This report describes the results of an extended national field spectroscopy campaign designed to validate the Landsat 8 and Sentinel 2 Analysis Ready Data (ARD) surface reflectance (SR) products generated by Digital Earth Australia. Field spectral data from 55 overpass coincident field campaigns have been processed to match the ARD surface reflectances. The results suggest the Landsat 8 SR is validated to within 10%, the Sentinel 2A SR is validated to within 6.5% and Sentinel 2B is validated to within 6.8% . Overall combined Sentinel 2A and 2B are validated within 6.6% and the SR for all three ARD products are validated to within 7.7%.

Geoscience Australia and Monash University have produced a series of renewable energy capacity factor maps of Australia. Solar photovoltaic, concentrated solar power, wind (150 metre hub height) and hybrid wind and solar capacity factor maps are included in this dataset. All maps are available for download in geotiff format. Solar Photovoltaic capacity factor map The minimum capacity factor is <10% and the maximum is 25%. The map is derived from Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). Concentrated Solar Power capacity factor map The minimum capacity factor is 52% and the maximum is 62%. The map is derived from Bureau of Meteorology (2020) data. Minimum exposure cut-off values used are from International Renewable Energy Agency (2012) and Wang (2019). The scientific colour map is sourced from Crameri (2018). Wind (150 m hub height) capacity factor map The minimum capacity factor is <15% and the maximum is 42%. The map is derived from Global Modeling and Assimilation Office (2015) and DNV GL (2016) data. The scientific colour map is sourced from Crameri (2018). Hybrid Wind and Solar capacity factor maps Nine hybrid wind and solar maps are available, divided into 10% intervals of wind to solar ratio (eg. (wind 40% : solar 60%), (wind 50% : solar 50%), (wind 60% : solar 40%) etc.). The maps show the capacity factor available for electrolysis. Wind and solar plants might be oversized to increase the overall running time of the hydrogen plant allowing the investor to reduce electrolyser capital expenditures for the same amount of output. Calculations also include curtailment (or capping) of excess electricity when more electricity is generated than required to operate the electrolyser. The minimum and maximum capacity factors vary relative to a map’s specified wind to solar ratio. A wind to solar ratio of 50:50 produces the highest available capacity factor of 64%. The maps are derived from Global Modeling and Assimilation Office (2015), DNV GL (2016) and Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). See the ‘Downloads' tab for the full list of references. Disclaimer The capacity factor maps are derived from modelling output and not all locations are validated. Geoscience Australia does not guarantee the accuracy of the maps, data, and visualizations presented, and accepts no responsibility for any consequence of their use. Capacity factor values shown in the maps should not be relied upon in an absolute sense when making a commercial decision. Rather they should be strictly interpreted as indicative. Users are urged to exercise caution when using the information and data contained. If you have found an error in this dataset, please let us know by contacting clientservices@ga.gov.au. This dataset is published with the permission of the CEO, Geoscience Australia.

The National Geochemical Survey of Australia (NGSA) is Australia’s first national-scale geochemical survey. It was delivered to the public on 30 June 2011, after almost five years of stakeholder engagement, strategic planning, sample collection, preparation and analysis, quality assurance/quality control, and preliminary data analytics. The project was comprehensively documented in seven initial open-file reports and six data and map sets, followed over the next decade by more than 70 well-cited scientific publications. This review compiles the body of work and knowledge that emanated from the project to-date as an indication of the impact the NGSA had over the decade 2011-2021. The geochemical fabric of Australia as never seen before has been revealed by the NGSA. This has spurred further research and stimulated the mineral exploration industry. This paper also critically looks at operational decisions taken at project time (2007-2011) that were good and perhaps – with the benefit of hindsight – not so good, with the intention of providing experiential advice for any future large-scale geochemical survey of Australia or elsewhere. Strengths of the NGSA included stakeholder engagement, holistic approach to a national survey, involvement of other geoscience agencies, collaboration on quality assurance with international partners, and targeted promotion of results. Weaknesses included gaining successful access to all parts of the nation, and management of sample processing in laboratories. <b>Citation:</b> Patrice de Caritat; The National Geochemical Survey of Australia: review and impact. <i>Geochemistry: Exploration, Environment, Analysis </i>2022;; 22 (4): geochem2022–032. doi: https://doi.org/10.1144/geochem2022-032 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

