New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.

Investigation of Artifacts and Optimization in Proton Resonance Frequency Thermometry Towards Heating Risk Monitoring of Implantable Medical Devices in Magnetic Resonance Imaging.

OBJECTIVE: Artifacts limit the application of proton resonance frequency (PRF) thermometry for on-site, individualized heating evaluations of implantable medical devices such as deep brain stimulation (DBS) for use in magnetic resonance imaging (MRI). Its properties are unclear and the research on how to choose an unaffected measurement region is insufficient. METHODS: The properties of PRF signals around the metallic DBS electrode were investigated through simulations and phantom experiments considering electromagnetic interferences from material susceptibility and the radio frequency (RF) interactions. A threshold method on phase difference Δϕ was used to define a measurement area to estimate heating at the electrode surface. Its performance was compared to that of the Bayesian magnitude method and probe measurements. RESULTS: The B0 magnetic field inhomogeneity due to the electrode susceptibility was the main influencing factor on PRF compared to the RF artifact. Δϕ around the electrode followed normal distribution but was distorted. Underestimation occurred at places with high temperature rises. The noise was increased and could be well estimated from magnitude images using a modified NEMA method. The Δϕ-threshold method based on this knowledge outperformed the Bayesian magnitude method by more than 42% in estimation error of the electrode heating. CONCLUSION: The findings favor the use of PRF with the proposed approach as a reliable method for electrode heating estimation. SIGNIFICANCE: This study clarified the influence of device artifacts and could improve the performance of PRF thermometry for individualized heating assessments of patients with implants under MRI.