Together, the three particle size fractions (PSFs) of clay, silt, and sand are the most fundamental soil properties because the relative abundance influences the physical, chemical, and biological activities in soil. Unfortunately, determining PSFs requires a laboratory method which is time-consuming. One way to add value is to use digital soil mapping, which relies on empirical models, such as multiple linear regression (MLR), to couple ancillary data to PSFs. This approach does not account for the special requirements of compositional data. Here, ancillary data were coupled, via MLR modelling, to additive log-ratio (ALR) or isometric log-ratio (ILR) transformations of the PSFs to meet these requirements. These three approaches (MLR vs. ALR-MLR and ILR-MLR) were evaluated along with the use of different ancillary data that included proximally sensed gamma-ray spectrometry, electromagnetic induction, and elevation data. In addition, how the prediction might be improved was examined using ancillary data that was measured on transects and was compared to data interpolated from transects spaced far apart. Although the ALR-MLR approach did not produce significantly better results, it predicted soil PSFs that summed to 100 and had the advantage of interpreting the ancillary data relative to the original coordinates (i.e. clay, silt, and sand). For the prediction of PSFs at various depths, all ancillary data were useful. Elevation and gamma-ray data were slightly better for topsoil and elevation and electromagnetic (EM) data were better for subsoil prediction. In addition, a smaller transect spacing (26 m) and number of samples (9–16) might be adopted for mapping soil PSFs and soil texture across the study field. The ALR-MLR approach can be applied elsewhere to map the spatial distribution of clay minerals.

Arctic sea ice is going through a dramatic change in its extent and volume at an unprecedented rate. Sea-ice thickness (SIT) is a controlling geophysical variable that needs to be understood with greater accuracy. For the first time, a SIT-retrieval method that exclusively uses only airborne SIT data for training the empirical algorithm to retrieve SIT from Soil Moisture Ocean Salinity (SMOS) brightness temperature (TB) at different polarization is presented. A large amount of airborne SIT data has been used from various field campaigns in the Arctic conducted by different countries during 2011–15. The algorithm attempts to circumvent the issue related to discrimination between TB signatures of thin SIT versus low sea-ice concentration. The computed SIT has a rms error of 0.10 m, which seems reasonably good (as compared to the existing algorithms) for analysis at the used 25 km grid. This new SIT retrieval product is designed for direct operational application in ice prediction/climate models.

An important attribute for soil use is clay content, because it affects the water-holding capacity and hydraulic properties of a soil. Soil surveys are time-consuming, labour-intensive and costly, while geophysical methods, such as Electromagnetic Induction (EMI) and Ground Penetrating Radar (GPR), offer a non-invasive and non-destructive approach to mapping soil features. The objective of this paper is to assess the spatial relationship between clay content and geophysical data. The EMI and GPR data and soil cores were collected and analysed in a field of about 3 ha size in Rutigliano, Bari, in SE Italy. The EMI data and clay contents were interpolated using ordinary cokriging. The GPR data were pre-processed and the envelope of the filtered GPR data was used to produce 3Dmaps of the kriged estimates. The correlation with clay content was large and positive for EMI, whereas it was negative for the GPR measurement. In this work, a combination of geostatistical and statistical analysis has shown a significant correlation between the EMI and GPR observations. Estimation of the mathematical function relating these two groups of variables requires a multivariate approach of non-stationary geostatistics.

Knowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

Subglacial hydrologic systems in Antarctica and Greenland play a fundamental role in ice-sheet dynamics, yet critical aspects of these systems remain poorly understood due to a lack of observations. Ground-based electromagnetic (EM) geophysical methods are established for mapping groundwater in many environments, but have never been applied to imaging lakes beneath ice sheets. Here, we study the feasibility of passive- and active-source EM imaging for quantifying the nature of subglacial water systems beneath ice streams, with an emphasis on the interfaces between ice and basal meltwater, as well as deeper groundwater in the underlying sediments. We describe a suite of model studies that exam the data sensitivity as a function of ice thickness, water conductivity and hydrologic system geometry for models representative of a subglacial lake and a grounding zone estuary. We show that EM data are directly sensitive to groundwater and can image its lateral and depth extent. By combining the conductivity obtained from EM data with ice thickness and geological structure from conventional geophysical techniques, such as ground-penetrating radar and active seismic surveying, EM data have the potential to provide new insights on the interaction between ice, rock and water at critical ice-sheet boundaries.

In this paper we explore simulated responses of electromagnetic (EM) signals relative to in situ field surveys and quantify the effects that different values of conductivity in sea ice have on the EM fields. We compute EM responses of ice types with a three-dimensional (3-D) finite-volume discretization of Maxwell’s equations and present 2-D sliced visualizations of their associated EM fields at discrete frequencies. Several interesting observations result: First, since the simulator computes the fields everywhere, each gridcell acts as a receiver within the model volume, and captures the complete, coupled interactions between air, snow, sea ice and sea water as a function of their conductivity; second, visualizations demonstrate how 1-D approximations near deformed ice features are violated. But the most important new finding is that changes in conductivity affect EM field response by modifying the magnitude and spatial patterns (i.e. footprint size and shape) of current density and magnetic fields. These effects are demonstrated through a visual feature we define as ‘null lines’. Null line shape is affected by changes in conductivity near material boundaries as well as transmitter location. Our results encourage the use of null lines as a planning tool for better ground-truth field measurements near deformed ice types.

To date, the technique of inductive power transfer has found applications in industry including two-dimensional battery charging. However, this restricts any load to planar movements. This paper proposes custom designed magnetic structures of a loosely magnetically coupled three-dimensional inductive power transfer system. This is done via computational software utilizing the finite-element-method. More specifically, single-phase and multi-phase primary magnetic structures are proposed to distribute a power transfer window along three orthogonal axes. Next, a secondary magnetic structure is custom designed to induce electromotive force in three-dimensions. The proposed system is simulated to demonstrate power transfer for charging an AA-battery cell. Finally, the thermal effects upon the secondary load are considered.