borrego, M.; Landa, B.B.; Navas-Cortés, J.A.; Zarco-Tejada, P.J. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precis. Agric. 2014, 15, 639-661
26. Sankaran, S.; Maja, J.; Buchanon, S.; Ehsani, R. Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques. Sensors 2013, 13, 2117-2130
27. Jansen, M.; Bergsträsser, S.; Schmittgen, S.; Müller-Linow, M.; Rascher, U. Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding. Agriculture 2014, 4, 147-158
28. Polder, G.; van der Heijden, G.W.A.M.; van Doorn, J.; Baltissen, T.A.H.M.C. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosyst. Eng. 2014, 117, 35-42
29. Whetton, R.L.; Hassall, K.L.;Waine, T.W.; Mouazen, A.M. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement. Biosyst. Eng. 2018, 166, 101-115
30. Moshou, D.; Bravo, C.; Oberti, R.; West, J.S.; Ramon, H.; Vougioukas, S.; Bochtis, D. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosyst. Eng. 2011, 108, 311-321
31. Schaepman-Strub, G.; Schaepman, M.E.; Painter, T.H.; Dangel, S.; Martonchik, J.V. Reflectance quantities in optical remote sensing-Definitions and case studies. Remote Sens. Environ. 2006, 103, 27-42
32. Herrmann, I.; Vosberg, S.K.; Ravindran, P.; Singh, A.; Chang, H.-X.; Chilvers, M.I.; Conley, S.P.; Townsend, P.A. Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean. Remote Sens. 2018, 10, 426
33. Zhang, J.; Pu, R.; Huang, W.; Yuan, L.; Luo, J.;Wang, J. Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crop. Res. 2012, 134, 165-174
34. Yu, K.; Leufen, G.; Hunsche, M.; Noga, G.; Chen, X.; Bareth, G. Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices. Remote Sens. 2013, 6, 64-86
35. Yang, C.-M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precis. Agric. 2009, 11, 61-81
36. Rumpf, T.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Dehne, H.-W.; Plümer, L. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput. Electron. Agric. 2010, 74, 91-99
37. Cooke, L.R.; Schepers, H.T.A.M.; Hermansen, A.; Bain, R.A.; Bradshaw, N.J.; Ritchie, F.; Shaw, D.S.; Evenhuis, A.; Kessel, G.J.T.; Wander, J.G.N.; et al. Epidemiology and Integrated Control of Potato Late Blight in Europe. Potato Res. 2011, 54, 183-222
38. Sugiura, R.; Tsuda, S.; Tamiya, S.; Itoh, A.; Nishiwaki, K.; Murakami, N.; Shibuya, Y.; Hirafuji, M.; Nuske, S. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst. Eng. 2016, 148, 1-10
39. Duarte-Carvajalino, J.; Alzate, D.; Ramirez, A.; Santa-Sepulveda, J.; Fajardo-Rojas, A.; Soto-Suárez, M. Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms. Remote Sens. 2018, 10, 1513
40. Nebiker, S.; Lack, N.; Abächerli, M.; Läderach, S. Light-weight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, XLI-B1, 963-970
41. Whitehead, K.; Hugenholtz, C.H.; Myshak, S.; Brown, O.; LeClair, A.; Tamminga, A.; Barchyn, T.E.; Moorman, B.; Eaton, B. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications. J. Unmanned Veh. Sys. 2014, 2, 86-102
42. Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production. Sensors 2017, 17, 1428
43. Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Assessing Changes in Potato Canopy Caused by Late Blight in Organic Production Systems Through Uav-Based Pushbroom Imaging Spectrometer. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 109-112
44. Römer, C.; Wahabzada, M.; Ballvora, A.; Pinto, F.; Rossini, M.; Panigada, C.; Behmann, J.; Léon, J.; Thurau, C.; Bauckhage, C.; et al. Early drought stress detection in cereals: Simplex volume maximisation for hyperspectral image analysis. Funct. Plant Biol. 2012, 39, 878
45. European and Mediterranean Plant Protection Organization Phytophthora infestans on potato. EPPO Bull. 2008, 38, 268-271
46. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens. 2013, 5, 5006-5039
47. Clevers, J.G.P.W.; Kooistra, L. Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 574-583
48. Harwin, S.; Lucieer, A.; Osborn, J. The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis. Remote Sens. 2015, 7, 11933-11953
49. Honkavaara, E.; Rosnell, T.; Oliveira, R.; Tommaselli, A. Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes. ISPRS J. Photogramm. Remote Sens. 2017, 134, 96-109
50. Meier, U. Growth stages of mono-and dicotyledonous plants: BBCH Monograph. OpenAgrar Repository 2018
51. Zhang, Z. A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 1330-1334
52. Lowekamp, B.C.; Chen, D.T.; Ibanez, L.; Blezek, D. The Design of SimpleITK. Front. Neuroinform. 2013, 7, 45
