Petascale archives of Earth observations from space (EOS) have the potential to characterise water resources at continental scales. For this data to be useful, it needs to be organised, converted from individual scenes as acquired by multiple sensors, converted into ‘analysis ready data’ and made available through high performance computing platforms. Moreover, converting this data into insights requires integration of non-EOS datasets that can provide biophysical and climatic context for EOS. Digital Earth Australia has demonstrated its ability to link EOS to rainfall and stream gauge data to provide insight into surface water dynamics during the hydrological extremes of flood and drought. This information is supporting the characterisation of groundwater resources across Australia’s north and could potentially be used to gain an understanding of the vulnerability of transport infrastructure to floods in remote, sparsely gauged regions of northern and central Australia.

Seismological data are used for a variety of purposes, from earthquake hazard zonation to mapping Earth structure and mineral resource exploration. The immense volumes of seismic data now available challenge the application of routine seismic analysis techniques using existing tools. These tools fail to take advantage of recent advances in computing hardware and data formats. Given the scale of data to process and the computational complexity of algorithms involved, a more efficient approach that scales on high-performance computing and data (HPC-HPD) platforms is needed. In addition, different agencies have tended to use bespoke and ad hoc data formats, data curation processes and quality standards, hindering large-scale analyses and modelling. High-performance seismological tools (HiPerSeis) facilitate the transformation of source seismological data into formats geared towards HPC-HPD platforms. HiPerSeis also implements optimised seismological workflows that can be run at large scale on HPC-HPD platforms. <b>Citation:</b> Hassan, R., Hejrani, B., Medlin, A., Gorbatov, A. and Zhang, F., 2020. High-performance seismological tools (HiPerSeis). 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.

The implementation of a wide-land deformation monitoring program in eastern states of Australia, characterized by an extensive landmass and extensive coastlines, necessitates the generation of deformation maps with a significantly larger spatial extent. A pragmatic approach for achieving such coverage involves the integration of multiple interferometric synthetic aperture radar (InSAR) time series results encompassing various tracks and frames. The Sentinel-1 satellite constellation is facilitating the generation of expansive ground surface deformation, while the initial challenge in obtaining such a product arises from the varying spatial coverage of the Sentinel-1 data. To overcome this obstacle, we proposed a novel Sentinel-1 image definition to facilitate consistent interferometric processing. Before the formation of any large displacement map, the inconsistency between different synthetic aperture radar (SAR) image scenes must, nevertheless, be addressed. Several factors contribute to these inconsistencies in observations, such as differences in the angle of observation at the overlapping regions of adjacent image tracks and imprecise estimations within the dataset itself (e.g., burst or swath discontinuities). This study delves into solutions for these challenges, specifically: 1) introducing a computer-vision-based algorithm for InSAR dataset quality assessment and 2) proposing a global least square mosaicking procedure for the amalgamation of tiles from multitracks and frames. Statistical analyses show that better accuracy can be achieved. The mosaicking InSAR product demonstrates the capacity to quantify ground surface changes, which also exhibits a correlation with other geological layers, and the subsidence range of [−7, 10] mm/year shows a strong association with clay content levels ranging from 10% to 30% along the Darling River. <b>Citation:</b> Z. Du et al., "Toward a Wide-Scale Land Subsidence Product in Eastern States of Australia," in <i>IEEE Transactions on Geoscience and Remote Sensing</i>, vol. 61, pp. 1-12, 2023, Art no. 5213312, doi: 10.1109/TGRS.2023.3299928.