Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption.
References
[1]
Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998.
[2]
Allen, R.G.; Pereira, L.S.; Smith, M.; Raes, D.; Wright, J.L. The FAO-56 dual crop coefficient method for predicting evaporation from soil and application extensions. J. Irrig. Drain. Eng 2005, 131, 2–13.
[3]
Allen, R.G.; Walter, I.A.; Elliot, R.L.; Howell, T.A.; Itenfisu, D.; Jensen, M.E.; Snyder, R.L. The ASCE Standardized Reference Evaportranspiration Equation; American Society of Civil Engineers: Danvers, MA, USA, 2005; p. 59.
[4]
Justice, C.O.; Townshend, J.R.G. Special issue on the Moderate Resolution Imaging Spectroradiometer (MODIS): A new generation of land surface monitoring. Remote Sens. Environ 2002, 83, 1–2.
[5]
Doorenbos, J.; Pruitt, W.O. Guidelines for Predicting Crop Water Requirements. Irrigation and Drainge Paper 24; FAO: Rome, Italy, 1975.
[6]
Kamble, B.; Chemin, Y.H. GIPE in GRASS Raster Addons, Available online: https://svn.osgeo.org/grass/grass-addons/grass6/imagery/gipe/i.vi.grid/description.html (accessed on 1 October 2012).
[7]
Kamble, B.; Irmak, A. Assimilating Remote Sensing-Based ET into SWAP Model for Improved Estimation of Hydrological Predictions. Proceeding of the 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 7–11 July 2008; 3.
[8]
Sellers, P.J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens 1985, 6, 1335–1372.
[9]
Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ 1979, 8, 127–150.
[10]
Boegh, E.; Soegaard, H.; Hanan, N.; Kabat, O.; Lesch, L. A remote sensing study of the NDVI-Ts relationship and the transpiration from sparse vegetation in the Sahel based on high resolution data. Remote Sens. Environ 1998, 69, 224–240.
Jayanthi, H.; Neale, C.M.U.; Wright, J.L. Seasonal Evapotranspiration Estimation Using Canopy Reflectance: A Case Study Involving Pink Beans. Proceedings of Remote Sensing and Hydrology 2000, Santa Fe, NM, USA, 2–7 April 2000; pp. 302–305.
[17]
Irmak, A.; Ratcliffe, I.; Ranade, P.; Hubbard, K.; Singh, R.K.; Kamble, B.; Kjaersgaard, J. Estimation of land surface evapotranspiration with a satellite remote sensing procedure. Great Plains Res 2011, 21, 73–88.
[18]
Benedetti, R.; Rossinni, P. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ 1993, 45, 311–326.
[19]
Choudhury, B.J.; Ahmed, N.U.; Idso, S.B.; Reginato, R.J.; Daughtry, C.S.T. Relations between evaporation coefficients and vegetation indices studies by model simulations. Remote Sens. Environ 1994, 50, 1–17.
[20]
Irmak, A.; Kamble, B. Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation. Irrig. Sci 2009, 28, 101–112.
[21]
Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Use and Future Perspectives. Proceedings of an International Workshop, Lincoln, NE, USA, 6–10 March 2000. Hubbard, K.G., Sivakumar, M.V.K., Eds.;
[22]
Allen, R.G.; Clemmens, A.J.; Burt, C.M.; Solomon, K.; O’Halloran, T. Prediction accuracy for projectwide evapotranspiration using crop coefficients and reference evapotranspiration. J. Irrig. Drain. Eng 2005, 131, 24–36.
[23]
Gamon, J.A.; Field, C.B.; Goulden, M.; Griffn, K.; Hartley, A.; Joel, G.; Penuelas, J.; Valentini, R. Relationships between NDVI, canopy structure and photosynthesis in three Californian vegetation types. Ecol. Appl 1995, 5, 28–41.
[24]
Tasumi, M.; Allen, R.; Trezza, R.; Wright, J. Satellite-Based energy balance to assess within-population variance of crop coefficient curves. J. Irrig. Drain. Eng 2005, 131, 94–109.
[25]
Hubbard, K.G. Climatic factors that limit daily evapotranspiration in sorghum. Clim. Res 1992, 2, 73–80.
[26]
Baldocchi, D.; Falge, E.; Gu, L.H.; Olson, R.; Hollinger, D.; Running, S.; Anthoni, P.; Bernhofer, C.; Davis, K.; Evans, R.; et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc 2001, 82, 2415–2434.
Hargreaves, G.H.; Allen, R.G. History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng 2003, 129, 53–63.
[31]
Duffie, J.A.; Beckman, W.A. Solar Engineering of Thermal Processes; John Wiley & Sons: New York, NY, USA, 1980.
[32]
Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS, Proceedings of Third ERTS Symposium, Washington, DC, USA, 10–14 December 1973; 1, pp. 309–317.
[33]
Gitelson, A.; Vina, A.; Masek, J.; Verma, S.; Suyker, A. Synoptic monitoring of gross primary productivity of maize using Landsat data. IEEE Geosci. Remote Sens. Lett 2008, 5, 133–137.