Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture

Grassland Science

Volumn 57, Issue 2, 2011, Pages 81-93

  Kawamura, Kensuke ^a   Watanabe, Nariyasu ^b   Sakanoue, Seiichi ^b   Lee, Hyo Jin ^a   Inoue, Yoshio ^c  

^a HIROSHIMA UNIVERSITY   (Japan)

^b NATIONAL AGRICULTURAL RESEARCH CENTER FOR HOKKAIDO REGION   (Japan)

^c NATIONAL INSTITUTE FOR AGRO ENVIRONMENTAL SCIENCES   (Japan)

Author keywords

Bootstrap procedure; Fertilizer application; Hyperspectral remote sensing; Pasture quality; White clover

Indexed keywords

BOOTSTRAPPING; CANOPY REFLECTANCE; FERTILIZER APPLICATION; FORAGE; LEGUME; MULTIPLE REGRESSION; NUMERICAL MODEL; PASTURE; REMOTE SENSING;

HOKKAIDO; JAPAN;

TRIFOLIUM REPENS;

EID: 79958261809     PISSN: 17446961     EISSN: 1744697X     Source Type: Journal    
DOI: 10.1111/j.1744-697X.2011.00212.x     Document Type: Article

References (57)

1. AOAC (1995) Official methods of analysis, 16th edn. In: AOAC International (Ed Cunniff P), AOAC International, Arlington, VA.
2. Ben-Dor E, Inbar Y, Chen Y (1997) Reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500nm) during a controlled decomposition process. Remote Sens Environ 61: 1-15.
3. Berardo N (1997) Prediction of the chemical composition of white clover by near-infrared reflectance spectroscopy. Grass Forage Sci 52: 27-32.
4. Blackburn AG (1998) Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens Environ 66: 273-285.
5. Bolster KL, Martin ME, Aber JD (1996) Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: a comparison of statistical methods. Can J Forest Res 26: 590-600.
6. Cho MA, Skidmore A, Corsi F, van Wieren SE, Sobhan I (2007) Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int J Appl Earth Obs Geoinf 9: 414-424.
7. Clark DH, Lamb RC (1991) Near infrared reflectance spectroscopy: a survey of wavelength selection to determine dry matter digestibility. J Dairy Sci 74: 2200-2205.
8. Clevers JGPW (1999) The use of imaging spectrometry for agricultural applications. ISPRS J Photogramm Remote Sens 54: 299-304.
9. Colwell JE (1974) Vegetation canopy reflectance. Remote Sens Environ 3: 175-183.
10. Cooper CS, Hyder DN, Petersen RG, Sneva FA (1957) The constituent differential method of estimating species composition in mixed hay. Agron J 49: 190-193.
11. Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30: 271-278.
12. Curran PJ, Dungan JL, Peterson DL (2001) Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sens Environ 76: 349-359.
13. Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C (2008) Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens Environ 112: 2592-2604.
14. Dawson TP, Curran PJ (1998) A new technique for interpolating the reflectance red edge position. Int J Remote Sens 19: 2133-2139.
15. Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap. Chapman & HAll/CRC, London.
16. 2 fixation, N transfer and N recovery. Plant Soil 221: 281-299.
17. Elvidge DC (1990) Visible and near infrared reflectance characteristics of dry plant materials. Int J Remote Sens 11: 1775-1795.
18. Ferwerda JG, Skidmore AK (2007) Can nutrient status of four woody plant species be predicted using field spectrometry? ISPRS J Photogramm Remote Sens 62: 406-414.
19. Ferwerda JG, Skidmore AK, Mutanga O (2005) Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species. Int J Remote Sens 26: 4083-4095.
20. Ferwerda JG, Skidmore AK, Stein A (2006) A bootstrap procedure to select hyperspectral wavebands related to tannin content. Int J Remote Sens 27: 1413-1424.
21. Gao B-C, Goetzt AFH (1995) Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ 52: 155-162.
22. García-Ciudad A, García-Criado B, Pérez-Corona ME, Vázquez DE, Aldana BR, Ruano-Ramos MA (1993) Application of near-infrared reflectance spectroscopy to chemical analysis of heterogeneous and botanically complex grassland samples. J Sci Food Agric 63: 419-426.
23. Gitelson AA, Merziyak NN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148: 494-500.
24. Gitelson A, Merzlyak MN (1994) 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 143: 286-292.
25. Gitelson AA, Kaufman YJ, Merzlyak NM (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58: 289-298.
26. Goering HK, Van Soest PJ (1970) Forage fiber analyses: apparatus, reagents, procedures, and some applications. In: Agricultural Handbook No. 379 (Ed ARS, USDA), US Government Print Office, Washington DC, 1-20.
27. Haboudane D, Miller RJ, Tremblay N, Zarco-Tejada JP, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81: 416-426.
