Application of the METRIC model to estimate Maize crop evapotranspiration at field scale with Google Earth Engine

Abstract

Determination of actual crop evapotranspiration (ETc) is a crucial challenge for sustainable irrigation water management. In this sense, robust and accurate estimation models of crop water consumption along with spatial tools and processing platforms in the cloud are necessary to determine the timing and amount of irrigation needed as a first step toward proposing solutions and water use efficiency. The objective of this study was to determine maize crop evapotranspiration using the algorithms of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model in the Google Earth Engine (GEE) platform. The crop was monitored with 14 Landsat images during its growth period. ETc values with METRIC were compared with ETc obtained with the FAO-56 methodology, and the cumulative ETc was compared with ETc derived from a soil moisture sensor. The evaluation between the METRIC model and FAO-56 displayed a determination coefficient (R2) of 0.87, mean squared error (MSE) of 0.8 mm/day, and bias percentage (PBIAS) of -14.5. According to the cumulative ETc, the difference was 16 mm for METRIC and 63 mm for FAO-56, compared with moisture sensor values. METRIC overestimated by 3.0% (PBIAS=-3.0), and FAO-56 underestimated by 11.9% (PBIAS=11.9). The results and the programmed algorithms in this work can be the basis for future calibrations and validations of the evapotranspiration of different crops.

Keywords

• evapotranspiration
• google earth engine
• fao-56
• energy balance
• soil moisture