Spectral Crop Coefficient Approach for Estimating Daily Crop Water Use
Affiliation(s)
1 Texas A & M AgriLife Research and Extension Center, Vernon, Texas, USA. 2 Texas Tech University and Texas A & M AgriLife Research Center, Lubbock, Texas, USA.
1 Texas A & M AgriLife Research and Extension Center, Vernon, Texas, USA. 2 Texas Tech University and Texas A & M AgriLife Research Center, Lubbock, Texas, USA.
ABSTRACT
While the amount of water used by a crop can be measured using lysimeters or eddy covariance systems, it is more common to estimate this quantity based on weather data and crop-related factors. Among these approaches, the standard crop coefficient method has gained widespread use. A limitation of the standard crop coefficient approach is that it applies to “standard conditions” that are invariant from field to field. In this article, we describe a method for estimating daily crop water use (CWU) that is specific to individual fields. This method, the “spectral crop coefficient” approach, utilizes a crop coefficient numerically equivalent to the crop ground cover observed in a field using remote sensing. This “spectral crop coefficient” Ksp is multiplied by potential evapotranspiration determined from standard weather observations to estimate CWU. We present results from a study involving three farmers' fields in the Texas High Plains in which CWU estimated using the Ksp approach is compared to observed values obtained from eddy covariance measurements. Statistical analysis of the results suggests that the Ksp approach can produce reasonably accurate estimates of daily CWU under a variety of irrigation strategies from fully irrigated to dryland. These results suggest that the Ksp approach could be effectively used in applications such as operational irrigation scheduling, where its field-specific nature could minimize over-irrigation and support water conservation.
While the amount of water used by a crop can be measured using lysimeters or eddy covariance systems, it is more common to estimate this quantity based on weather data and crop-related factors. Among these approaches, the standard crop coefficient method has gained widespread use. A limitation of the standard crop coefficient approach is that it applies to “standard conditions” that are invariant from field to field. In this article, we describe a method for estimating daily crop water use (CWU) that is specific to individual fields. This method, the “spectral crop coefficient” approach, utilizes a crop coefficient numerically equivalent to the crop ground cover observed in a field using remote sensing. This “spectral crop coefficient” Ksp is multiplied by potential evapotranspiration determined from standard weather observations to estimate CWU. We present results from a study involving three farmers' fields in the Texas High Plains in which CWU estimated using the Ksp approach is compared to observed values obtained from eddy covariance measurements. Statistical analysis of the results suggests that the Ksp approach can produce reasonably accurate estimates of daily CWU under a variety of irrigation strategies from fully irrigated to dryland. These results suggest that the Ksp approach could be effectively used in applications such as operational irrigation scheduling, where its field-specific nature could minimize over-irrigation and support water conservation.
Cite this paper
Rajan, N. and Maas, S. (2014) Spectral Crop Coefficient Approach for Estimating Daily Crop Water Use. Advances in Remote Sensing, 3, 197-207. doi: 10.4236/ars.2014.33013.
Rajan, N. and Maas, S. (2014) Spectral Crop Coefficient Approach for Estimating Daily Crop Water Use. Advances in Remote Sensing, 3, 197-207. doi: 10.4236/ars.2014.33013.
References
[1] Burman, R.D. (2003) Evapotranspiration Formulas. In: Stewart, B.A. and Howell, T.A., Eds., Encyclopedia of Water Science, Marcel Dekker, New York, 253-257. [2] Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998) Crop Evaporation: Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome. [3] Heilman, J.L., W.E. Heilman, and D.G. Moore (1982) Evaluating the Crop Coefficient Using Spectral Reflectance. Agronomy Journal, 74, 967-971.
http://dx.doi.org/10.2134/agronj1982.00021962007400060010x [4] Bausch, W.C. and Neale, C.M.U. (1987) Crop Coefficients Derived from Reflected Canopy Radiation: A Concept. Transactions of ASAE, 30, 703-709. http://dx.doi.org/10.13031/2013.30463 [5] Bausch, W.C. and Neale, C.M.U. (1989) Spectral Inputs Improve Corn Crop Coefficients and Irrigation Scheduling. Transactions of ASAE, 32, 1901-1908. http://dx.doi.org/10.13031/2013.31241 [6] Neale, C.M.U., Bausch, W.C. and Heerman, D.F. (1989) Development of Reflectance Based Crop Coefficients for Corn. Transactions of ASAE, 28, 773-780. [7] Bausch, W.C. (1995) Remote-sensing of Crop Coefficients for Improving the Irrigation Scheduling of Corn. Agricultural Water Management, 27, 55-68. http://dx.doi.org/10.1016/0378-3774(95)01125-3 [8] Hunsaker, D.J., Pinter Jr., P.J., Barnes, E.M. and Kimball, B.A. (2003) Estimating Cotton Evapotranspiration Crop Coefficients with a Multispectral Vegetation Index. Irrigation Science, 22, 95-104. http://dx.doi.org/10.1007/s00271-003-0074-6 [9] Hunsaker, D.J., Pinter Jr., P.J. and Kimball, B.A. (2005) Wheat Basal Crop Coefficients Determined by Normalized Difference Vegetation Index. Irrigation Science, 24, 1-14. http://dx.doi.org/10.1007/s00271-005-0001-0 [10] Rajan, N., Maas, S.J. and Kathilankal, J. (2010) Estimating Crop Water Use of Cotton in the Texas High Plains. Agronomy Journal, 102, 1641-1651. http://dx.doi.org/10.2134/agronj2010.0076 [11] Maas, S.J. and Rajan, N. (2008) Estimating Ground Cover of Field Crops Using Medium-Resolution Multispectral Satellite Imagery. Agronomy Journal, 100, 320-327.
