Remote sensing of evapotranspiration for irrigated crops at Yuma, Arizona, USA

A satellite-based vegetation index model that tracks daily crop growth and evapotranspiration (ETc) is developed, tested, and validated over irrigated farms in Yuma irrigation districts of Arizona and California. Model inputs are remotely sensed normalized difference vegetation index (NDVI) images, crop type maps, and local weather. The utility and novelty of the model is a more accurate assessment of ETc than currently provided by the US Bureau of Reclamation's evapotranspiration modeling system. The model analyzes NDVI time series data from the European Space Agency's Sentinel-2 satellites using the Google Earth Engine, constructs FAO-56 style crop growth stages from NDVI, and then estimates daily ETc using pre-defined crop coefficients (Kc) and grass reference evapotranspiration (ETos). Four crops were selected to test and evaluate model performance: short-season broccoli, mid-season cotton and wheat, and perennial alfalfa. Comparison of model results showed that Reclamation reports overestimate alfalfa and wheat ETc by 21-25%, cotton ETc by 6%, and underestimate broccoli ETc by 21%. Variability resolved by the model ranged 6-18% of median total ETc. Comparison of model results with those obtained from 13 eddy covariance sites showed validation discrepancies ranging 1-14%: average total actual ETc differences were 12, - 14, 78, and 87 mm/season, respectively, for alfalfa, broccoli, cotton, and wheat. The wide availability of Sentinel-2 data, collected every 5 days or less, and the rapid processing via Google Earth Engine make the vegetation index model implementation fast and practical. Its accuracy and ability to resolve ETc for every field would benefit the Reclamation water accounting system and provide valuable consumptive water use data for any Colorado River stakeholder.