28. Hokkaido Government Department of Agriculture (2004) Guidelines for fertilizer application in Hokkaido. Hokkaido Government Department of Agriculture, available from: [cited 13 March 2008].
29. Horler HND, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4: 273-288.
30. Inoue Y, Peñuelas J (2006) Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content. Int J Remote Sens 27: 5109-5114.
31. Johnson KD, Lechtenberg VL, Vorst JJ, Hendrix KS (1982) Indirect estimation of grass-legume rations in mixed swards. Agron J 74: 1089-1091.
32. Kawamura K, Watanabe N, Sakanoue S, Inoue Y (2008) Estimating forage biomass and quality in a mixed sown pasture based on PLS regression with waveband selection. Grassl Sci 54: 131-146.
33. Kawamura K, Betteridge K, Sanches ID, Tuohy MP, Costall D, Inoue Y (2009) Field radiometer with canopy pasture probe as a potential tool to estimate and map pasture biomass and mineral components: a case study in the Lake Taupo catchment, New Zealand. NZ J Agric Res 52: 417-434.
34. Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67: 267-287.
35. Lamb WD, Steyn-Ross M, Schaare P, Hanna MM, Silvester W, Steyn-Ross A (2002) Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations. Int J Remote Sens 23: 3619-3648.
36. Lescano CE, Thomas D (1988) Forage quality and animal selection of Arachis pintoi in association with tropical grasses in the eastern plains of Colombia. Grass Forage Sci 43: 433-439.
37. Locher F, Heuwinkel H, Gutser R, Schmidhalter U (2005) Development of near Infrared reflectance spectroscopy calibrations to estimate legume content of multispecies legume-grass mixtures. Agron J 97: 11-17.
38. Lorber A, Wangen LE, Kowalski BR (1987) A theoretical foundation for the PLS algorithm. J Chemom 1: 19-31.
39. Mannetje L, Haydock KP (1963) The dry-weight-rank method for the botanical analysis of pasture. Grass Forage Sci 18: 268-275.
40. Marten GC (1964) Visual estimation of botanical composition in simple legume-grass mixtures. Agron J 56: 549-552.
41. Moore KJ, Roberts CA, Fritz JO (1990) Indirect estimation of botanical composition of alfalfa-smooth bromegrass mixture. Agron J 82: 287-290.
42. Mutanga O, Skidmore AK, Prins HHT (2004) Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens Environ 89: 393-408.
43. Mutanga O, Skidmore AK, Kumar L, Ferwerda J (2005) Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. Int J Remote Sens 26: 1093-1108.
44. Norris KH, Barnes RF, Moore JE, Shenk JS (1976) Predicting forage quality by infrared reflectance spectroscopy. J Anim Sci 43: 889-897.
45. Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens Environ 48: 135-146.
46. Post CJ, Degloria SD, Cherney JH, Mikhailova EA (2007) Spectral measurements of alfalfa/grass fields related to forage properties and species composition. J Plant Nutr 30: 1779-1789.
47. Schils MLR, Vellinga VT, Kraak T (1999) Dry-matter yield and herbage quality of a perennial ryegrass/white clover sward in a rotational grazing and cutting system. Grass Forage Sci 54: 19-29.
48. Schut AGT, Van Der Heijden GWAM, Hoving I, Stienezen MWJ, Van Evert FK, Meuleman J (2006) Imaging spectroscopy for on-farm measurement of grassland yield and quality. Agron J 98: 1318-1325.
49. Shibayama M (2003) Prediction of the ratio of legumes in a mixed seeding pasture by polarized reflected light. Grassl Sci 49: 229-237.
50. Sims AD, Gamon AJ (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81: 337-354.
51. Soukupova J, Rock BN, Albrechtova J (2002) Spectral characteristics of lignin and soluble phenolics in the near infrared - a comparative study. Int J Remote Sens 23: 3039-3055.
52. Starks JP, Zhao D, Phillips AW, Coleman SW (2006) Herbage mass, nutritive value and canopy spectral reflectance of bermudagrass pastures. Grass Forage Sci 61: 101-111.
53. Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer B (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91: 354-376.
54. Twidwell EK, Johnson KD, Cherney JH (1986) Indirect estimation of wheat-hairy vetch ratios in a mixed stand. Agron J 78: 344-346.
55. Vankeuren RW, Ahlgren HL (1957) A statistical study of several methods used in determining the botanical composition of a sward: I. a study of established pastures. Agron J 49: 532-536.
56. Watanabe N, Umemura K, Sudo K (2004) Combination use of bite counter and GPS for monitoring foraging behaviour in grazing cow. Grassl Sci 50 (Ext): 404-405. (In Japanese.)
57. Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW (2007) Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassl Sci 53: 39-49.