1- Aboelghar, M.A., Arafat, S.M., Eslam, F.A. (2013). Hyper Spectral Measurements as a Method for Potato Crop Characterization. International Journal of Advanced Remote Sensing and GIS. Volume 2, Issue 1, pp. 122-129, Article ID ISSN 2320-2-02403.
2- Arafat, S.M., Aboelghar, M. A., Eslam, F.A. (2013). Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data. Advances in Remote Sensing. 2, 63-70.
3- Chang, C.I. (2000). An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Trans. Information Theory, 46, 1927-1932.
4- Chen, J.M. (1995). Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed the boreal conifer forests. IEEE Trans. Geosci. Remote Sens.(submitted).
5- Crippen, R.E.(1990).Calculating the vegetation index faster. Remote Sensing of Environment,34,pp.71-73.
6- Darvishsefat, A.A., Abbasi, M., Schaepman, M. E. (2011).Evaluation of Spectral Reflectance of Seven Iranian Rice Varieties Canopies. J. Agr. Sci. Tech. Vol. 13: 1091-1104.
7- Du, Y., Chang, C.I., Ren, H., Chang, C.C., Jensen, J.O., D’Amico, F.M. (2004). New hyper-spectral discrimination measure for spectral characterization. Opt. Eng. 43,1777-1786.
8-Escadafal, R., Huete, A.R. (1991). tude des propriétés spectrales des sols arides appliquée à lamélioration des indices de vegetation obtenus par télédection. CR Acad. Sci. Paris 312, 1385-1391.
9- FieldSpec® 3 User Manual.(2010). ASD Inc. ASD Document 600540 Rev.
10- Gamon, J. G., Surfus, J. S.(1999). Assessing leaf pigment content and activity with a reflectometer. New Phytologist 143:105-117.
11- Goel, N.S., Qi, W. (1994) Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation. Remote Sensing Reviews, 10, 309-347.
12- Gong, P., Pu, R., Biging, G.S.; Larrieu, M.R. (2003). Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Trans. Geosci. Remote Sens. 41, 1355-1362.
13- Gower, J.C.1985,.Properties of Euclidean and non-Euclidean distance matrices. Linear Algebra and its Applications, 67, 81-97.
14- Gitelson, A.A., Kaufman, Y.J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment. 80, 87-76
15- Gitelson, A.A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18, 2691-2697
16-Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & 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, 90, 337-352.
17- Hardisky M.A., Klemas, V., and Smart, R.M. (1983). The influence of soilsalinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 49, 77-83.
18- Huete, A.R. (1988). A soil-adjusted vegetation index. Remote Sensing of Environment. 25. 295-307.
19- Jordan, C.F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology50. 663-666.
20- Kruse, F., et al.(1993). The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44, 145-163.
21- Merton, R., & Huntington, J. (1999). Early simulation results of the ARIES-1 satellite sensor for multi temporal vegetation research derived from AVIRIS. Available at ftp://popo.jpl.nasa.gov/pub/docs/workshops/ 99_docs/41.pdf (verifi ed 8 Apr. 2008). NASA Jet Propulsion Lab., Pasadena, CA.
22- Pearson, R.L., Miller, L.D. (1972). Remote Mapping of Standing Crop Biomass and Estimation of the Productivity of the Short Grass Prairie, Pawnee National Grasslands, Colorado. In Proceedings of the 8th International Symposium on Remote Sensing of the Environment, Ann Arbor, MI, USA, pp.1357-1381.
23- Pinty, B. & Verstraete, M.M. (1992) GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101: 15-20.
24- Qi, J., Chehbouni, A., Huete, A. R., & Kerr, Y. H. (1994). Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment, 48, 119-126.
25- Rao, N.R., Zbell, B. (2011a). Use of field reflectance data for crop mapping using airborne hyperspectral image. ISPRS Journal of Photogrammetry and Remote Sensing. 66 683-691.
26- Rao, N.R., Zbell, B. (2011b).Transferring spectral libraries of canopy reflectance for crop classification using hyperspectral remote sensing data. biosystems engineering 110 231e246.
27- Rondeaux, G., Steven, M., & Baret, F Optimization of soil -adjusted vegetation index. Remote Sensing of Environment, 24, 109-127.
28- Roujean,Jean-Louis and Breon,Francois-Marie. (1995). Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements. REMOTE SENS. ENVIRON.51: 384-375.
29- Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.,Harlan, J.C. (1974). Monitoring the Vernal Advancements and Retrogradation (Greenwave Effect) of Nature Vegetation; NASA/GSFC Final Report; NASA: Greenbelt, MD, USA.
30- Swain, P.H., Robertson, T.V., Wacker, A.G. (1971). Comparison of the Divergence andB-Distance in Feature Selection. LARS Report. Purdue University.
31- Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127-150.
32- Van Aardat, J. A. N. (2000). Spectral separability among six southern tree species. MSc Thesis,Virginia polytechnic institute and state university Blacksburg, USA. Pp184.
33- Van den Berg, A.K., & T.D. Perkins. (2005). Nondestructive estimation of anthocyanin content in autumn maple leaves. HortScience 40:685-686
34- Van der Meer, F. & Bakker, W. (1997). Cross correlogram spectral matching (CCSM): application to surface mineralogical mapping using AVIRIS data from Cuprite, Nevada. Remote Sensing of Environment61. 371-382.
35- Zomer, R.J., Trabucco, A., Ustin, S.L. (2009). Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. Journal of Environmental Management. 90 ,2170e2177.