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 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). Copyright © 2017 John Wiley & Sons, Ltd.

Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion

“…While numerous studies have been conducted with thermal inertia, the triangle approach and microwave to estimate SM from satellite and manned airborne remote sensing [11,36,63,64], only few studies have been conducted with UAS observations [4,17,19]. For instance, Sobrino et al (2012) used NDVI and T s with a polynomial formulation to predict SM and this approach can estimate SM with RMSDs of 0.05 m 3 •m −3 from observations from a manned airborne system [64].…”

“…However, studies on the quantification of SM from UAS imagery are relatively limited. Hassan-Esfahani et al (2015, 2017) conducted the first UAS studies to estimate SM from optical and thermal images by using an artificial neural network [4,17]. Results indicate that SM can be retrieved with root mean square deviations (RMSDs) equal to 0.05 m 3 •m −3 .…”

Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience

“…Fig. 3 shows a root zone soil volumetric water content (%) map (pixel-wise at 0.15m resolution) for the first AggieAir flight [7].…”

Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

“…The review of current literature indicates that soil water content (SWC) and grain yield relationship has not been thoroughly studied from a time and depth perspective, and least of all at the field scale. Hassan-Esfahani, et al [11] presented one of the few studies on the estimation of SWC at the root zone, using high resolution remote sensing data, and demonstrated the potential of vegetation indices to estimate SWC, but did not link SWC to crop yield. A recent review paper have identified the need for a combined soil-based and crop-based approach to better estimate crop water needs [12].…”