Aparicio, N., Villegas, D., Araus, J. L., Casadesus, J. and Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science 42: 1547-1555.
Bannari, A., Khurshid, K. S., Staenz, K. and Schwarz, J. (2008). Potential of Hyperion EO-1 hyperspectral data for wheat crop chlorophyll content estimation. Canadian Journal of Remote Sensing 34: 139-157.
Crippen, E. R. (1990). Calculating the vegetation index faster. Remote Sensing of Environment 34: 71-73.
Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment 25: 295-309.
Jiang, Y., Carrow, R. N. and Duncan, R. R. (2003). Correlation analysis procedures for canopy spectral reflectance data of Seashore Paspalum under Traffic stress. J. Amer. Soc. Hort. Sci. 13: 187-208.
Lymburner, L., Beggs, P. J. and Jacobson, C. R. (2000). Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrametric Engineering & Remote Sensing 66:183-191.
Moran, M. S. (2000). New imaging sensor technologies suitable for agricultural management. Aspects of Applied Biology, 60. Remote Sensing in Agriculture, 1-10.
Pearson, R. L. and Miller, L. D. (1972). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grassland, Colorado. In: proceeding of the 8th international symposium on Remote Sensing of Environment, ERIM International (pp. 1357-1381) (Ann Arbor, MI, USA)
Reujean, J. and Breon, F. (1995). Estimating PAR absorbed by vegetation from bidirectional from reflectance measurements. Remote Sensing of Environment 51: 375-384.
Rondeaux, G., Steven, M. and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55: 95-107.
Rouse, J. W., Haas, Jr. R. H., Deering, D. W., Schell, J. A. and Harlan, J. C. (1974). Monitoring the vernal advancement and retro gradation (green wave effect) of natural vegetation; NASA/GSFC Type III final report, Greenbelt, MD. pp 371.
Tucker, C. J. (1979). Red and Photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment 8: 127-150.
Tyler, A. N., Svab, E., Preston, T., Presing, M. and Kovacs, W. A. (2006). Remote Sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high-suspended sediment. International Journal of Remote Sensing 27(8): 1521-1537.
Vina, A. (2003). Remote detection of Biophysical Properties of Plant Canopies.[Online]:http://calamps.unl.edu/snrscoq/SNRS_Colloquium_2002_Andres_Vina.ppt.
Wu, J., Wang, D. and Bauer, M. E. (2007b). Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crops Research 102: 33-42.
Wu, J., Wang, D., Rosen, C. J. and Bauer, M. B. (2007a). Comparison of petiole nitrate concentration, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research 101: 96-103.
Yang, C., Everitt, J. H. and Bradford, J. M. (2006a). Comparisons of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns. Precision Agriculture 7: 33-44.
Yang, C., Everitt, J. H. and Bradford, J. M. (2006b). Evaluating high-resolution QuickBird satellite imagery for estimating cotton yield. Transactions of the ASABE 49 (5): 1599-1606.
)
View on SCiNiTO