Abstract. As the accuracy and sensitivity of remote-sensing satellites improve, there is an increasing demand for more accurate and updated base datasets for surveying and monitoring. However, differentiating rock outcrop from snow and ice is a particular problem in Antarctica, where extensive cloud cover and widespread shaded regions lead to classification errors. The existing rock outcrop dataset has significant georeferencing issues as well as overestimation and generalisation of rock exposure areas. The most commonly used method for automated rock and snow differentiation, the normalised difference snow index (NDSI), has difficulty differentiating rock and snow in Antarctica due to misclassification of shaded pixels and is not able to differentiate illuminated rock from clouds. This study presents a new method for identifying rock exposures using Landsat 8 data. This is the first automated methodology for snow and rock differentiation that excludes areas of snow (both illuminated and shaded), clouds and liquid water whilst identifying both sunlit and shaded rock, achieving higher and more consistent accuracies than alternative data and methods such as the NDSI. The new methodology has been applied to the whole Antarctic continent (north of 82°40′ S) using Landsat 8 data to produce a new rock outcrop dataset for Antarctica. The new data (merged with existing data where Landsat 8 tiles are unavailable; most extensively south of 82°40′ S) reveal that exposed rock forms 0.18 % (21 745 km2) of the total land area of Antarctica: half of previous estimates.

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A new method for modeling heat flux shows that the upper crust contributes up to 70% of the Antarctic Peninsula's subglacial heat flux and that heat flux values are more variable at smaller spatial resolutions than geophysical methods can resolve. Results indicate a higher heat flux on the east and south of the Peninsula (mean 81 mW m−2) where silicic rocks predominate, than on the west and north (mean 67 mW m−2) where volcanic arc and quartzose sediments are dominant. While the data supports the contribution of heat‐producing element‐enriched granitic rocks to high heat flux values, sedimentary rocks can be of comparative importance dependent on their provenance and petrography. Models of subglacial heat flux must utilize a heterogeneous upper crust with variable radioactive heat production if they are to accurately predict basal conditions of the ice sheet. Our new methodology and data set facilitate improved numerical model simulations of ice sheet dynamics.

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Abstract. Antarctic geothermal heat flow (GHF) affects the temperature of the ice sheet, determining its ability to slide and internally deform, as well as the behaviour of the continental crust. However, GHF remains poorly constrained, with few and sparse local, borehole-derived estimates and large discrepancies in the magnitude and distribution of existing continent-scale estimates from geophysical models. We review the methods to estimate GHF, discussing the strengths and limitations of each approach; compile borehole and probe-derived estimates from measured temperature profiles; and recommend the following future directions. (1) Obtain more borehole-derived estimates from the subglacial bedrock and englacial temperature profiles. (2) Estimate GHF from inverse glaciological modelling, constrained by evidence for basal melting and englacial temperatures (e.g. using microwave emissivity). (3) Revise geophysically derived GHF estimates using a combination of Curie depth, seismic, and thermal isostasy models. (4) Integrate in these geophysical approaches a more accurate model of the structure and distribution of heat production elements within the crust and considering heterogeneities in the underlying mantle. (5) Continue international interdisciplinary communication and data access.

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. West Baram Line (Figures 1 and 3). These "lines" are poorly understood features that have never 114 been rigorously defined. In early tectonic models (Hamilton, 1978; Hollaway; 1982; Daly et al. 115 1991) the lines are not featured; whereas some subsequent models interpret these lines as lying ANALYTICAL METHODS 187Appendix A summarizes our analytical techniques and results are given in Tables 1, 2 188 and 3. Age determinations used performed using the Ar-Ar method (University of Nevada Las Ar Age Determinations: Three samples were analyzed using conventional 214 furnace step-wise heating analyses on bulk mineral separates (Table 1). Although the samples 215 had U-shaped age spectra commonly associated with excess argon (Figure 6), stable plateau ages 216 11 could be determined for each sample; UP 7 yielded a 3 point isochron age. An isochron age is 217 the best estimate of the age of a sample, even if a plateau age is obtained. 218The age spectrum for the UP-4 biotite is characterized by high initial ages (step 2 ~6.2 219Ma) that decrease progressively to ages of ~4 Ma by ~10% gas released. This decline is 220 followed by a flat, concordant age spectrum for the remainder of the gas released. anomalously high. The possibility that the shape of this age spectrum is a result of excess argon 231 in the sample cannot be confirmed, as no isochron is defined by these data. The overall 232 concordant nature of the age spectrum and the observation that recoil artifacts are common in 233 biotites, the plateau age (3.90 ± 0.04 Ma) is considered the most reliable for this sample. 234The UP-8 biotite sample produced an age spectrum similar to the UP-4 biotite and is 235 interpreted similarly. The total gas age for this sample is 3.94 ± 0. Nd as a function of SiO 2 (Figure 10). Because such isotopic variations should not 307Specifically, we are able to address five important considerations: (1) age of magmatic activity, 308(2) nature of their source regions, (3) the nature of the subsurface crust via melt-crust interaction, 309(4) the relationship of Luconia to other crustal blocks, and (5) causes of volcanism. interpret these as two distinct magmatic episodes rather than eruption from a single stratified 318 magma chamber. First and foremost, the more evolved rocks are distinctly older than the basalts. 319The similar timing of basaltic volcanism at the Usun Apau, Linau Balui, and Nankan plateaus same arrays as the SSA (Figure 11) is strong evidence that the magmas from these regi...

Highlights (3 points, 85 characters each inc. spaces) (1) Mt Michael shows volcanic activity in all suitable satellite images from 1989-2018 (2) SWIR anomalies image a 110 m (±40 m) wide lava lake inside the crater from 2003-2018 (3) Unmixing the SWIR anomalies shows a component of magmatic temperatures (989-1279 °C)

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