The National Spectral Database (NSD) houses data from Australian remote sensing scientists. The database includes spectra covering targets as diverse as mineralogy, soils, plants, water bodies and various land surfaces. Currently the database holds spectral information from multiple locations across the country and as the collection grows in spatial / temporal coverage, the NSD will service continental scale validation requirements of the Earth observation community for satellite-based measurements of surface reflectance. <b>Value:</b> Curated spectral data provides a wealth of knowledge to remote sensing scientists. For other parties interested in calibration and validation (Cal/Val) of surface reflectance products, the Geoscience Australia (GA) Cal/Val dataset provides a useful resource of ground-truth data to compare to reflectance captured by Landsat 8 and Sentinel 2 satellites. The Aquatic Library is a robust collection of Australian datasets from 1994 to present time, primarily of end-member and substratum measurements. The University of Wollongong collection represents immense value in end-member studies, both terrestrial and aquatic. <b>Scope:</b> The NSD covers Australian data including historical datasets as old as 1994. Physical study sites encompass locations around Australia, with spectra captured in every state. <b>Data types:</b> - Spectral data: raw digital numbers (DN), radiance and reflectance.  - From spectral bands VIS-NIR, SWIR1 & SWIR2: wavelengths 350nm - 2500nm collected with instruments in the field or lab setting. Contact for further information: NSDB_manager@ga.gov.au

This is a collection of continuous seismic records gathered by temporal and semi-permanent seismic deployments where real-time data transmission was not available. Time spans vary from half an hour to more than a year depending on the purpose of the survey. Description of the employed instrumentation and array constellations can be found in the accompanied material. <b>Value: </b>Passive seismic data contains records of soil vibration due to the natural earth movements, ocean, weather, and anthropogenic activities. This data is used in ongoing research to infer national lithospheric structure from depth of a few meters to a hundred kilometres. Derived models are an important source of information for assessment of resource potential and natural hazard. <b>Scope: </b>Over time, surveys have been focused on areas of economic interest, current work of the Australian Passive Seismic Array Project (AusArray) is seeking to create a grid pattern, spaced ~55 km apart, and complemented by semi-permanent higher sensitivity broadband seismic stations. For more information about AusArray click on the following URL: <a href="https://www.ga.gov.au/eftf/minerals/nawa/ausarray">https://www.ga.gov.au/eftf/minerals/nawa/ausarray</a> <b>Data from phase 1 are available on request from clientservices@ga.gov.au - Quote eCat# 135284</b>

Hydrochemistry data for Australian groundwater, including field and laboratory measurements of chemical parameters (electrical conductivity (EC), potential of hydrogen (pH), redox potential, and dissolved oxygen), major and minor ions, trace elements, nutrients, pesticides, isotopes and organic chemicals. < <b>Value: </b>The chemical properties of groundwater are key parameters to understand groundwater systems and their functions. Groundwater chemistry information includes the ionic and isotopic composition of the water, representing the gases and solids that are dissolved in it. Hydrochemistry data is used to understand the source, flow, and interactions of groundwater samples with surface water and geological units, providing insight into aquifer characteristics. Hydrochemistry information is key to determining the quality of groundwater resources for societal, agricultural, industrial and environmental applications. Insights from hydrochemical analyses can be used to assess a groundwater resource, the impact of land use changes, irrigation and groundwater extraction on regional groundwater quality and quantity, assess prospective mineral exploration targets, and determine how groundwater interacts with surface water in streams and lakes. <b>Scope: </b>The database was inaugurated in 2016 with hydrochemical data collected over the Australian landmass by Geoscience Australia and its predecessors, and has expanded with regional and national data. It has been in the custodianship of the hydrochemists in Geoscience Australia's Minerals, Energy and Groundwater Division and its predecessors. Explore the <b>Geoscience Australia portal - https://portal.ga.gov.au/</b>

