Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing

Field Crops Research

Volumn 122, Issue 3, 2011, Pages 214-224

  Ryu, Chanseok ^a   Suguri, Masahiko ^a   Umeda, Mikio ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

Airborne hyperspectral remote sensing; Heading stage; Multiple linear regression (MLR); Nitrogen content; Partial least square regression (PLSR)

Indexed keywords

AIRBORNE SENSING; DRY MATTER; MULTIVARIATE ANALYSIS; NITROGEN; PREDICTION; RADIATION BALANCE; REGRESSION ANALYSIS; RICE; SPECTRAL REFLECTANCE;

EID: 79956085996     PISSN: 03784290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fcr.2011.03.013     Document Type: Article

References (59)

1. Angus J.F., Sudjadi M., Fazekas de st.Groth C., Mulyani N.S., Hadiwahyono D.M., Wetselaar R. Simulating nitrogen response of irrigated rice. Plant Soil 1990, 219-229.
2. Blackburn G.A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 1998, 66(3):273-285.
3. Blackmore S. Precision farming: an introduction. Outlook Agric. 1994, 23(4):275-280.
4. Casanova D., Epema G.F., Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Res. 1998, 55:83-92.
5. Casanova D., Goudriaan J., Bosch A.D. Testing the performance of ORYZA1, an explanatory model for rice growth simulation, for Mediterranean conditions. Eur. J. Agron. 2000, 12:175-189.
6. Chang K.W., Shen Y., Lo J.C. Predicting rice yield using canopy reflectance measured at booting stage. Agron. J. 2005, 97:872-878.
7. Chappelle E.W., McMurtery III, Kim M.S. Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis. Remote Sens. Environ. 1991, 36:213-218.
8. Chen Q., Huang J.F., Wang X.Z., Wang R.C. In situ hyperspectral data analysis for pigment content estimation of rice leaves. J. Zhejiang Univ. Sci. 2003, 4(6):727-733.
9. Curran P.J., Dungan J.L., Macler B.A., Plummer S.E. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sens. Environ. 1991, 35:69-76.
10. Curran P.J. Remote sensing of foliar chemistry. Remote Sens. Environ. 1989, 30:271-278.
11. Demetriades-shah T.H., Steven M.D., Clark J.A. High resolution derivative spectra in remote sensing. Remote Sens. Environ. 1990, 33:55-64.
12. Evri M., Akiyama T., Kawamura K. Spectrum analysis of hyperspectral red edge position to predict rice biophysical parameters and grain weight. Jpn. Soc. Photogramm. Remote Sen. 2008, 47(2):4-15.
13. Green R.O., Eastwood M.L., Sarture C.M., Chrien T.G., Aronsson M., Chippendale B.J., et al. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 1998, 65:227-248.
14. Hache C., Shibusawa S., Sasao A. Discriminating conventional and conservation agricultural management practices with airborne multispectral imagery. Agric. Ecosyst. Environ. 2005, 111:354-366.
15. Inamura T., Hamada H., Iida K., Umeda M. Correlation of the amount of nitrogen accumulated in the aboveground biomass at panicle initiation and nitrogen content of soil with the nitrogen uptake by lowland rice during the period from panicle initiation to heading. Plant Prod. Sci. 2003, 6:302-308.
16. Inoue E., Mihara Y., Tsuboi Y. Agro-meteorological studies on rice growth in Japan. Agric. Meteor. 1963, 2:85-107.
17. Inoue Y. Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production. Plant Prod. Sci. 2003, 6:3-16.
18. Inoue Y., Kurosu T., Maeno H., Uratsuka S., Kozu T., Dabrowska-Zielinska K., et al. Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables. Remote Sens. Environ. 2002, 81:194-204.
19. Inoue Y., Penuelas J. An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. Int. J. Remote Sens. 2001, 22(18):3883-3888.
20. 2 flux measurements in rice. Remote Sens. Environ. 2008, 112:156-172.
21. Islam M.S., Morison J.I.L. Influence of solar radiation and temperature on irrigated rice grain yield in Bangladesh. Field Crops Res. 1992, 30:13-28.
22. Japan Meteorological Agency, 2007. The past meteorological data. (accessed 02.03.07). http://www.data.jma.go.jp/obd/stats/etrn/index.php.
23. Kimura R., Okada S., Miura H., Kamichika M. Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rife field. Agric. Water Manage. 2004, 69:83-100.
24. Lee Y.J., Yang C.M., Chang K.W., Shen Y. A simple spectral index using reflectance of 735nm to assess nitrogen status of rice canopy. Agron. J. 2008, 100:205-212.
25. Lewis M., Jooste V., Gasparis A.A. Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery. IEEE Trans. Geosci. Remote Sen. 2001, 39(7):1471-1479.
26. Lichtenthaler H.K. Remote sensing of chlorophyll fluorescence in oceanography and in terrestrial vegetation: an introduction. Applications of Chlorophyll Fluorescence 1988, 287-297. Kluwer, Dordrecht. H.K. Lichtenthaler (Ed.).
27. Liu Z.Y., Huang J.F., Tao R.X. Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci. 2008, 15(3):232-242.
28. Micol R., Michele M., Mirco M., Giovanni M., Sergio C., Lorenzo B., et al. High resolution field spectroscopy measurements for estimating gross ecosystem production in a rice field. Agric. Forest Meteor. 2010, 150:1283-1296.
