Presenting a simple equation to determine the reference evapotranspiration using NOAA satellite data

Document Type : Research Paper

Authors

Graduated master science

Abstract

Accurate estimation of reference evapotranspiration (ETo) is needed for water resources management, farm irrigation scheduling, and environmental assessment. A large number of methods have been developed for assessing ETo from meteorological data. However, most weather stations around the globe are located in nonagricultural settlings of dry, bare soil surface and/or concrete surfaces. Using these weather data may cause serious errors. Satellite images on the other hand cover large cultivated areas and fields. Throughout the present study, Penman–Monteith equation was converted to a simple equation of three components for each component of which there was a linear regression presented with satellite input data. To create and test regression equations, 297 NOAA- AVHRR satellite images for duration of ten years were acquired. The present investigation has been carried out in Amirkabir irrigated unit in Khuzestan province. The results indicate that the simplified equation can estimate ETo with an R2 of 0.92 and an error of 8 percent. 

Keywords

Main Subjects


Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration. Guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy, 300 pp.
Blaney, H. F. and Criddle W. D. (1950). Determining water requirements in irrigated areas from climatological and irrigation data. Soil Conservation Service Technical  Paper 96. 44 pp.
Bussieres, N., Granger, R. J. and Strong, G. S. (1997). Estimates of regional evapotranspiration using GOES-7 satellite data: Saskatchewan case study, Canadian Journal of remote sensing, 23(1): 3-14.
Cambell, J.B. 1987. Introduction to Remote Sensing. The Guilford Press, New York.
Fisher, J. B., DeBiase, T., Qi, Y., Xu, M., and Goldstein, A. H. (2005). Evapotranspiration models compared on a Sierra Nevada forest ecosystem. Environmental Modeling and Software, 20: 783−796.
John G., Yuan D., Lunetta, R. S. and Elvidge, C. D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64(2), 143-150.
Julien, Y., J.A. Sobrino and W. Verhoef. 2006. Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sens. Environ. 103(1): 43-55.
Hargreaves, G. H. (1974). Estimation of potential and crop evapotranspiration. Transactions of the ASAE, 17(4): 701-704.
McDonald J.H. (2008). Handbook of Biological Statistics. Sparky House Publishing, Baltimore, Maryland, 287 pp.
Moran, M. S. and Jackson, R. D. (1991). Assessing the spatial distribution of evapotranspiration remotely sensed inputs. Journal of Environmental Quality, 20: 725-737.
Nishida, K., Nemani, R. R., Glassy, J. M., and Running, S. W. (2003). Development of an evapotranspiration Index Aqua/MODIS for Monitoring Surface Moisture Status. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 493−501.
Penman, H. L. (1948). Natural evaporation from open water, bare soil, and grass. Proceedings of the Royal Society of London, A193: 120-146.
Sheng, J., Wilson, J.P. and Lee, S. (2009). Comparison of land surface temperature (LST) modeled with a spatially distributed solar radiation model (SRAD) and remote sensing data. Environmental Modelling & software, 24, 436-433.
Temesgen, B., Allen, R.G. and Jensen, D.T (1999). Adjusting temperature parameters to reflect well-watered conditions. J. Irrig. ASCE, 125(1), 26-33.
Thornthwaite, C. W. (1948). An approach towards a rational classification of climate. Geographical Review, 38: 55-94.
Wang, K., Li, Z., & Cribb, M. (2006). Estimation of evaporative fraction from a combination of day and night land surface temperature and NDVI: A new method to determine the Priestly–Taylor parameter. Remote Sensing of Environment, 102, 293−305