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Evapotranspiration (ET) estimation is important in precision agriculture water management, such as evaluating soil moisture, drought monitoring, and assessing crop water stress. As a traditional method, evapotranspiration estimation using crop coefficient ( K   c  ) has been commonly used. Since there are strong similarities between the  K   c   curve and the vegetation index curve, the crop coefficient  K   c   is usually estimated as a function of the vegetation index. Researchers have developed linear regression models for the  K   c   and the normalized difference vegetation index (NDVI), usually derived from satellite imagery. However, the spatial resolution of the satellite image is often insufficient for crops with clumped canopy structures, such as vines and trees. Therefore, in this article, the authors used Unmanned Aerial Vehicles (UAVs) to collect high-resolution multispectral imagery in a pomegranate orchard located at the USDA-ARS, San Joaquin Valley Agricultural Sciences Center, Parlier, CA. The  K   c   values were measured from a weighing lysimeter and the NDVI values were derived from UAV imagery. Then, the authors established a relationship between the NDVI and  K   c   by using a linear regression model and a stochastic configuration networks (SCN) model, respectively. Based on the research results, the linear regression model has an  R  2  of 0.975 and RMSE of 0.05. The SCN regression model has an  R  2  and RMSE value of 0.995 and 0.046, respectively. Compared with the linear regression model, the SCN model improved performance in predicting  K   c   from NDVI. Then, actual evapotranspiration was estimated and compared with lysimeter data in an experimental pomegranate orchard. The UAV imagery provided a spatial and tree-by-tree view of  ET  distribution.

Estimating Evapotranspiration of Pomegranate Trees Using Stochastic Configuration Networks (SCN) and UAV Multispectral Imagery