Light detection and ranging (LiDAR) systems measure surface properties at high resolution, including ground surface elevation, and vegetation height and density. As well as having routine application in studies of surface hydrology, vegetation, ecology, infrastructure and hazard assessments, LiDAR is important in groundwater studies as it can help characterise and inform hydrogeological architecture, recharge and discharge processes, surface water–groundwater connectivity, and groundwater-dependent ecosystems. LiDAR-based high-resolution elevation data support surface and subsurface mapping, borehole data analysis, and the processing, calibration and interpretation of geophysics and remote sensing. Here, we describe several applications of airborne LiDAR to understanding groundwater systems in two case study areas in northern Australia: the East Kimberley area in the Northern Territory and Western Australia, and the Upper Burdekin area in Queensland. The East Kimberley LiDAR data were critical to mapping geomorphology and near-surface hydrostratigraphy, which informed our understanding of recharge processes. The Upper Burdekin LiDAR data enabled the mapping of key surface features such as lava flows and rootless cones, which can act as recharge pathways. <b>Citation:</b> Halas, L., Kilgour, P., Gow, L. and Haiblen, A., 2020. Application of high-resolution LiDAR data for hydrogeological investigations. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Activity for secondary and senior secondary students examining a hypothetical city and its vulnerability to volcanic hazard risk. Includes background information for teachers, PowerPoint presentation, student activity sheet and worked answers.

Many scientific talks by Geoscience Australia staff are published on YouTube. These documents provide summaries (‘crib sheets’) of the presentations along with easy access links to each part of the video. They are intended to help teachers of Year 11/12 classes learning about natural hazards

World elevation map that shows the shape of the major tectonic plates. Physical print in colour for giveaway. When completed the 'Tectonic Plates Jigsaw Puzzle' will fit on a desk. Suitable for primary Years 5-6 and secondary Years 7-12.

This report presents key results from hydrogeological investigations in the Tennant Creek region, completed as part of Exploring for the Future (EFTF)—an eight year, $225 million Australian Government funded geoscience data and information acquisition program focused on better understanding the potential mineral, energy and groundwater resources across Australia. The EFTF Southern Stuart Corridor (SSC) Project area is located in the Northern Territory and extends in a north–south corridor from Tennant Creek to Alice Springs, encompassing four water control districts and a number of remote communities. Water allocation planning and agricultural expansion in the SSC is limited by a paucity of data and information regarding the volume and extent of groundwater resources and groundwater systems more generally. Geoscience Australia, in partnership with the Northern Territory Department of Environment and Natural Resources and Power and Water Corporation, undertook an extensive program of hydrogeological investigations in the SSC Project area between 2017 and 2019. Data acquisition included; helicopter airborne electromagnetic (AEM) and magnetic data; water bore drilling; ground-based and downhole geophysical data for mapping water content and defining geological formations; hydrochemistry for characterising groundwater systems; and landscape assessment to identify potential managed aquifer recharge (MAR) targets. This report focuses on the Tennant Creek region—part of the Barkly region of the Northern Territory. Investigations in this region utilised existing geological and geophysical data and information, which were applied in the interpretation and integration of AEM and ground-based geophysical data, as well as existing and newly acquired groundwater hydrochemical and isotope data. The AEM and borehole lithological data reveal the highly weathered (decomposed) nature of the geology, which is reflected in the hydrochemistry. These data offer revised parameters, such as lower bulk electrical conductivity values and increased potential aquifer volumes, for improved modelling of local groundwater systems. In many instances the groundwater is shown to be young and of relatively good quality (salinity generally <1000 mg/L total dissolved solids), with evidence that parts of the system are rapidly recharged by large rainfall events. The exception to this is in the Wiso Basin to the west of Tennant Creek. Here lower quality groundwater occurs extensively in the upper 100 m below ground level, but this may sit above potentially potable groundwater and that possibility should be investigated further. Faults are demonstrated to have significantly influenced the occurrence and distribution of weathered rocks and of groundwater, with implications for groundwater storage and movement. Previously unrecognised faults in the existing borefield areas should be investigated for their potential role in compartmentalising groundwater. Additionally a previously unrecognised sub-basin proximal to Tennant Creek may have potential as a groundwater resource or a target for MAR. This study has improved understanding of the quantity and character of existing groundwater resources in the region and identified a managed aquifer recharge target and potential new groundwater resources. The outcomes of the study support informed water management decisions and improved water security for communities; providing a basis for future economic investment and protection of environmental and cultural values in the Tennant Creek and broader Barkly region. Data and information related to the project are summarised in the conclusions of this report and are accessible via the EFTF portal (https://portal.ga.gov.au/).

