Exploring the best hyperspectral features for LAI estimation using partial least squares regression

Remote Sensing

Volumn 6, Issue 7, 2014, Pages 6221-6241

  Li, Xinchuan ^a   Zhang, Youjing ^a   Bao, Yansong ^b   Luo, Juhua ^c   Jin, Xiuliang ^d   Xu, Xingang ^d   Song, Xiaoyu ^d   Yang, Guijun ^d  

^a HOHAI UNIVERSITY   (China)

^b NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c NANJING INSTITUTE OF GEOGRAPHY AND LIMNOLOGY   (China)

^d NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

Author keywords

Hyperspectral remote sensing; Leaf area index (LAI); Partial least squares regression; Spectral feature

Indexed keywords

REFLECTION; REGRESSION ANALYSIS; REMOTE SENSING;

HYPERSPECTRAL REFLECTANCE; HYPERSPECTRAL REMOTE SENSING; LEAF AREA INDEX; PARTIAL LEAST SQUARES REGRESSION; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); REFLECTANCE SPECTRUM; SPECTRAL FEATURE; VARIABLE IMPORTANCES;

LEAST SQUARES APPROXIMATIONS;

EID: 84904497701     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6076221     Document Type: Article

References (68)

1. Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421-429.
2. Casa, R.; Varella, H.; Buis, S.; Gu E Rif, M.; de Solan, B.; Baret, F. Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach. Eur. J. Agron. 2012, 37, 1-10.
3. Chen, J.M.; Cihlar, J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ. 1996, 55, 153-162.
4. Pu, R. Comparing canonical correlation analysis with partial least squares regression in estimating forest leaf area index with multitemporal Landsat TM imagery. GISci. Remote Sens. 2012, 49, 92-116.
5. Walthall, C.; Dulaney, W.; Anderson, M.; Norman, J.; Fang, H.; Liang, S. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sens. Environ. 2004, 92, 465-474.
6. Eklundh, L.; Harrie, L.; Kuusk, A. Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest. Remote Sens. Environ. 2001, 78, 239-251.
7. Bréda, N.J. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. J. Exp. Bot. 2003, 54, 2403-2417.
8. Hansen, P.M.; Schjoerring, J.K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 2003, 86, 542-553.
9. Lee, K.; Cohen, W.B.; Kennedy, R.E.; Maiersperger, T.K.; Gower, S.T. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ. 2004, 91, 508-520.
10. Thenkabail, P.S.; Enclona, E.A.; Ashton, M.S.; van der Meer, B. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens. Environ. 2004, 91, 354-376.
11. Becker, B.L.; Lusch, D.P.; Qi, J. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis. Remote Sens. Environ. 2005, 97, 238-248.
12. Wang, F.; Huang, J.; Zhou, Q.; Wang, X. Optimal waveband identification for estimation of leaf area index of paddy rice. J. Zhejiang Univ. Sci. B 2008, 9, 953-963.
13. Strachan, I.B.; Pattey, E.; Boisvert, J.B. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens. Environ. 2002, 80, 213-224.
14. Pu R.; Gong, P.; Biging, G.S.; Larrieu, M.R. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Trans. Geosci. Remote Sens. 2003, 41, 916-921.
15. Darvishzadeh, R.; Atzberger, C.; Skidmore, A.K.; Abkar, A.A. Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position. Int. J. Remote Sens. 2009, 30, 6199-6218.
16. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. 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.
17. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295-309.
18. Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119-126.
19. Roujean, J.; Breon, F. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384.
20. 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.
21. Viña, A.; Gitelson, A.A.; Nguy-Robertson, A.L.; Peng, Y. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ. 2011, 115, 3468-3478.
22. Clevers, J.; de Jong, S.M.; Epema, G.F.; van der Meer, F.D.; Bakker, W.H.; Skidmore, A.K.; Scholte, K.H. Derivation of the red edge index using the MERIS standard band setting. Int. J. Remote Sens. 2002, 23, 3169-3184.
23. Ryu, C.; Suguri, M.; Umeda, M. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Field Crop. Res. 2011, 122, 214-224.
24. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 2008, 63, 409-426.
25. Oumar, Z.; Mutanga, O.; Ismail, R. Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 113-121.
26. Wold, H. Estimation of principal components and related models by iterative least squares. In Multivariate Analysis; Krishnaiah, P.R., Ed.; Academic Press: Waltham, MA, USA, 1966; pp. 391-420.
27. Chin, W.W. The partial least squares approach to structural equation modeling. Modern Methods Bus. Res. 1998, 295, 295-336.
28. Haenlein, M.; Kaplan, A.M. A beginner's guide to partial least squares analysis. Underst. Stat. 2004, 3, 283-297.
29. Huang, Z.; Turner, B.J.; Dury, S.J.; Wallis, I.R.; Foley, W.J. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens. Environ. 2004, 93, 18-29.
