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

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.

Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

“…Soil moisture is a critical component in land surface-atmospheric processes, and prolonged soil moisture deficits often lead to drought induced vegetation stress [8]. Pasture surface temperature retrieved from thermal infrared (T ir ) provides information about temperature extremes encountered during crop development in this region: extremely low in winter and extremely high in summer, and the consequences on stress and stomatal opening/closure process [19]. In the summer period, especially in the months of July and August, T ir reaches maximum values, while soil moisture has an opposite behaviour.…”

“…In comparison with the use of NDVI in pastures [10], these results suggest that NDWI has a more broad application potential, and is appropriate for monitoring drought stress in vegetation, through plant available water (SMC), light-harvesting capacity (biomass) or crop progress and vegetation health (PMC and PQDI, respectively) [19]. The response to the level of SMC and T ir is particularly important, since these parameters are drivers of dryland pasture growth [28].…”

Evaluating soil moisture estimation from ground‐penetrating radar hyperbola fitting with respect to a systematic time‐domain reflectometry data collection in a boreal podzolic agricultural field

“…TDR‐measured θ v in m 3 m −3 (min = 0.1034, max = 0.1798, and median = 0.1298; n = 21) and gravimetrically measured θ v in m 3 m −3 (min = 0.0907, max = 0.1618, and median = 0.1239; n = 21) were within an RMSE of 0.0161 m 3 m −3 (see also Figure ). Average soil bulk density ( n = 28) and the RMSE of θ v estimation ( n = 10) between TDR and gravimetric methods of a shallow soil were 1.31 g cm −3 and 0.018 m 3 m −3 , respectively, in a previous study conducted at the same site (Badewa et al, ). Table compares different TDR criteria used to average θ v measurements at a buried location.…”

“…TRIME‐PICO64 TDR probes provide reliable θ v estimations at point scale with an RMSE of 0.016 m 3 m −3 when compared with the gravimetric method. In addition, the TDR measurements are consistent as Badewa et al () reported similar accuracy (RMSE = 0.018 m 3 m −3 ) during the previous growing season at the same location. This accuracy has allowed TRIME‐PICO64 TDR probes (especially in sandy soils) to be used as the reference method of θ v estimation (IMKO Micromodultechnik, ).…”

“…Eroded and dissected lacustrine, glacial veneer, and organic deposits are dominant with a shallow unconfined sandy aquifer (Newfoundland & Labrador Geological Survey, ). Shallow soil at the adjacent cornfield is gravelly loamy sand (sand 82.0% [±3.4], silt 11.6% [±2.4], and clay 6.4% [±1.2]; Badewa et al, ). Soil textural analysis ( n = 8) at the experimental plot revealed that the shallow soil layer up to a depth of 50 cm is loamy sand, which is the same as the 0‐ to 15‐cm soil profile as described by Sadatcharam ().…”