29. Moya I., Guyot G., Goulas Y. Remotely sensed blue and red fluorescence emission for monitoring vegetation. ISPRS J. Photogramm. Remote Sens. 1992, 47:205-231.
30. Nakano H., Morita S. Effects of time of first harvest, total amount of nitrogen, and nitrogen application method on total matter yield in twice harvesting of rice. Field Crops Res. 2008, 105:40-47.
31. Nguyen H.T., Lee B.W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron. 2006, 24:349-356.
32. Ntanos D.A., Koutroubas S.D. Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Res. 2002, 74:93-101.
33. Okamoto H., Murata T., Kataoka T., Hata S. Plant classification for weed detection using hyperspectral imaging with wavelet analysis. Weed Boil. Manage. 2007, 7:31-37.
34. Park B., Windham W.R., Lawrence K.C., Smith D.P. Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosyst. Eng. 2007, 96:323-333.
35. Rock B.N., Hoshizaki T., Miller J.R. Comparison of in situ and airborne spectral measurement of the blue shift associated with forest decline. Remote Sens. Environ. 1988, 24:109-127.
36. Ryu C.S., Suguri M., Iida M., Umeda M. Investigation into possibility of methane fermentation digested sludge as liquid fertilizer for paddy field. J. Eng. Agric. Environ. Food 2010, 3(1):32-37.
37. Ryu C.S., Suguri M., Nishiike Y., Umeda M. Making nitrogen contents model using hyperspectral remote sensing and estimation nitrogen contents by nitrogen content model. J. Jpn. Soc. Agric. Mach. 2005, 67(6):47-54. (in Japanese, with English abstract).
38. Ryu C.S., Suguri M., Umeda M. Estimation and comparison of nitrogen contents model of rice plant using remote sensing. J. Jpn. Soc. Agric. Mach. 2007, 69(3):43-50. (in Japanese, with English abstract).
39. Ryu C.S., Suguri M., Umeda M. A model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing. Biosyst. Eng. 2009, 104:465-475.
40. Ryu, C.S., Suguri, M., Umeda, M., Lee, C.K. Integrating remote sensing and GIS for prediction of rice protein contents. Prec. Agric., doi:10.1007/s11119-010r-r9179-0.
41. Seager S., Turner E.L., Schafer J., Ford E.B. Vegetation's red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology 2005, 5:372-390.
42. Shibayama M., Sakamoto T., Inoue A., Morita K., Takahashi W., Kimura A. Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital camera. Plant Prod. Sci. 2009, 12(3):293-306.
43. Stroppiana D., Boschetti M., Brivio P.A., Bocchi S. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Res. 2009, 111:119-129.
44. Ta T., Ohira K. Effects of temperature and light intensity on the uptake and assimilation of 15N-labeled ammonium and nitrate in Indica and Japonica rice plants. Soil Sci. Plant Nutr. 1982, 28(1):79-90.
45. Takahashi W., Nguyen-Cong V., Kawaguchi S., Minamiyama M., Ninomiya S. Statistical models for prediction of dry weight and nitrogen accumulation based on visible and near-infrared hyper-spectral reflectance of rice canopies. Plant Prod. Sci. 2000, 3(4):377-386.
46. Thenkabail P.S., Smith R.B., De Pauw E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 2000, 71(2):158-182.
47. Tian Y., Zhu Y., Cao W. Monitoring leaf photosynthesis with canopy spectral reflectance in rice. Photosynthetica 2005, 43(4):481-489.
48. Urata T., Nishigaki S. Selection of rice variety Hinohikari with good eating quality and its cultivative characteristics. Bull. Osaka Agric. Forest Res. Ctr. 2001, 37:56-57.
49. Volkan Bilgili A., Van Es H.M., Akbas F., Durak A., Hively W.D. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. J. Arid Environ. 2010, 74:229-238.
50. Wang F.M., Huang J.F., Wand X.Z. Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol. 2007, 50(3):291-299.
51. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometircs. Chemom. Intell. Lab. Syst. 2001, 58:109-130.
52. Yanai J., Lee C.K., Umeda M., Kosaki T. Spatial variability of soil chemical properties in a paddy field. Soil Sci. Plant Nutr. 2000, 46(2):473-482.
53. Yang C.M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Prec. Agric. 2010, 11:61-81.
54. Yang C.M., Chen R.K. Modeling rice growth with hyperspectral reflectance data. Crop Sci. 2004, 44(4):1283-1290.
55. Yi Q.X., Huang J.F., Wang F.M., Wang X.Z., Liu Z.Y. Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environ. Sci. Technol. 2007, 41:6770-6775.
56. Yoshida H., Horie T., Shiraiwa T. A model for explaining genotypic and environmental variation in vegetative biomass growth in rice based on observed LAI and leaf nitrogen content. Field Crops Res. 2008, 108:222-230.
57. Yoshida S. Fundamentals of Rice Crop Science 1981, Int. Rice Res. Inst., Los Banõs, Philippines.
58. Yoshida S., Parao F.T. Climatic influence on yield and yield components of lowland rice in the tropics. Climate and Rice 1981, 471-494. Int. Rice Res. Inst., Los Banõs, Philippines.
59. Zarco-Tehada 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 Sen. 2001, 39(7):1491-1507.