Introduction Now days it is noticed several occurrences about water shortage in agriculture, decreasing yield crops and profits. The irrigation system is a resource to aid the farmers to manage the system production to achieve a reasonable yield. Among several variables for a irrigation management, there are two of them with a major importance, the crop coefficient (kc) and Reference crop evapotranspiration (ETo) (Gondim, Teixeira, and Barbosa 2005). In the last decades the remote sensing techniques has been used to identify landscapes, soil classes, and water energy balance as well, providing conditions to analyze data in a regional scale. By means orbital sensors and algorithms to convert digital numbers to reflectance and radiation flux, remotesensing methods to predict the evapotranspiration is a important tool to handle the hydrological cycle (Bernardo, Soares, and Mantovani 2006). The SEBAL (Surface Energy Balance Algorithm for Land) uses the surface energy balance to predict some hydrological features (evapotranspiration, water deficit, etc) and Its main creator is Professor Wim G. M. Bastiaanssen (W G M Bastiaanssen et al. 1998). That method has been validated under several conditions for different locations ((W G M Bastiaanssen 2000),(Wim G. M. Bastiaanssen, Ahmad, and Chemin 2002), (Santos, Fontana, and Alves 2010)). There are several available algorithms to evapotranspiration prediction ((Wolff 2016), (Cavalcante et al. 2016), (Hessels et al. 2017)). However is missing a specific code to run on python 3 and GRASS 74. Thus we aimed to implement SEBAL model for Landsat 8 iamges using the language Python version 3 (Rossum 1995) and GRASS version 7.4 (Neteler et al. 2012). References Bastiaanssen, W G M. 2000. “SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey.” Journal of Hydrology 229 (1): 87–100. doi:https://doi.org/10.1016/S0022-1694(99)00202-4. Bastiaanssen, W G M, M Menenti, R A Feddes, and A A M Holtslag. 1998. “A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation.” Journal of Hydrology 212-213: 198–212. doi:https://doi.org/10.1016/S0022-1694(98)00253-4. Bastiaanssen, Wim G. M., Mobin-ud-Din Ahmad, and Yann Chemin. 2002. “Satellite surveillance of evaporative depletion across the Indus Basin.” Water Resources Research 38 (12): 9–1–9–9. doi:10.1029/2001WR000386. Bernardo, Salassier, Antonio Alves Soares, and Everardo Chartuni Mantovani. 2006. Manual de irrigação. 8th ed. Viçosa: UFV. Cavalcante, Lucas Barbosa, Aline da Silva Inácio, Heliofábio Gomes Barros, Jiménez, Rosilene Mendonça Nicácio, and Simone Marilene Sievert da Costa Coelho. 2016. “Cálculo do saldo de radiação pelo algoritmo sebal na porção do baixo-médio São Francisco, Brasil, utilizando um software de código livre.” Revista Brasileira de Cartografia, no. 68: 1515–29. Gondim, R., A. d. S. Teixeira, and F. Barbosa. 2005. “Novo paradigma para a água e coeficientes de cultivos aplicados à gestão de recursos hídricos em nível de bacia hidrográfica.” Revista Item Irrigação E Tecnologia, no. 67: 14–18. Hessels, Tim, Jonna van Opstal, Patricia Trambauer, Wim Bastiaanssen, Mohamed Faouzi Smiej, Yasir Mohamed, and Ahmed Er-Raji. 2017. “pySEBAL_3.3.8.” https://github.com/wateraccounting/SEBAL/blob/master/pySEBAL/pySEBAL{\_}code.py. Neteler, M, M H Bowman, M Landa, and M Metz. 2012. “GRASS GIS: a multi-purpose Open Source GIS.” Environmental Modelling & Software 31. Elsevier: 124–30. doi:10.1016/j.envsoft.2011.11.014. Rossum, G van. 1995. “Python tutorial.” CS-R9526. Amsterdam: Centrum voor Wiskunde en Informatica (CWI). Santos, Thiago Veloso dos, Denise Cybis Fontana, and Rita Cássia Marques Alves. 2010. “Avaliação de fluxos de calor e evapotranspiração pelo modelo SEBAL com uso de dados do sensor ASTER.” Pesquisa Agropecuária Brasileira 45 (5). scielo: 488–96. doi:10.1590/S0100-204X2010000500008. Wolff, Wagner. 2016. “wwolff7/SEBAL_GRASS.” doi:10.5281/zenodo.167350.

Available in GITHUB https://github.com/rafatieppo/SEBAL