This collection contains all national level bathymetry grids held by Geoscience Australia (GA) dating back to survey data obtained since 1993. <b>Value: </b>Bathymetry data is used for a wide range of marine applications including: navigation, environmental assessment, jurisdictional boundaries, resource exploration. <b>Scope: </b>Data holdings lying within the offshore area of Australia, including international waters. <b>To access the AusSeaBed Marine Data Portal</b> use the following link: <a href="https://portal.ga.gov.au/persona/marine#/">https://portal.ga.gov.au/persona/marine#/</a>

Wind multipliers are factors that transform wind speeds over open, flat terrain (regional wind speeds) to local wind speeds that consider the effects of direction, terrain (surface roughness), shielding (buildings and structures) and topography (hills and ridges). During the assessment of local wind hazards (spatial significance in the order 10's of metres), wind multipliers allow for regional wind speeds (order 10 to 100's of kilometres) to be factored to provide local wind speeds. <b>Value: </b>The wind multiplier data is used in modelling the impacts (i.e. physical damage) of wind-related events such as tropical cyclones (an input for Tropical Cyclone Risk assessment), thunderstorms and other windstorms. <b>Scope: </b>Includes terrain, shielding and topographic multipliers for national coverage. Each multiplier further contains 8 directions.

Descriptions of and measurements from field sites and samples from geological (including regolith) surveys. <b>Value: </b>Used to constrained surface geology, important in resource exploration and understanding physical environment. <b>Scope: </b>Mapping surveys mainly in Australia, but also in Antarctica, Oceania and south-east Asia.

This collection includes information regarding the location and design of Australian onshore and offshore boreholes, where boreholes are defined as the generalized term for any narrow shaft drilled in the ground, either vertically or horizontally. In this context, boreholes include: Mineral Drillholes, Petroleum Wells and Water Bores along with a variety of others types, but does not include Costean, Trench or Pit. <b>Value: </b> Information related to the boreholes described in this collection have the potential to support geological investigations and assessment of a variety of resources. <b>Scope: </b>Selected open file boreholes Australian boreholes located onshore and offshore

Analysis Ready Data (ARD) takes medium resolution satellite imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive ARD package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. ARD consists of sub products, including : 1) NBAR Surface Reflectance which produces standardised optical surface reflectance data using robust physical models which correct for variations and inconsistencies in image radiance values. Corrections are performed using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). 2) NBART Surface Reflectance which performs the same function as NBAR Surface Reflectance, but also applies terrain illumination correction. 3) OA Observation Attributes product which provides accurate and reliable contextual information about the data. This 'data provenance' provides a chain of information which allows the data to be replicated or utilised by derivative applications. It takes a number of different forms, including satellite, solar and surface geometry and classification attribution labels. ARD enables generation of Derivative Data and information products that represent biophysical parameters, either summarised as statistics, or as observations, which underpin an understanding of environmental dynamics. The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent Derivative products include: - Water Observations from Space (WOfS) - National Intertidal Digital Elevation Model (NIDEM) - Fractional Cover (FC) - Geomedian ARD and Derivative products are reproduced through a period collection upgrade process for each sensor platform. This process applied improvements to the algorithms and techniques and benefits from improvements applied to the baseline data that feeds into the ARD production processes. <b>Value: </b>These data are used to understand distributions of and changes in surface character, environmental systems, land use. <b>Scope: </b>Australian mainland and some part of adjacent nations. Access data via the DEA web page - <a href="https://www.dea.ga.gov.au/products/baseline-data">https://www.dea.ga.gov.au/products/baseline-data</a>

3D structural and geological models that provide insight and understanding of the continents subsurface. The models capture 3D stratigraphy and architecture, including the depth to bedrock and the locations of different major rock units, faults and geological structures. <b>Value: </b>These models are valuable for exploration and reconstructions of Australia's evolution <b>Scope: </b>Contains a variety of 3D volumetric models and surfaces that were produced for specific projects at regional to continental scale.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.