Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery
Abstract
Soil moisture is an important parameter in irrigation scheduling and application. Knowledge of root zone volumetric water con-tent can support decisions for more efficient irrigation management by enabling estimation of required water application ratesat appropriate temporal and spatial scales. The study presented here proposes a data mining approach that combines knownfield conditions with remote sensing observations to provide probabilistic estimates of root zone soil moisture at three differentdepths in the root zone soil pro file. Bayesian data mining algorithms were tested and calibrated to combine the remotely sensedspatial information with field measurements. The remote sensing platform used in this study, called AggieAir™,isanunmanned autonomous aerial system developed by the Utah Water Research Laboratory at Utah State University. Finally, atrapezoidal integration method was used to estimate volumetric water content (VWC) in the root zone using the results ofthe modelling approach at three different depths in the root zone soil profile. The results show the model could estimate theroot zone VWC with good accuracy (RMSE =0.05, MAE = 0.04, r = 0.97, e = 0.92, R2= 0.94).
