The Cosmic-Ray Neutron Rover - Mobile Surveys of Field Soil Moisture and the Influence of Roads

TL;DR

This study investigates how roads influence cosmic-ray neutron sensing measurements of soil moisture and develops correction methods to improve accuracy in field surveys.

Contribution

The paper quantifies road effects on CRNS soil moisture measurements and introduces correction functions to mitigate bias, enhancing the method's practical application.

Findings

Roads can cause up to 40% overestimation of soil moisture.

Bias diminishes beyond a few meters from roads.

Corrective functions effectively remove road-induced bias.

Abstract

Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic-Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture non-invasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that the presence of roads introduces a bias in the CRNS estimation of field soil moisture compared to non-road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Measurements from the road could overestimate the field value by up to 40 % depending on road material, width, and the surrounding field water content. The bias could be successfully removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used on-the-fly without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road-effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, any measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture.