Towards a universal hyperspectral index to assess chlorophyll content in deciduous forests

Remote Sensing

Volumn 9, Issue 3, 2017, Pages

  Sonobe, Rei ^a   Wang, Quan ^a  

^a SHIZUOKA UNIVERSITY   (Japan)

Author keywords

Chlorophyll concentration; Deciduous species; Hyperspectral remote sensing; PROpri t s SPECTrales Version 5 (PROSPECT 5); Ratio of performance to deviation (RPD)

Indexed keywords

FORESTRY; MEAN SQUARE ERROR; PLANTS (BOTANY); REFLECTION; REMOTE SENSING;

CHLOROPHYLL CONCENTRATION; DECIDUOUS SPECIES; FIRST ORDER DERIVATIVES; HYPERSPECTRAL REMOTE SENSING; NORMALIZED DIFFERENCES; RATIO OF PERFORMANCE TO DEVIATION (RPD); REFLECTANCE PROPERTIES; ROOT MEAN SQUARE ERRORS;

CHLOROPHYLL;

EID: 85019391242     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9030191     Document Type: Article

References (62)

1. Korus, A. Effect of preliminary and technological treatments on the content of chlorophylls and carotenoids in kale (Brassica oleracea L. Var. Acephala). J. Food Process. Preserv. 2013, 37, 335-344.
2. Datt, B. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Int. J. Remote Sens. 1999, 20, 2741-2759.
3. Atzberger, C.; Guerif, M.; Baret, F.; Werner, W. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Comput. Electron. Agric. 2010, 73, 165-173.
4. Mattos, E.R.; Singh, M.; Cabrera, M.L.; Das, K.C. Enhancement of biomass production in scenedesmus bijuga high-density culture using weakly absorbed green light. Biomass Bioenergy 2015, 81, 473-478.
5. Zarco-Tejada, P.J.; Miller, J.R.; Noland, T.L.; Mohammed, G.H.; Sampson, P.H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1491-1507.
6. Collins,W. Remote sensing of crop type and maturity. Photogramm. Eng. Remote Sens. 1978, 44, 43-55.
7. Filella, I.; Amaro, T.; Araus, J.L.; Penuelas, J. Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI). Physiol. Plant. 1996, 96, 211-216
8. Horler, D.N.H.; Dockray, M.; Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 1983, 4, 273-288.
9. Miller, J.R.; Hare, E.W.; Wu, J. Quantitative characterisation of the red edge reflectance 1. An inverted-gaussian model. Int. J. Remote Sens. 1990, 11, 1755-1773.
10. Elvidge, C.D.; Chen, Z.K. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens. Environ. 1995, 54, 38-48.
11. Filella, I.; Serrano, L.; Serra, J.; Penuelas, J. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 1995, 35, 1400-1405.
12. Sonobe, R.; Wang, Q. Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests. Ecol. Inform. 2017, 37, 1-9.
13. Hosgood, B.; Jacquemoud, S.; Andreoli, G.; Verdebout, J.; Pedrini, G.; Schmuck, G. Leaf Optical Properties Experiment 93 (Lopex93); European Commission-Joint Research Centre: Ispra, Italy, 1994; p. 20.
14. Féret, J.-B.; Francois, C.; Asner, G.P.; Gitelson, A.A.; Martin, R.E.; Bidel, L.P.R.; Ustin, S.L.; le Maire, G.; Jacquemoud, S. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ. 2008, 112, 3030-3043.
15. Marshall, M.; Thenkabail, P. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM plus, and MODIS vegetation indices in crop biomass estimation. ISPRS J. Photogramm. Remote Sens. 2015, 108, 205-218.
16. Yamamoto, H.; Kouyama, T.; Obata, K.; Tsuchida, S. Assessment of hisui radiometric performance using vicarious calibration and cross-calibration. In Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 26-31 July 2015; pp. 2805-2808.
17. Yamamoto, H.; Nakamura, R.; Tsuchida, S. Radiometric Calibration Plan for the Hyperspectral Imager Suite (HISUI) Instruments. In Proceedings of the Conference on Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, Kyoto, Japan, 30-31 October 2012.
18. Williams, P. Variables affecting near-infraredreflectance spectroscopic analysis. In Near-Infrared Technology in the agricultural and Food Industries;Williams, P., Norris, K., Eds.; American Association of Cereal Chemists Inc.: St. Paul, MN, USA, 1987; pp. 143-167.
19. Minasny, B.; McBratney, A. Why you don't need to use RPD. Pedometron 2013, 33, 14-15.
20. Watanabe, S. Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 2010, 11, 3571-3594.
21. Japan Meteorological Agency. Available online: http://www.data.jma.go.jp/ (accessed on 23 January 2017).
22. Gamon, J.A.; Rahman, A.F.; Dungan, J.L.; Schildhauer, M.; Huemmrich, K.F. Spectral Network (SpecNet)-What is it and why do we need it? Remote Sens. Environ. 2006, 103, 227-235.
23. Wang, Q.; Iio, A.; Tenhunen, J.; Kakubari, Y. Annual and seasonal variations in photosynthetic capacity of Fagus crenata along an elevation gradient in the Naeba Mountains, Japan. Tree Physiol. 2008, 28, 277-285.
24. Iwasaki, T.; Aoki, K.; Seo, A.; Murakami, N. Comparative phylogeography of four component species of deciduous broad-leaved forests in japan based on chloroplast dna variation. J. Plant Res. 2012, 125, 207-221.
25. Takahara, H.; Takeoka, M. Vegetation history since the last glacial period in the Mikata lowland, the Sea of Japan area, western Japan. Ecol. Res. 1992, 7, 371-386.
