Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices

Canadian Journal of Remote Sensing

Volumn 34, Issue , 2008, Pages S124-S138

  Liu, Jiangui ^a,b   Miller, John R ^a   Haboudane, Driss ^c   Pattey, Elizabeth ^b   Hochheim, Klaus ^d  

^a YORK UNIVERSITY   (Canada)

^b AGRICULTURE AND AGRI FOOD CANADA   (Canada)

^c UNIVERSITÉ DU QUÉBEC À CHICOUTIMI   (Canada)

^d Noetix Research Inc   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARID REGIONS; ATMOSPHERICS; CROPS; EIGENVALUES AND EIGENFUNCTIONS; ESTIMATION; FORESTRY; IMAGE SENSORS; SOILS;

AGRICULTURAL FIELDS; COMPACT AIRBORNE SPECTROGRAPHIC IMAGER (CASI); CROP GROWTH; CROP MANAGEMENTS; CROP PERFORMANCE; EARLY GROWTH; FRACTION ESTIMATION; GROWING SEASONS; HYPER-SPECTRAL DATA; LINEAR SPECTRAL UNMIXING; MONITOR (CO); NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI); PRECISION AGRICULTURE; REGIONAL WEATHER; SPECTRA (CO); VEGETATION FRACTIONS; VEGETATION INDEX (VI);

VEGETATION;

EID: 47549090072     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m07-062     Document Type: Article

References (48)

