Estimation of real evapotranspiration (ETR) and potential evapotranspiration (ETP) in the southwest of the Buenos Aires Province (Argentina) using MODIS images

F. Marini, M. Santamaría, P. Oricchio, C. M. Di Bella, A. Basualdo

Abstract

Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.


Keywords

real evapotranspiration; potential evapotranspiration; MODIS; surface temperature; NDVI

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References

Aguirre Rodríguez, A. 2014. Estimación de la evapotranspiración mediante imágenes satelitales en la cuenca del río La Sierra, Chiapas y Tabasco, México. Tesis (Maestría en Ciencias, especialista en Hidrociencias) - Colegio de Postgraduados.

Allen, R., Pereira, L., Raes, D., Smith. M. 1998. Crop evapotranspiration: guidelines for computing crop water requeriments. FAO Irrigation and Drainage, Paper No. 56.

Allen, R.G., Tasumi, M., Morse, A., Trezza, R., Wright, J.L., Bastiaanssen, W., Kramber, W., Lorite, I. Robison, C.W. 2007. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC) Applications. J. of Irrigation Drainage Engineering -ASCE, 133(4), 395-40. https://doi.org/10.1061/(ASCE)0733- 9437(2007)133:4(395)

Bastiaanssen, W. 1995. Regionalization of surface flux desities and moisture indicators in composite terrain: a remote sensing approach under clear skies in Mediterranean climates. Land bouw universiteit te Wageningen, 109, 273.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). Journal of Hydrology, 212-213, 198-212. https://doi. org/10.1016/S0022-1694(98)00253-4

Bastiaanssen, W.G.M. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 222, 87-100. https:// doi.org/10.1016/S0022-1694(99)00202-4

Blaney, H.F. y Criddle, W.D. 1950. Determining Water Requirements in Irrigated Area from Climatological Irrigation Data. US Department of Agriculture, Soil Conservation Service, Tech. Pap. Nº. 96, 48 pp.

Burnham, K.P., Anderson, D.R. 2002. Model Selection and Multimodel Inference - A Practical InformationTheoretic Approach. New York: Springer-Verlag.

Caselles, V., Delegido, J., Sobrino, J.A., Hurtado, E. 1992. Evaluation of the maximum evapotranspiration over the La Mancha region, Spain, using NOAAA-VHRR data. Internacional Journal of Remote Sensing, 13(5), 939-946. https:// doi.org/10.1080/01431169208904167

Caselles,V., Artigao, M., Hurtado, E., Coll, C., Brasa, A. 1998. Mapping actual evapotranspiration by combining Landsat TM and NOAA-AVHRR images: application to the Barrax area, Albacete, Spain. Remote Sensing of Environment, 63(1), 1-10. https://doi.org/10.1016/S0034-4257(97)00108-9

Castañeda-Ibáñez, C., Martínez-Menes, M., PascualRamírez, F., Flores-Magdaleno, H., FernándezReynoso, D. y Esparza-Govea, S. 2015. Estimación de coeficientes de cultivo mediante sensores remotos en el distrito de riego río Yaqui, Sonora, México. Agrociencia, 49, 221-232.

Castellvi, F., Snyder, R.L. 2010. A new procedure to estimate sensible heat flux using surface renewal analysis. A case study over grapevines. Journal of Hydrometeorology, 11(2), 496-508. https://doi. org/10.1175/2009JHM1151.1

Cihlar, J., St. Laurent, L., Dyer, J. 1991. Relation between the Normalized Difference Vegetation Index and ecological variables. Remote Sensing of Environment, 35(2-3), 279-298. https://doi. org/10.1016/0034-4257(91)90018-2

Di Bella, C., Rebella, C., Paruelo, J. 2000. Evapotranspiration estimates using NOAAAVHRR imagery in the Pampa region of Argentina. International Journal of Remote Sensing, 21(4), 791-797. https://doi.org/10.1080/014311600210579