53. Vakalopoulou, M.; Karantzalos, K. Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data. Remote Sens. 2014, 6, 3409-3426
54. Mattes, D.; Haynor, D.R.; Vesselle, H.; Lewellen, T.K.; Eubank, W. Nonrigid multimodality image registration. Med. Imaging 2001, 1609-1620
55. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 2004, 60, 91-110
56. Muja, M.; Lowe, D.G. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 2009, 2, 331-340
57. Tibshirani, R.; Walther, G.; Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 2001, 63, 411-423
58. Zou, H.; Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 2005, 67, 301-320
59. Jay, S.; Gorretta, N.; Morel, J.; Maupas, F.; Bendoula, R.; Rabatel, G.; Dutartre, D.; Comar, A.; Baret, F. Estimating leaf chlorophyll content in sugar beet canopies using millimeter-to centimeter-scale reflectance imagery. Remote Sens. Environ. 2017, 198, 173-186
60. Wood, C.W.; Reeves, D.W.; Duffield, R.R.; Edmisten, K.L. Field chlorophyll measurements for evaluation of corn nitrogen status. J. Plant Nutr. 1992, 15, 487-500
61. Uddling, J.; Gelang-Alfredsson, J.; Piikki, K.; Pleijel, H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res. 2007, 91, 37-46
62. Clevers, J.G.P.W. Beyond NDVI: Extraction of Biophysical Variables from Remote Sensing Imagery. In Land Use and Land Cover Mapping in Europe; Manakos, I., Braun, M., Eds.; Springer: Dordrecht, The Netherlands, 2014; Volume 18, pp. 363-381. ISBN 978-94-007-7968-6
63. Yu, K.; Anderegg, J.; Mikaberidze, A.; Karisto, P.; Mascher, F.; McDonald, B.A.; Walter, A.; Hund, A. Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease. Front. Plant Sci. 2018, 9, 1195
64. Delalieux, S.; Auwerkerken, A.; Verstraeten, W.W.; Somers, B.; Valcke, R.; Lhermitte, S.; Keulemans, J.; Coppin, P. Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves. Remote Sens. 2009, 1, 858-874
65. Galecki, A.; Burzykowski, T. Linear Mixed-Effects Models Using R; Springer Texts in Statistics; Springer: New York, NY, USA, 2013; ISBN 978-1-4614-3899-1
66. Lenth, R. Emmeans: Estimated marginal means, aka least-squares means. R Package, v. 1, n. 2, 2018
67. Pinheiro, J.; Bates, D.; Debroy, S.; Sakar, D. The R Core Team. nlme: Linear and Nonlinear Mixed Effects Models; R Core Team: Vienna, Austria, 2016
68. Thurau, C.; Kersting, K.; Bauckhage, C.; Augustin, F.I.S. Yes We Can-Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization. In Proceedings of the Conference on Information and Knowledge Management, Toronto, ON, Canada, 26-30 October 2010
69. Thurau, C.; Kersting, K.;Wahabzada, M.; Bauckhage, C. Descriptive matrix factorization for sustainability Adopting the principle of opposites. Data Min. Knowl. Discov. 2012, 24, 325-354
70. Kersting, K.; Bauckhage, C.;Wahabzada, M.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Römer, C.; Plümer, L. Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. In Computational Sustainability; Lässig, J., Kersting, K., Morik, K., Eds.; Springer International Publishing: Cham, Switzerland, 2016; Volume 645, pp. 99-120. ISBN 978-3-319-31856-1
71. Alfeld, M.; Wahabzada, M.; Bauckhage, C.; Kersting, K.; van der Snickt, G.; Noble, P.; Janssens, K.; Wellenreuther, G.; Falkenberg, G. Simplex Volume Maximization (SiVM): A matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data. Microchem. J. 2017, 132, 179-184
72. Ghamisi, P.; Plaza, J.; Chen, Y.; Li, J.; Plaza, A.J. Advanced Spectral Classifiers for Hyperspectral Images: A review. IEEE Geosci. Remote Sens. Mag. 2017, 5, 8-32
73. Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image processing system (SIPS)-Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145-163
74. Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra. Appl. Spectrosc. 1989, 43, 772-777
75. Mohd Asaari, M.S.; Mishra, P.; Mertens, S.; Dhondt, S.; Inzé, D.; Wuyts, N.; Scheunders, P. Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a highthroughput phenotyping platform. ISPRS J. Photogramm. Remote Sens. 2018, 138, 121-138
76. Naidu, R.A.; Perry, E.M.; Pierce, F.J.; Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 2009, 66, 38-45
77. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56-S66
78. Mahlein, A.-K.; Steiner, U.; Hillnhütter, C.; Dehne, H.-W.; Oerke, E.-C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 2012, 8, 3
