Andreas Pfaffling

Airborne electromagnetic footprints in 1D earths

Existing estimates of footprint size for airborne electromagnetic (AEM) systems have been based largely on the inductive limit of the response. We present calculations of frequency-domain, AEM-footprint sizes in infinitehorizontal, thin-sheet, and half-space models for the case of finite frequency and conductivity. In a half-space the original definition of the footprint is extended to be the side length of the cube with its top centered below the transmitter that contains the induced currents responsible for 90% of the secondary field measured at the receiver. For a horizontal, coplanar helicopter frequency-domain system, the in-phase footprint for induction numbers less than 0.4 (thin sheet) or less than 0.6 (half-space) increases from around 3.7 times the flight height at the inductive limit to more than 10 times the flight height. For a vertical-coaxial system the half-space footprint exceeds nine times the flight height for induction numbers less than 0.09. For all models, geometries, and frequencies, the quadrature footprint is approximately half to two-thirds that of the in-phase footprint. These footprint estimates are supported by 3D model calculations that suggest resistive targets must be separated by the footprint dimension for their individual anomalies to be resolved completely. Analysis of frequency-domain AEM field data acquired for antarctic sea-ice thickness measurements supports the existence of a smaller footprint for the quadrature component in comparison with the in-phase, but the effect is relatively weak. In-phase and quadrature footprints estimated by comparing AEM to drillhole data are considerably smaller than footprints from 1D and 3D calculations. However, we consider the footprints estimated directly from field data unreliable since they are based on a drillhole data set that did not adequately define the true, 3D, sea-ice thickness distribution around the AEM flight line.

Direct helicopter EM — Sea-ice thickness inversion assessed with synthetic and field data

Accuracy and precision of helicopter electromagneticHEM sounding are the essential parameters for HEM seaicethickness profiling. For sea-ice thickness research, thequality of HEM ice thickness estimates must be better than10 cm to detect potential climatologic thickness changes.Weintroduce and assess a direct, 1D HEM data inversion algorithmfor estimating sea-ice thickness. For synthetic qualityassessment, an analytically determined HEM sea-ice thicknesssensitivity is used to derive precision and accuracy. Precisionis related directly to random, instrumental noise, althoughaccuracy is defined by systematic bias arising fromthe data processing algorithm. For the in-phase component ofthe HEM response, sensitivity increases with frequency andcoil spacing, but decreases with flying height. For small-scaleHEM instruments used in sea-ice thickness surveys, instrumentalnoise must not exceed 5 ppm to reach ice thicknessprecision of 10 cm at 15-m nominal flying height. Comparableprecision is yielded at 30-m height for conventional explorationHEM systems with bigger coil spacings. Accuracylosses caused by approximations made for the direct inversionare negligible for brackish water and remain better than10 cm for saline water. Synthetic precision and accuracy estimatesare verified with drill-hole validated field data fromEast Antarctica, where HEM-derived level-ice thicknessagrees with drilling results to within 4%, or 2 cm.

ARISE (Antarctic Remote Ice Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-ice Data Product

Abstract Preliminary results are presented from the first validation of geophysical data products (ice concentration, Snow thickness on Sea ice (hs) and ice temperature (TI) from the NASA EOS Aqua AMSR-E Sensor, in East Antarctica (in September–October 2003). The challenge of collecting Sufficient measurements with which to validate the coarse-resolution AMSR-E data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other Satellite data. Initial results from a circumnavigation of the experimental Site indicate that, at least under cold conditions with a dry Snow cover, there is a reasonably close agreement between Satellite- and aerial-photo-derived ice concentrations, i.e. 97.2±3.6% for NT2 and 96.5±2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the AMSR-E concentration represents a Slight overestimate of the actual concentration, with the largest discrepancies occurring in regions containing a relatively high proportion of thin ice. The AMSR-E concentrations from the NT2 and BBA algorithms are Similar on average, although differences of up to 5% occur in places, again related to thin-ice distribution. The AMSR-E ice temperature (TI) product agrees with coincident Surface measurements to approximately 0.5˚C in the limited dataset analyzed. Regarding Snow thickness, the AMSR hs retrieval is a Significant underestimate compared to in situ measurements weighted by the percentage of thin ice (and open water) present. For the case Study analyzed, the underestimate was 46% for the overall average, but 23% compared to Smooth-ice measurements. The Spatial distribution of the AMSR-E hs product follows an expected and consistent Spatial pattern, Suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is highlighted.

In situ measurements of the direct-current conductivity of Antarctic sea ice: implications for airborne electromagnetic sounding of sea-ice thickness

Abstract Airborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

Retrieval of thin-ice thickness using the L-band polarization ratio measured by the helicopter-borne scatterometer HELISCAT

Abstract Climate warming makes an increasing thin-ice fraction likely to occur in the Arctic, underpinning the need for its regular observation. synchronous helicopter-borne measurements of the sea-ice thickness and like-polarized L-band radar backscatter carried out along identical flight tracks north of svalbard during winter are combined to develop an algorithm to estimate the thin-ice thickness solely from the L-band backscatter co-polarization ratio (LCPR). Airborne ice-thickness and LCPR data are smoothed along track (to reduce noise), co-located and compared. A linear and a logarithmic fit are applied using thickness values between 0.0 and 0.6m and 0.0 and 1.0 m, respectively. The thin-ice thickness is derived from the LCPR data using these fits, first for dependent data (used to obtain the fits) and subsequently for independent data. The results are compared to airborne ice-thickness measurements for ice-thickness values between 0.0 and 0.6m using linear regression. The logarithmic fit gives the most reliable results, with a correlation of 0.72 and a rms difference of 8 cm. It permits us to derive the thickness of thin ice (below 50–60cm thickness) from airborne LCPR data with an uncertainty of about 10 cm.