Evett, S.R., Kustas, W.P., Gowda, P.H., Anderson, M.C., Prueger, J.H., Howell, T.A. 2012. Overview of the Bushland Evapotranspiration and Agricultural Remote Sensing EXperiment 2008 (BEAREX08): A field experiment evaluating methods for quantifying ET at multiple scales. Advances in Water Resources, 50, 4-19. https://doi.org/10.1016/j. advwatres.2012.03.010

Gao, B.C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266. https://doi.org/10.1016/S0034- 4257(96)00067-3

García, A.G., Campos, A.N., Di Bella, C.M. y Posse Beaulieu, G. 2013. Evolución de la evapotranspiración en diferentes coberturas vegetales de la Argentina utilizando productos derivados del sensor MODIS. INTA – Instituto Nacional de Tecnología Agropecuaria. Artículo de divulgación. Disponible en http://inta.gob.ar/ documentos/evolucion-de-la-evapotranspiracionen-diferentes-coberturas-vegetales-de-la-argentinautilizando-productos-derivados-del-sensor-modis

Grassi C. I. 1964. Estimation of evapotraspiration from climatic formulas. Master of Science Thesis, Utah State University, 101, Utah, EE.UU.

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. y Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034- 4257(02)00096-2

Kustas, W. P. and Norman, J. M. 1997. A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations, Water Resources Research, 33(6), 1495-1508. https://doi. org/10.1029/97WR00704

Mora, F., Iverson, L. 1998. On the sources of vegetation activity variation, and their relation with water balance in Mexico. Internacional Journal of Remote Sensing, 19(10), 1843-1871. https://doi. org/10.1080/014311698215027

Mulleady, C., Barrera, D. 2013. Estimación de la tasa de evapotranspiración a partir de datos satelitales. Meteorológica, 38(1), 21-39.

Penman-Monteith – FAO. 1998. Crop evapotranspiration – Guidelines for computing crop water requirements – FAO irrigation and drainage paper 56, Food and agriculture Organization of the United Nations. Rome.

Sánchez, M. 2000. Características y apreciaciones generales de los métodos de medida y estimación de evapotranspiración. Revista de Geografía Norte Grande, 27, 27-36.

Sánchez, M., Chuvieco, E. 2000. Estimación de evapotranspiración del cultivo de referencia, ET0, a partir de imágenes NOAA-AVHRR. Revista de Teledetección, 14, 11-21. Disponible en: http://www. aet.org.es/revistas/revista14/AET14_2.pdf [Último acceso: junio 2017].

Seguin, B. 1993. NOAA-AVHRR data for crop monitoring at a regional level: posibilities and limits in the European context. EARSel - Advances in Remote Sensing, 2(2), 87-93.

Snipes, M., Taylor, C.D. 2014. Model selection and Akaike Information Criteria: An example from wine ratings and prices. Wine Economics and Policy, 3(1), 3-9. https://doi.org/10.1016/j.wep.2014.03.001

Tasumi, M., Bastiaanssen, W.G.M., Allen, R.G. 2000. Application of the SEBAL methodology for estimating consumptive use of water and stream flow depletion in the Bear River Basin of Idaho through remote sensing. Idaho Department of Water Resources, University of Idaho, Department of Biological and Agricultural Engineering.

Thornthwaite C.W., Mather, J.R. 1955. The water balance. Publications in climatology. Centerton, NJ: Drexel Institute of Technology. Vol. VIII, Nº. 1.

Wan, Z. 2008. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sensing of Environment, 112(1), 59-74. https://doi.org/10.1016/j.rse.2006.06.026

Wang, W., Liang, S., Meyers, T. 2008. Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sensing of Environment, 112(3), 623-635. https:// doi.org/10.1016/j.rse.2007.05.024

Wiegand, C., Richardson, A. 1990. Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield, I. Rationale. Agronomy Journal, 82, 623-629. https://doi.org/10.2134/agronj 1990.00021962008200030037x

Yang, W., Yang, L., Merchant, J. 1997. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. Internacional Journal of Remote Sensing, 18(10), 2161-2180. https://doi. org/10.1080/014311697217819

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