Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

Emmanuel Badewa,Lakshman Galagedara,Mumtaz Cheema,Adrian Unc,Vanessa Kavanagh

doi:10.3390/agronomy8100224

Abstract

Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn’s Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale.

Highlights

Development of site-specific management (SSM) over large fields is the goal of precision agriculture (PA)
Lesch et al [3] have shown that different types of spatial information such as soil texture and salinity derived from bulk apparent electrical conductivity (ECa ) obtained by electromagnetic induction (EMI)
Accuracy of the HD2-time domain reflectometry (TDR) for the 16 cm probe length is similar to the root mean square error (RMSE) of 0.013 m3 m−3 by Topp et al [11] while the HD2-TDR for the 11 and 30 cm probe lengths has RMSE values of 0.040 m3 m−3 and 0.018 m3 m−3, respectively (Figure 2 and Table 1)

Summary

Introduction

Development of site-specific management (SSM) over large fields is the goal of precision agriculture (PA). PA encompasses the use of spatial and temporal information to support decisions on agronomic practices that best match soil and crop requirements as they vary in the field [1,2]. Surveys can offer significant support to the development of accurate management decisions for agricultural fields. It includes all those agricultural production practices that use information technology either to tailor input to achieve desired outcomes, or to monitor those outcomes (e.g., variable rate application of pesticides and fertilizers) [4]. The measurement of ECa using the EMI technology has been proposed as an effective and rapid response methodology in support of PA [5,6]

Objectives

Results

Discussion

Conclusion