http://dx.doi.org/10.2134/agrojnl2007.0140 [12] Rajan, N. and Maas, S.J. (2009) Mapping Crop Ground Cover Using Airborne Multispectral Digital Imagery. Precision Agriculture, 10, 304-318. http://dx.doi.org/10.1007/s11119-009-9116-2 [13] NRCS (1978) Soil Survey of Floyd County, Texas. Natural Resource Conservation Service, US Department of Agriculture, Washington DC. [14] Larkin, T.J. and Bowmar, G.W. (1983) Climatic Atlas of Texas. Publication LP-192, Texas Department of Water Resources, Austin. [15] Campbell Scientific (2006) Open-Path Eddy Covariance System Operator’s Manual (Revised 9/06). Campbell Scientific, Inc., Logan. [16] Foken, T. (2008) The Energy Balance Closure Problem: An Overview. Ecological Applications, 18, 1351-1367. http://dx.doi.org/10.1890/06-0922.1 [17] Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulmans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalsky, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R. and Verma, S. (2002) Energy Balance Closure at FLUXNET Sites. Agricultural and Forest Meteorology, 113, 223-243.
http://dx.doi.org/10.1016/S0168-1923(02)00109-0 [18] Mauder, M., Liebenthal, C., Gockede, M., Leps, J., Beyrich, F. and Foken, T. (2006) Processing and Quality Control of Flux Data during LITFASS-2003. Boundary-Layer Meteorology, 121, 67-88.
http://dx.doi.org/10.1007/s10546-006-9094-0 [19] Twine, T.E., Kustas, W.P., Norman, J.M., Cook, D.R., Houser, P.R., Meyers, T.P., Prueger, J.P., Starks, P.J. and Wesley, M.L. (2000) Correcting Eddy-Covariance Flux Underestimates over a Grassland. Agricultural and Forest Meteorology, 103, 279-300. http://dx.doi.org/10.1016/S0168-1923(00)00123-4 [20] http://edcsns17.cr.usgs.gov/EarthExplorer/ [21] Chander, G. and Markham, B. (2003) Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674-2677. http://dx.doi.org/10.1109/TGRS.2003.818464 [22] http://www.mesonet.ttu.edu/ [23] Monteith, J.L. and Unsworth, M. (1990) Principles of Environmental Physics. Edward Asner Publishers, London. [24] Allen, R.G., Pruitt, W.O., Wright, J.L., Howell, T.A., Ventura, F., Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J.B., Smith, M., Pereira, L.B., Raes, D., Perrier, A., Alves, I., Walter, I. and Elliott, R. (2006) A Recommendation on Standardized Surface Resistance for Hourly Calculation of Reference ET0 by the FAO56 Penman-Monteith Method. Agricultural Water Management, 81, 1-22. http://dx.doi.org/10.1016/j.agwat.2005.03.007 [25] Ostle, B. and Mensing, R.W. (1982) Statistics in Research. Iowa State University Press, Ames. [26] Rosenthal, W.D., Arkin, G.F., Shouse, P.J. and Jordan, W.R. (1987) Water Deficit Effects on Transpiration and Leaf Growth. Agronomy Journal, 79, 1019-1026.
http://dx.doi.org/10.2134/agronj1987.00021962007900060014x
[1] Burman, R.D. (2003) Evapotranspiration Formulas. In: Stewart, B.A. and Howell, T.A., Eds., Encyclopedia of Water Science, Marcel Dekker, New York, 253-257. [2] Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998) Crop Evaporation: Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome. [3] Heilman, J.L., W.E. Heilman, and D.G. Moore (1982) Evaluating the Crop Coefficient Using Spectral Reflectance. Agronomy Journal, 74, 967-971.
