Amani, M., & Mobasheri, M.R. (2015). A Parametric method for estimation of leaf area index using landsat ETM+ data. GIScience & Remote Sensing 52(4), 475-497.
Ashourloo, D., Mobasheri, M.R., & Huete, A. (2014). Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina). Remote Sens, 6, 4723-4740.
Ashourloo, D., Mobasheri, M.R., & Huete, A. (2014). Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements. Remote Sens, 6, 5107-5123.
Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse ,D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agric, 10, 459-470.
Jordan, C. F., (1969). Derivation of leaf-area index from quality of light on forest floor. Ecological Society of America, 50, 663-666.
Rouse, J.W., Haas, R.H., Schell, J.A., & Eering, D.W D. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In 3rd ERTS Symposium, NASA SP-351 I, 309–317.
Soudani, K., Francois, C., Maire, G., Dantec, V., & Dufrene ,E. (2006). Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 161-175.
Tucker, J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8, 127-150.
Zhang, J., Pu, R., Huang, W., Yuan, L., Luo,J., & Wang ,J. (2012). Using in – situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Research, 134, 165-174.