26. Foley, S.; Rivard, B.; Sanchez-Azofeifa, G.A.; Calvo, J. Foliar spectral properties following leaf clipping and implications for handling techniques. Remote Sens. Environ. 2006, 103, 265-275.
27. Richardson, A.D.; Berlyn, G.P. Changes in foliar spectral reflectance and chlorophyll fluorescence of four temperate species following branch cutting. Tree Physiol. 2002, 22, 499-506.
28. Arnon, D.I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in beta vulgaris. Plant Physiol. 1949, 24, 1-15.
29. Pavan, G.; Jacquemoud, S.; Bidel, L.; Francois, C.; de Rosny, G.; Rambaut, J.P.; Frangi, J.P. Ramis: A new portable field radiometer to estimate leaf biochemical content. In Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management, Minneapolis, MN, USA, 25-28 July 2004; p. 14.
30. Boochs, F.; Kupfer, G.; Dockter, K.; Kuhbauch, W. Shape of the red edge as vitality indicator for plants. Int. J. Remote Sens. 1990, 11, 1741-1753.
31. Carter, G.A.; Knapp, A.K. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 2001, 88, 677-684.
32. Gamon, J.A.; Surfus, J.S. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 1999, 143, 105-117.
33. Tanaka, S.; Kawamura, K.; Maki, M.; Muramoto, Y.; Yoshida, K.; Akiyama, T. Spectral Index for Quantifying Leaf Area Index ofWinter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan. Remote Sens. 2015, 7, 5329-5346.
34. Sonobe, R.; Wang, Q. Assessing the xanthophyll cycle in natural beech leaves with hyperspectral reflectance. Funct. Plant Biol. 2016, 43, 438-447.
35. Blackburn, G.A. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. Int. J. Remote Sens. 1998, 19, 657-675.
36. 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.
37. Carter, G.A. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int. J. Remote Sens. 1994, 15, 697-703.
38. 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.
39. Datt, B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sens. Environ. 1998, 66, 111-121.
40. Datt, B. A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using eucalyptus leaves. J. Plant Physiol. 1999, 154, 30-36.
41. Gitelson, A.A.; Merzlyak, M.N. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. J. Plant Physiol. 1996, 148, 494-500.
42. Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969, 50, 663-666.
43. Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Miehe, J.A. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 1996, 148, 599-612.
44. 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.
45. Penuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Save, R. The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14, 1887-1905.
46. Smith, R.C.G.; Adams, J.; Stephens, D.J.; Hick, P.T. Forecasting wheat yield in a mediterranean-type environment from the noaa satellite. Aust. J. Agric. Res. 1995, 46, 113-125.
47. Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563-1575.
48. Zarco-Tejada, P.J.; Miller, J.R.; Haboudane, D.; Tremblay, N.; Apostol, S. Detection of chlorophyll fluorescence in vegetation from airborne hyperspectral casi imagery in the red edge spectral region. In Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 21-25 July 2003; pp. 598-600.
49. 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.
50. Gandia, S.; Fernández, G.; Moreno, J. Retrieval of vegetation biophysical variables from chris/proba data in the sparc campaign. In Proceedings of the 2nd CHRIS/ProbaWorkshop, Frascati, Italy, 28-30 April 2004; ESA/ESRIN: Frascati, Italy; pp. 40-48.
51. Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286-292.
52. le Maire, G.; Francois, C.; Soudani, K.; Berveiller, D.; Pontailler, J.Y.; Breda, N.; Genet, H.; Davi, H.; Dufrene, E. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sens. Environ. 2008, 112, 3846-3864.
53. Wang, Q.; Li, P. Identification of robust hyperspectral indices on forest leaf water content using prospect simulated dataset and field reflectance measurements. Hydrol. Process. 2012, 26, 1230-1241.
54. 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.
55. Gitelson, A.A.; Zur, Y.; Chivkunova, O.B.; Merzlyak, M.N. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 2002, 75, 272-281.
56. Chang, C.W.; Laird, D.A.; Mausbach, M.J.; Hurburgh, C.R. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 2001, 65, 480-490.
57. Scott, D. Multivariate Density Estimation: Theory, Practice, and Visualization; Wiley: New York, NY, USA, 1992.
58. Lawley, V.; Lewis, M.; Clarke, K.; Ostendorf, B. Site-based and remote sensing methods for monitoring indicators of vegetation condition: An australian review. Ecol. Indic. 2016, 60,1273-1283.
59. Inoue, Y.; Sakaiya, E.; Zhu, Y.; Takahashi,W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens. Environ. 2012, 126, 210-221.
60. Sanches, I.D.A.; Souza Filho, C.R.; Kokaly, R.F. Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal. ISPRS J. Photogramm. Remote Sens. 2014, 97, 111-122.
61. 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.
62. 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.