79. Kong, W.; Huang, W.; Zhou, X.; Ye, H.; Dong, Y.; Casa, R. Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies. Sensors 2017, 17, 2711
80. Gitelson, A.A.; Keydan, G.P.; Merzlyak, M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett. 2006, 33, L11402
81. Gitelson, A.A.; Merzlyak, M.N.; Chivkunova, O.B. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochem. Photobiol. 2001, 74, 38-45
82. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271-282
83. Vincini, M.; Frazzi, E.; D'Alessio, P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis. Agric. 2008, 9, 303-319
84. Jordan, C.F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology 1969, 50, 663-666
85. Le Maire, G.; François, C.; Dufrêne, E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 2004, 89, 1-28
86. Zarco-Tejada, P.J.; Berjón, A.; López-Lozano, R.; Miller, J.R.; Martín, P.; Cachorro, V.; González, M.R.; de Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99, 271-287
87. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289-298
88. Gandia, S.; Fernández, G.; Garcia, J.C.; Moreno, J. Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaign. In Proceedings of the 2nd CHRIS/Proba Workshop, Frascati, Italy, 28-30 April 2004; Volume 578, pp. 40-48
89. Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Miehé, J.A. Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System. J. Plant Physiol. 1996, 148, 599-612
90. Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; De Colstoun, E.B.; McMurtrey, J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 2000, 74, 229-239
91. Wu, C.; Niu, Z.; Tang, Q.; Huang, W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol. 2008, 148, 1230-1241
92. Haboudane, D. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352
93. Maccioni, A.; Agati, G.; Mazzinghi, P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J. Photochem. Photobiol. B Biol. 2001, 61, 52-61
94. Chen, J.M. Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications. Can. J. Remote Sens. 1996, 22, 229-242
95. Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403-5413
96. Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.;Waller, P.; Choi, C.; Riley, E.; Thompson, T. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 16-19 July 2000; Volume 1619
97. Rouse, J.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of the NASA Goddard Space Flight Center Third ERTS-1 Symposium, Washington, DC, USA, 10-14 December 1973; NASA: Greenbelt, MD, USA, 1974
98. Gitelson, A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B Biol. 1994, 22, 247-252
99. Gong, P.; Pu, R.; Biging, G.S.; Larrieu, M.R. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1355-1362
100. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107
101. Gamon, J.A. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112, 492-501
102. Blackburn, G.A. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches. Remote Sens. Environ. 1998, 66, 273-285
103. Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 1999, 106, 135-141
104. Chappelle, E.W.; Kim, M.S.; McMurtrey, J.E. Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sens. Environ. 1992, 39, 239-247
105. Roujean, J.-L.; Breon, F.-M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384
106. Guyot, G.; Baret, F. Utilisation de la haute resolution spectrale pour suivre l'etat des couverts vegetaux. In Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France, 18-22 January 1988; ESA: Aussois, France, 1988; Volume 287, pp. 279-286
107. Peñuelas, J.; Baret, F.; Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 1995, 31, 221-230
108. Gitelson, A.A.; Merzlyak, M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18, 2691-2697
109. McMurtrey, J.E.; Chappelle, E.W.; Kim, M.S.; Meisinger, J.J.; Corp, L.A. Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sens. Environ. 1994, 47, 36-44
110. Zarco-Tejada, P.J.; Pushnik, J.C.; Dobrowski, S.; Ustin, S.L. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sens. Environ. 2003, 84, 283-294
111. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426
112. Haboudane, D.; Tremblay, N.; Miller, J.R.; Vigneault, P. Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived from Hyperspectral Data. IEEE Trans. Geosci. Remote Sens. 2008, 46, 423-437
113. Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2001, 76, 156-172
114. Clevers, J. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens. Environ. 1988, 25, 53-69