Modeling corn evapotranspiration using the SEBAL algorithm in the Peruvian highlands

Abstract

The estimation of evapotranspiration (ET) and crop water requirements are crucial for the proper management and allocation of water resources in terms of quantity, quality, and timeliness. Therefore, remote sensing estimation of ET using the SEBAL algorithm (Surface Energy Balance Algorithm for Land) can provide spatio-temporal, non-punctual data, unlike traditional calculations relying on the nearest meteorological station. This research analyzed ET using SEBAL, based on ten Landsat 8 satellite images processed with a program developed in the Model Builder of ArcGis^® version 10.2. The analysis was conducted during the vegetative period of starchy corn from May to October 2016. Validation of the results happened with a drainage lysimeter installed in a monitoring plot. Additionally, the statistical indices – such as percentage relative error (PRE) (0,09), root mean square error (RMSE) (0,30), R² (0,92), and Nash-Sutcliffe efficiency (NASH) (0,91) – indicated a good correlation of ET for starchy corn in the central highlands of Peru. The ET identified at ten monitoring points ranged from 1,05 to 7,79 mm d^-1.