Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery

Soil moisture is an important parameter in irrigation scheduling and application. Knowledge of root zone volumetric water content can support decisions for more efficient irrigation management by enabling estimation of required water application rates at appropriate temporal and spatial scales. The study presented here proposes a data mining approach that combines known field conditions with remote sensing observations to provide probabilistic estimates of root zone soil moisture at three different depths in the root zone soil profile. Bayesian data mining algorithms were tested and calibrated to combine the remotely sensed spatial information with field measurements. The remote sensing platform used in this study, called AggieAir™, is an unmanned autonomous aerial system developed by the Utah Water Research Laboratory at Utah State University. Finally, a trapezoidal integration method was used to estimate volumetric water content (VWC) in the root zone using the results of the modelling approach at three different depths in the root zone soil profile. The results show the model could estimate the root zone VWC with good accuracy (RMSE =0.05, MAE = 0.04, r = 0.97, e = 0.92, R2 = 0.94).