30. Nguyen, H.T.; Lee, B. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron. 2006, 24, 349-356.
31. Vyas, D.; Christian, B.; Krishnayya, N.S.R. Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data. Int. J. Remote Sens. 2012, 34, 1676-1690.
32. Li, F.; Mistele, B.; Hu, Y.; Chen, X.; Schmidhalter, U. Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Eur. J. Agron. 2014, 52, 198-209.
33. Savitzky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36, 1627-1639.
34. Clark, R.N.; Roush, T.L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res.: Sol. Earth 1984, 89, 6329-6340.
35. Mutanga, O.; Skidmore, A.K. Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. Int. J. Appl. Earth Obs. Geoinf. 2004, 5, 87-96.
36. Kokaly, R.F.; Clark, R.N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens. Environ. 1999, 67, 267-287.
37. Gong, P.; Pu, R.; Heald, R.C. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. Int. J. Remote Sens. 2002, 23, 1827-1850.
38. Kaufman, Y.J.; Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 1992, 30, 261-270.
39. Richardson, A.J.; Weigand, C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 1977, 43, 1541-1552.
40. Hui, Q.L.; Huete, A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 1995, 33, 457-465.
41. 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.
42. 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.
43. Chen, J.M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 1996, 22, 229-242.
44. Rouse, J.W.; Hass, R.H.; Shell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of Third ERTS Symosium, Washington, DC, USA, 10-14 December 1973.
45. Goel, N.S.; Qin, W. Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation. Remote Sens. Rev. 1994, 10, 309-347.
46. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107.
47. Jordan, C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969, 50, 663-666.
48. Tang, S.; Zhu, Q.; Wang, J.; Zhou, Y.; Zhao, F. Theoretical bases and application of three gradient difference vegetation index (In Chinese). Sci. China Ser. D Earth Sci. 2003, 33, 1094-1102.
49. 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.
50. Gitelson, A.A. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 2003, 30, doi:10.1029/2002GL016450.
51. Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403-5413.
52. Penuelas, J.; Filella, I.; Serrano L.; Savé, R. Cell wall elasticity and Water Index (R970 nm/R900 nm) in wheat under different nitrogen availabilities. Int. J. Remote Sens. 1996, 17, 373-382.
53. Gao, B.; Goetzt, A.F.H. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens. Environ. 1995, 52, 155-162.
54. Hardisky, M.A.; Klemas, V.; Smart, R.M. The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of spartina alterniflora canopies. Photogramm. Eng. Remote. Sens. 1983, 49, 77-83.
55. Apan, A.; Held, A.; Phinn, S.; Markley, J. Detecting sugarcane "range rust" disease using EO-1 Hyperion hyperspectral imagery. Int. J. Remote Sens. 2004, 25, 489-498.
56. Delalieux, S.; Somers, B.; Hereijgers, S.; Verstraeten, W.W.; Keulemans, W.; Coppin, P. A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sens. Environ. 2008, 112, 3762-3772.
57. Abdi, H. Partial Least Square Regression (PLS Regression). In Encyclopedia of Social Science Research Methods; SAGE: Thousand Oaks, CA, USA, 2003; pp. 792-795.
58. Helland, I.S. Partial least squares regression and statistical models. Scand. J. Stat. 1990, 17, 97-114.
59. Sheng, C.; Xia, H.; Harris, C.J.; Sharkey, P.M. Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Trans. Syst. Man Cybern. B Cybern. 2004, 34, 898-911.
60. Chong, I.; Jun, C. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. 2005, 78, 103-112.
61. Lazraq, A.; Cléroux, R.; Gauchi, J. Selecting both latent and explanatory variables in the PLS1 regression model. Chemom. Intell. Lab. 2003, 66, 117-126.
62. Mutanga, O.; Skidmore, A.K.; Prins, H.H.T. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens. Environ. 2004, 89, 393-408.
63. Oumar, Z.; Mutanga, O. Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa using field spectra resampled to the Sumbandila Satellite Sensor. Int. J. Appl. Earth Obs. Geoinf. 2010, 12, 158-164.
64. Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999-4014.
65. Herrmann, I.; Pimstein, A.; Karnieli, A.; Cohen, Y.; Alchanatis, V.; Bonfil, D.J. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sens. Environ. 2011, 115, 2141-2151.
66. Delegido, J.; Verrelst, J.; Meza, C.M.; Rivera, J.P.; Alonso, L.; Moreno, J. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur. J. Agron. 2013, 46, 42-52.
67. Atzberger, C.; Guérif, 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.
68. Mirzaie, M.; Darvishzadeh, R.; Shakiba, A.; Matkan, A.A.; Atzberger, C.; Skidmore, A. Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 1-11