Estimating Completely Remote Sensing-Based Evapotranspiration for Salt Cedar (Tamarix ramosissima), in the Southwestern United States, Using Machine Learning Algorithms

Accurate estimation of evapotranspiration (ET) is a prerequisite for water management in arid regions. Field based methods estimate point-wise ET accurately, but the challenge is in estimating ET over a region with high accuracies. Machine learning based approaches were taken to estimate ET over a large spatial scale using the Bowen Ratio Energy Balance (BREB) technique. The BREB method depends on terrestrial energy balance equations to estimate ET. Thus, remote sensing-based parameters representing variables in the energy balance equation, and vegetation index representing plant health conditions were used in the model. The study was conducted in the arid areas of the southwestern United States, where dense patches of Salt cedar consume water from the primary water source. The preliminary model used enhanced vegetation index (EVI), global horizontal irradiance (GHI), surface temperature (TS), and relative humidity (RH) as parameters. The k-nearest neighbor method consistently generated poor accuracies. When all the parameters were used, accuracies of the other models varied within 90-94%. When one predictor parameter was dropped, the best model produced accuracies between 90 to 93%, which dropped to 87-92% when a second variable was dropped. Random forest and support vector machine with radial kernel consistently produced the best predictive accuracies.

Automated summary

This study used machine learning approaches and remote sensing data to estimate evapotranspiration in arid regions. The results show that using multiple parameters can improve the accuracy, and random forest and support vector machine with radial kernel consistently produced the best predictive accuracies.