http://dx.doi.org/10.2134/agronj1982.00021962007400060010x [4] Bausch, W.C. and Neale, C.M.U. (1987) Crop Coefficients Derived from Reflected Canopy Radiation: A Concept. Transactions of ASAE, 30, 703-709. http://dx.doi.org/10.13031/2013.30463 [5] Bausch, W.C. and Neale, C.M.U. (1989) Spectral Inputs Improve Corn Crop Coefficients and Irrigation Scheduling. Transactions of ASAE, 32, 1901-1908. http://dx.doi.org/10.13031/2013.31241 [6] Neale, C.M.U., Bausch, W.C. and Heerman, D.F. (1989) Development of Reflectance Based Crop Coefficients for Corn. Transactions of ASAE, 28, 773-780. [7] Bausch, W.C. (1995) Remote-sensing of Crop Coefficients for Improving the Irrigation Scheduling of Corn. Agricultural Water Management, 27, 55-68. http://dx.doi.org/10.1016/0378-3774(95)01125-3 [8] Hunsaker, D.J., Pinter Jr., P.J., Barnes, E.M. and Kimball, B.A. (2003) Estimating Cotton Evapotranspiration Crop Coefficients with a Multispectral Vegetation Index. Irrigation Science, 22, 95-104. http://dx.doi.org/10.1007/s00271-003-0074-6 [9] Hunsaker, D.J., Pinter Jr., P.J. and Kimball, B.A. (2005) Wheat Basal Crop Coefficients Determined by Normalized Difference Vegetation Index. Irrigation Science, 24, 1-14. http://dx.doi.org/10.1007/s00271-005-0001-0 [10] Rajan, N., Maas, S.J. and Kathilankal, J. (2010) Estimating Crop Water Use of Cotton in the Texas High Plains. Agronomy Journal, 102, 1641-1651. http://dx.doi.org/10.2134/agronj2010.0076 [11] Maas, S.J. and Rajan, N. (2008) Estimating Ground Cover of Field Crops Using Medium-Resolution Multispectral Satellite Imagery. Agronomy Journal, 100, 320-327.
http://dx.doi.org/10.2134/agrojnl2007.0140 [12] Rajan, N. and Maas, S.J. (2009) Mapping Crop Ground Cover Using Airborne Multispectral Digital Imagery. Precision Agriculture, 10, 304-318. http://dx.doi.org/10.1007/s11119-009-9116-2 [13] NRCS (1978) Soil Survey of Floyd County, Texas. Natural Resource Conservation Service, US Department of Agriculture, Washington DC. [14] Larkin, T.J. and Bowmar, G.W. (1983) Climatic Atlas of Texas. Publication LP-192, Texas Department of Water Resources, Austin. [15] Campbell Scientific (2006) Open-Path Eddy Covariance System Operator’s Manual (Revised 9/06). Campbell Scientific, Inc., Logan. [16] Foken, T. (2008) The Energy Balance Closure Problem: An Overview. Ecological Applications, 18, 1351-1367. http://dx.doi.org/10.1890/06-0922.1 [17] Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulmans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalsky, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R. and Verma, S. (2002) Energy Balance Closure at FLUXNET Sites. Agricultural and Forest Meteorology, 113, 223-243.
http://dx.doi.org/10.1016/S0168-1923(02)00109-0 [18] Mauder, M., Liebenthal, C., Gockede, M., Leps, J., Beyrich, F. and Foken, T. (2006) Processing and Quality Control of Flux Data during LITFASS-2003. Boundary-Layer Meteorology, 121, 67-88.
http://dx.doi.org/10.1007/s10546-006-9094-0 [19] Twine, T.E., Kustas, W.P., Norman, J.M., Cook, D.R., Houser, P.R., Meyers, T.P., Prueger, J.P., Starks, P.J. and Wesley, M.L. (2000) Correcting Eddy-Covariance Flux Underestimates over a Grassland. Agricultural and Forest Meteorology, 103, 279-300. http://dx.doi.org/10.1016/S0168-1923(00)00123-4 [20] http://edcsns17.cr.usgs.gov/EarthExplorer/ [21] Chander, G. and Markham, B. (2003) Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674-2677. http://dx.doi.org/10.1109/TGRS.2003.818464 [22] http://www.mesonet.ttu.edu/ [23] Monteith, J.L. and Unsworth, M. (1990) Principles of Environmental Physics. Edward Asner Publishers, London. [24] Allen, R.G., Pruitt, W.O., Wright, J.L., Howell, T.A., Ventura, F., Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J.B., Smith, M., Pereira, L.B., Raes, D., Perrier, A., Alves, I., Walter, I. and Elliott, R. (2006) A Recommendation on Standardized Surface Resistance for Hourly Calculation of Reference ET0 by the FAO56 Penman-Monteith Method. Agricultural Water Management, 81, 1-22. http://dx.doi.org/10.1016/j.agwat.2005.03.007 [25] Ostle, B. and Mensing, R.W. (1982) Statistics in Research. Iowa State University Press, Ames. [26] Rosenthal, W.D., Arkin, G.F., Shouse, P.J. and Jordan, W.R. (1987) Water Deficit Effects on Transpiration and Leaf Growth. Agronomy Journal, 79, 1019-1026.
http://dx.doi.org/10.2134/agronj1987.00021962007900060014x