The AEM method measures regolith and rocks' bulk subsurface electrical conductivity, typically to a depth of several hundred meters. AEM survey data is widely used in Australia for mineral exploration (i.e. mapping undercover and detection of mineralisation), groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). Geoscience Australia (GA) has flown Large regional AEM surveys over Northern Australia, including Queensland, Northern Territory and Western Australia. The surveys were flown nominally at 20-kilometre line spacing, using the airborne electromagnetic systems that have signed technical deeds of staging with GA to ensure they can be modelled quantitatively. Geoscience Australia commissioned the survey as part of the Exploring for the Future (EFTF) program. The EFTF program is led by Geoscience Australia (GA), in collaboration with the Geological Surveys of the Northern Territory, Queensland, South Australia and Western Australia, and is investigating the potential mineral, energy and groundwater resources in northern Australia and South Australia. We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled conductivity with a suite of national environmental and geological covariates. These covariates include terrain derivatives, gamma-ray radiometric, geological maps, climate derived surfaces and satellite imagery. Conductivity-depth values were derived from a single model using GA's deterministic 1D smooth-30-layer layered-earth-inversion algorithm. (Brodie and Richardson 2015). Three conductivity depth interval predictions are generated to interpolate the actual modelled conductivity data, which is 20km apart. These depth slices include a 0-50cm, 9-11m and 22-27m depth prediction. Each depth interval was modelled and individually optimised using the gradient boosted tree algorithm. The training cross-validation step used label clusters or groups to minimise over-fitting. Many hundreds of conductivity models are generated (i.e. ensemble modelling). Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th) to measure model uncertainty. Grids show conductivity (S/m) in log 10 units. Reported out-of-sample r-squares for each interval in order of increasing depth are 0.74, 0.64, and 0.67. A decline in model performance with increasing depth was expected due to the decrease in suitable covariates at greater depths. Modelled conductivities seem to be consistent with the geological, regolith, geomorphological, and climate processes in the study area. The conductivity grids are at the resolution of the covariates, which have a nominal pixel size of 85 meters. Datasets in this data package include; 1. 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif 2. 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif 3. 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif 4. Covariate shift; Cov_shift.tif (higher values = great shift in covariates) Reference: Ross C Brodie & Murray Richardson (2015) Open Source Software for 1D Airborne Electromagnetic Inversion, ASEG Extended Abstracts, 2015:1, 1-3, DOI: 10.1071/ ASEG2015ab197

In October 2019, opportunistic mapping and imagery of the Wessel Marine Park on the RV Investigator revealed a localised band of high biodiversity linked to a unique and culturally important geomorphological feature in the otherwise uniform seascape prevalent in the Wessel Marine Park. Our findings help contribute to an understanding of the values of a northern marine park, including an inventory of communities and habitats as well as potential relationships to geomorphic features and culturally important sites. This has national significance to the implementation of the northern marine park management plan, as well as informing future monitoring programs in northern Australia. <b>Citation:</b> Przeslawski R, Beaman R, Fava L, Nichol S, Woehler E, Yule C (2020). Wessel Marine Park: Post-Survey Report for INV2019T02. Report to the National Environmental Science Program, <i>Marine Biodiversity Hub</i>. Geoscience Australia.