Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil

Irrigated agriculture requires the implementation of innovative tools to improve irrigation water management and accurate estimation of actual evapotranspiration (ETa) such as remote sensing-based methodology. This study aimed to evaluate the irrigation management and estimating evapotranspiration through the geeSEBAL, a new tool for automated estimation of ETa based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the Calibration using Inverse Modeling at Extreme Conditions (CIMEC) process for the endmembers selection, implemented into the Google Earth Engine (GEE) environment. GeeSEBAL has not been used yet in Brazil for irrigation proposes, and in this research, it was applied to estimate ETa using Landsat images and ERA5-Land as meteorological inputs in the largest sugarcane producing region of the world in Brazil for two ratoon seasons by comparing daily ETa with values obtained from eddy covariance (EC) data, Energy balance components using geeSEBAL were consistent with the measured data and daily ETa presenting RMSE of 0.46 mm with R2 = 0.97. Modeled ETa and Kc were similar for the two seasons, although somewhat overestimated for the fifth ratoon when compared to the EC data, mainly during high atmospheric demand (crop mid-stage). Still, the Kc values were similar to the standard values available in the literature and measured flux tower data for the two ratoons seasons. With ETa from geeSEBAL it was possible to identify water stress over the growing seasons using the remote sensing-based soil water balance, which occurred mainly during the phase after the crop reached the peak Kc (full cover stage) when the irrigation depth required was very high. This analysis showed that geeSEBAL has a significant potential for assessment of ETa for irrigation monitoring and management, even in missing climate data areas, allowing important advances in water resources management for sugarcane and other irrigated crops at field or regional scales. •An open-source SEBAL framework within the GEE using ERA5 and automatic calibration was used to estimate ET for sugarcane.•Modeled energy balance components using the geeSEBAL agreed with the eddy covariance values, ETa presenting RMSE of 0.46 mm.•The RS-SWB for the two ratoons identified water stress. Modeled ETa was overestimated at high water atmospheric demand.•The geeSEBAL showed to be able to improve irrigation management for sugarcane in Brazil without using any ground data.