ESTIMATION OF CROP COEFFICIENT VARIATION THROUGH SATELLITE VNIR SPECTRAL DATA | ||||
| Misr Journal of Agricultural Engineering | ||||
| Article 15, Volume 34, Issue 2, April 2017, Page 829-842 PDF (591.47 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2017.96741 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. A. Badr1; M. A. El-Shirbeny1; M. Y. El-Ansary2; M. A. Awad3 | ||||
| 1National Authority for Remote Sensing and Space Sciences (NARSS), Egypt. | ||||
| 2Department of Agricultural Engineering, Faculty of Agriculture, Benha University, Egypt | ||||
| 3Department of Agricultural Engineering, Faculty of Agriculture, Benha University, Egypt. | ||||
| Abstract | ||||
| Crop coefficient is a principle factor to estimate crop water consumption. Traditionally Kc estimated from Kc = ETc/ETo or from the FAO table. The aim of this paper is to develop a simple model to estimate KcNDVI using remote sensing technique. To develop such model this work was cared out in salhia area on three winter crops wheat, potato and sugar beet, on certain dates of their growing seasons of 2013-2014. Landsat 8 and Landsat7, the Enhanced Thematic Mapper Plus (ETM+) satellites were used to generate NDVI values on these dates. The values of NDVI and the Kc estimated from Kc = ETc/ETo were used to established KcNDVI equation. The following equation Kc NDVI = 1.259 NDVI + 0.034 is the developed model with r= 0.92. To validate this model the values of Kc predicted using it at six dates on wheat and potato crops raised on same site on 2014-2015 growing season was compared with estimated KcFAO on same dates. The validation give a good results with r= 0.98 for the two crops. | ||||
| Keywords | ||||
| Normalized deference vegetation index (NDVI), landsat8, Landsat 7 ETM+; Crop coefficient (Kc) | ||||
| References | ||||
|
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998). Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage. FAO56, Rome, Italy.
Allen, R.G., Pereira, L.S., Smith, M., Raes, D. and Wright, J.L. (2005). The FAO-56 dual crop coefficient method for predicting evaporation from soil and application extensions. J. Irrig. Drain. Eng. 131: 2–13.
Allen, R.G., Walter, I.A., Elliot, R.L., Howell, T.A., Itenfisu, D., Jensen, M.E. and Snyder, R.L. (2005b). The ASCE Standardized Reference Evaportranspiration Equation. American Society of Civil Engineers: Danvers, MA, USA: 59.
Baioumy, E., El-Shirbeny, M., El-Ansary, M. and Awad, M. (2016). Estimation of Regional Evapotranspiration Based on Tri-Angle Method Using Thermal and VNIR Data. Inter. J. Plant Soil Sci. 10(6): 1-15.
Bannari, A., Morin, D., Bonn, F. and Huete, A. (1995). A review of vegetation indices. Inter. J. Remote Sensing 13: 85–120. Bausch, W.C. and Neale, C.M.U. (1989). Spectral inputs improve maize crop coefficients and irrigation scheduling. Trans. ASAE, 32: 1901–1908.
Doorenbos, J. and Pruitt, W.O. (1977). Crop water requirement: food and agriculture organization of the United Nations, FAO Irrig. Drain.: 24, Rome, Italy.
El-Shirbeny, M.A., Ali, A.M., Badr, M.A. and Bauomy, E.M. (2014). Assessment of wheat crop coefficient using remote sensing techniques. World Res. J. Agric. Sci. 1(2): 012-016.
Gamon, J.A., Field, C.B., Goulden, M., Griffn, K., Hartley, A., Joel, G., Penuelas, J. and Valentini, R. (1995). Relationships between NDVI, canopy structure and photosynthesis in three Californian vegetation types. Ecol. Appl. 5: 28–41.
Glenn, E.P., Morino, K., Didan, K., Jordan, F., Carroll, K.C., Nagler, P.L., Hultine, K., Sheader, L. and Waugh, J. (2008). Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse-resolution remote sensing. Ecohydrology 1: 316–329.
Huete, A.R. and Glenn, E.P. (2011). Remote sensing of ecosystem structure and function. In Advances in Environmental Remote Sensing, Weng Q (ed). CRC Press: Boca Raton, Florida; 291–320. Hunsaker, D.J., Pinter-Jr, P.J. and Kimball, B.A. (2005).Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig. Sci. 24: 1–14. Irmak, S. (2010). Nebraska water and energy flux measurement, modeling, and research network (NEBFLUX). Trans. ASABE 53: 1097–1115.
Jiang, L. and Islam, S. (1999). A methodology for estimation of surface evapotranspiration overlarge areas using remote sensing observations. Geophys. Res. Lett. 26 (17): 2773–2776.
Jiang, L. and Islam, S. (2001). Estimation of surface evaporation map over Southern Great Plains using remote sensing data. Water Resour. Res. 37 (2), 329–340.
Jiang, L. and Islam, S. (2003). An intercomparison of regional latent heat flux estimation using remote sensing data. Int. J. Remote Sens. 24 (11): 2221–2236.
Justice, C.O. and Townshend, J.R.G. (2002). Special issue on the Moderate Resolution Imaging Spectroradiometer (MODIS): A new generation of land surface monitoring. Remote Sens. Environ. 83: 1–2.
Kamble, B., Kilic, A. and Hubbard, K. (2013). Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index. Remote Sens. 5(4): 1588-1602.
Moran, M.S., Clarke, T.R., Inoue, Y. and Vidal, A. (1994). Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index, Remote Sens. Environ. 49: 246-263
Neale, C.M.U. (1983). Monitoring corn development using reflected radiation. M.S. Thesis, Colorado State University, Fort Collins: 134.
Rouse, J.W., Hass, R.H., Schell, J.A. and Deering, D.W. (1973). Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351, 1: 309–317. Sellers, P.J. (1985). Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6, 1335–1372. Shahidian, S., Serralheiro, R., Teixeira, J.L., Santos, F.L., Oliveira, M.R., Costa, J., Toureiro, C., Haie, N. and Machado, R. (2009). Drip Irrigation using a PLC based Adaptive Irrigation System WSEAS Transactions on Environment and Development, 2.
Tasumi, M. and Allen, R.G. (2007). Satellite-Based ET Mapping to Assess Variation in ET with Timing of Crop Development.
Agricultural Water Management, 88, 54-62.
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127–150. Yoder, B.J. and Waring, R.H. (1994). The normalized difference vegetation index of small Douglas fir canopies with varying chlorophyll concentrations, Remote Sensing Environ. 49: 81-91 | ||||
|
Statistics Article View: 240 PDF Download: 951 |
||||