1. Adams, J.B., Sabol, D.E., Kapos, V., Filho, R.A., Roberts, D.A., Smith, M.O., and Gillespie, A.R. 1995. Classification of multispectral images based on fractions of end-members: application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment, Vol. 52, No. 2, pp. 137-154.
2. Baret, F., and Goyot, G. 1991. Potential and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, Vol. 35, No. 2, pp. 161-173.
3. Baret, F., Guyot, G., and Major, D.J. 1989. TSAVI: a vegetation index that minimizes soil brightness effects on LAI and APAR estimation. In IGARSS’89: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium and the 12th Canadian Symposium on Remote Sensing, 10-14 July 1989, Vancouver, B.C. pp. 1355-1358.
4. Baret, F., Clevers, J.G.P.W., and Steven, M.D. 1995. The robustness of canopy gap fraction estimation from red and near-infrared reflectance: a comparison of approaches. Remote Sensing ofEnvironment, Vol. 54, No. 2, pp. 141-151.
5. Bateson, A., and Curtiss, B. 1996. A method for manual end-member selection and spectral unmixing. Remote Sensing of Environment, Vol. 55, No. 3, pp. 229-243.
6. Boardman, J. 1993. Automated spectral unmixing of AVIRIS data using convex geometry concepts. In Summary of the 4th Annual JPL Airborne Geoscience Workshop, 25-29 October 1993, Washington, D.C. Edited by R.O. Green. Jet Propulsion Laboratory (JPL), Pasadena, Calif. Publication JPL-93-26, Vol. 1, pp. 11-14.
7. Boardman, J.W., and Kruse, F.A. 1994. Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine Mountains, Nevada. In Proceedings of the 10th Thematic Conference on Geologic Remote Sensing, 9-12 May 1994, San Antonio, Tex. Environmental Research Institute of Michigan (ERIM), Ann Arbor, Mich. pp. 407-418.
8. Boardman, J.W., Kruse, F.A, and Green, R.O. 1995. Mapping target signatures via partial unmixing of AVIRIS data. In Summaries of the 5th Annual JPL Airborne Geoscience Workshop, 23-26 January 1995, Pasadena, Calif. Edited by R.O. Green. Vol. 1, pp. 23-26.
9. Borel, C.C., and Gerstl, A.W. 1994. Nonlinear spectral mixing models for vegetation and soil surfaces. Remote Sensing of Environment, Vol. 47, pp. 403-416.
10. Carlson, T.N., and Ripley, D.A. 1997. On the relationship between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, Vol. 62, pp. 241-252.
11. Clevers, J.G.P.W. 1989. The application of a weighted infrared vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sensing of Environment, Vol. 29, pp. 25-37.
12. Deardorff, J.W. 1978. Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation. Journal of Geophysical Research, Vol. 20, pp. 1889-1903.
13. Eastwood, J.A., Yates, M.G., Thomson, A.G., and Fuller, R.M. 1997. The reliability of vegetation indices for monitoring saltmarsh vegetation cover. International Journal of Remote Sensing, Vol. 18, No. 18, pp. 3901-3907.
14. Gillespie, A.R., Smith, M.O., Adams, J.B., Willis, S.C., Fischer, A.F., III, and Sabol, D.E. 1990. Interpretation of residue images: spectral mixture analysis of AVIRIS images, Owens Valley, California. In Proceedings of the 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, 45 June 1990, Pasadena, Calif. Edited by R.O. Green. pp. 243-270.
15. Gitelson, A.A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., and Derry, D. 2002a. Vegetation and soil lines in visible spectral space: a concept and technique for remote sensing of vegetation fraction. International Journal of Remote Sensing, Vol. 23, No. 13, pp. 2537-2562.
16. Gitelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. 2002b. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, Vol. 80, pp. 76-87.
17. Guilfoyle, K.J., Althouse, M.L., and Chang, C.I. 2001. A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 8, pp. 2314-2318.
18. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., and Strachan, I.B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment, Vol. 90, pp. 337-352.
19. Hapke, B. 1981. Bidirectional reflectance spectroscopy, I: Theory. Journal of Geophysical Research, Vol. 86, pp. 3039-3054.
20. Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, Vol. 25, pp. 295-309.
21. Jacquemoud, S., and Baret, F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, Vol. 34, No. 1, pp. 75-91.
22. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.M., and Jaggard, K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data: application to TM and AVIRIS sensors. Remote Sensing of Environment, Vol. 52, No. 3, pp. 163-172.
23. Jacquemoud, S., Bacour, C., Poilve, H., and Frangi, J.-P. 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: direct andinverse mode. Remote Sensing ofEnvironment, Vol. 74, pp. 471-481.
24. Johnson, P.E., Smith, M.O., and Adams, J.B. 1992. Simple algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra. Journal of Geophysical Research, Vol. 97, No. E2, pp. 2649-2657.
25. Kaufman, Y.J., and Tanre, D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, pp. 261-270.
26. Lelong, C.C.D., Pinet, P.C., and Poilve, H. 1998. Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sensing of Environment, Vol. 66, pp. 179-191.
27. Nilson, T. 1971. A theoretical analysis of the frequency of gaps in plant stands. Agricultural Meteorology, Vol. 8, pp. 25-38.
28. O’neill, N.T., Zagolski, F., Bergeron, M., Royer, A., Miller, J.R., and Freemantle, J. 1997. Atmospheric correction validation of casi images acquired over the BOREAS southern study area. Canadian Journal of Remote Sensing, Vol. 23, pp. 142-162.
29. PCI Geomatics Enterprises Inc. 2001. EASI/PACE to Geomatica, version 8.2.3. PCI Geomatics Enterprises Inc., Richmond Hill, Ont.
30. Peddle, D.R., Hall, F.G., and LeDrew, E.F. 1999. Spectral mixture analysis and geometric-optical reflectance modeling of boreal forest biophysical structure. Remote Sensing of Environment, Vol. 67, pp. 288-297.
31. Petrou, M., and Foschi, P.G. 1999. Confidence in linear spectral unmixing of single pixels. IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 1, pp. 624-626.
32. Pickup, G., Chewings, V.H., and Nelson, D.J. 1993. Estimating changes of vegetation cover over time in arid rangelands using Landsat MSS data. Remote Sensing of Environment, Vol. 43, pp. 243-263.
33. Purevdorj, Ts., Tateishi, R., Ishiyam, T., and Honda, Y. 1998. Relationship between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, Vol. 19, No. 28, pp. 3519-3535.
34. Qi, J., Chehbouni, A., Huete, A.R., Keer, Y.H., and Sorooshian, S. 1994. A modified soil vegetation adjusted index. Remote Sensing of Environment, Vol. 48, pp. 119-126.
35. Ray, T.W., and Murray, B.C. 1996. Nonlinear spectral mixing in desert vegetation. Remote Sensing of Environment, Vol. 55, No. 1, pp. 59-64.
36. Richardson, A.J., and Wiegand, C.L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, Vol. 43, pp. 1541-1552.
37. Richardson, A.J., Wiegand, C.L., Wanjura, D.F., Dusek, D., and Steiner, J.L. 1992. Multi-site analysis of spectral-biophysical data for sorghum. Remote Sensing of Environment, Vol. 41, pp. 71-82.
38. Roberts, D.A., Smith, M.O., and Adams, J.B. 1993. Green vegetation, nonphotosynthetic vegetation, and soil in AVIRIS data. Remote Sensing of Environment, Vol. 44, pp. 255-269.
39. Roberts, D.A., Green, R.O., and Adams, J.B. 1997. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sensing of Environment, Vol. 62, pp. 223-240.
40. Rondeaux, G., Steven, M., and Baret, F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, Vol. 55, No. 2, pp. 95107.
41. Roujean, J.-L., Tanre, D., Breon, F.-M., and Deuze, J.-L. 1997. Retrieval of land surface parameters from airborne POLDER bi-directional reflectance distribution function during HAPEX-Sahel. Journal of Geophysical Research, Vol. 102, pp. 11 201 - 11 218.
42. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. 1974. Monitoring the vernal advancements and retrogradation of natural vegetation. Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA), Greenbelt, Md. NASA/GSFC Final Report. 371 pp.
43. Sabol, D.E., Adams, J.B., and Smith, M.O. 1992. Quantitative sub-pixel spectral detection of targets in multispectral images. Journal of Geophysical Research, Vol. 97, No. E2, pp. 2659-2672.
44. Smith, M.O., Ustin, S.L., Adams, J.B., and Gillepsie, A.R. 1990. Vegetationin deserts: I. A regional measure of abundance from multi-spectral images. Remote Sensing of Environment, Vol. 31, No. 1, pp. 1-26.
45. Steven, M.D., Biscoe, P.V., Jaggard, K.W., and Paruntu, J. 1986. Foliage cover and radiation interception. Field Crops Research, Vol. 13, pp. 75-87.
46. Tompkins, S., Mustard, J.F., Pieters, C.M., and Forsyth, D.W. 1997. Optimization of end-members for spectral mixture analysis. Remote Sensing of Environment, Vol. 59, No. 3, pp. 472-489.
47. Verhoef, W. 1984. Light scattering by leaf layers with applications to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, Vol. 16, pp. 125-141.
48. Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., Mohammed, G.H., and Sampson, P.H. 2001. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 7, pp. 1491-1507.