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High-resolution bathymetry is a critical dataset for marine geoscientists. It can be used to characterize the seafloor and its marine habitats, to understand past sedimentary records and even to support the development of offshore engineering projects. Most methods to acquire bathymetry data are costly and can only be practically deployed on relatively small areas. It is therefore critical to develop cost-effective and advanced techniques to produce large-scale bathymetry datasets. This paper presents an integrated workflow that builds on satellites images and 3D seismic surveys, integrated with historical depth soundings, to generate a regional high-resolution digital elevation model. The method was applied to the southern half of Australia's North West Shelf and led to the creation of a new high-resolution bathymetry, with a resolution of 10 × 10 m in nearshore areas and 30 × 30 m elsewhere. The vertical and spatial accuracy of the datasets have been thoroughly assessed using open source Laser Airborne Depth Sounder (LADS) and Multi Beam Echo Sounder (MBES) surveys as a reference. The comparison of the datasets indicates that the seismic-derived bathymetry has a vertical accuracy better than 1 m + 2 % of the absolute water depths, while the satellite-derived bathymetry has a depth accuracy better than 1 m + 5 % of the absolute water depths. This dataset constitutes a significant improvement of the pre-existing regional 250 × 250 m grid and will support the onset of research projects on costal morphologies, marine habitats, archaeology, and sedimentology. All datasets used as inputs are publicly available and the method is fully integrated in Python scripts making it readily applicable elsewhere in Australia and around the world. The workflow as well as the resulting bathymetry have been independently reviewed and approved for release by a technical committee from the AusSeabed Community (Geoscience Australia). The regional digital elevation model as well as the underlying datasets can be accessed at: https://doi.org/10.26186/144600. [ABSTRACT FROM AUTHOR]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring. [ABSTRACT FROM AUTHOR]

The objective of this paper is to use remote sensing to measure on-road emissions and to examine the impact and usefulness of additional measurement devices at three sites. Supplementing remote sensing device (RSD) equipment with additional equipment increased the capture rate by almost 10%. Post-processing of raw data is essential to obtain useful and accurate information. A method is presented to identify vehicles with excessive emission levels (high emitters). First, an anomaly detection method is applied, followed by identification of cold start operating conditions using infrared vehicle profiles. Using this method, 0.6% of the vehicles in the full (enhanced) RSD data were identified as high emitters, of which 35% are likely in cold start mode where emissions typically stabilize to low hot running emission levels within a few minutes. Analysis of NOx RSD data confirms that poor real-world NOx performance of Euro 4/5 light-duty diesel vehicles observed around the world is also evident in Australian measurements. This research suggests that the continued dieselisation in Australia, in particular under the current Euro 5 emission standards and the more stringent NO2 air quality criteria expected in 2020 and 2025, could potentially result in local air quality issues near busy roads. [ABSTRACT FROM AUTHOR]

Abstract: Sounding rocket programs for upper atmospheric research (UAR) commenced at Woomera in 1957, with the British Skylark project and the Anglo–Australian HARP rockoon program. Shortly afterwards, the Weapons Research Establishment (WRE) inaugurated its own sounding rocket program with the first successful Australian-built sounding rocket, Long Tom, developed from surplus British motors. This research would eventually lead to the development of Australia''s first satellite, WRESAT, launched in 1967. Long Tom became the first more than 10 Australian designed and built sounding rockets that would be employed at Woomera until the demise of the WRE upper atmosphere program in 1976, as the Range was winding down to the cessation of the Anglo–Australian Joint Project. Like Long Tom, most of the early Australian sounding rockets were constructed by the WRE from surplus British motors, but after 1967 the later vehicles had a much higher local content, based on combinations of British motors and/or rocket motors developed in Australia. These new motors were named after constellations of the southern sky, while the rockets themselves bore the names of Australian native birds. This paper will outline the technical and scientific history of the Australian sounding rocket program, examining its origins and the reasons for its demise. It will look at the sequential development of the various Australian rockets and consider the particular research projects with which they were associated, the relationship with the WRESAT project and the move to Australian production after 1967. [Copyright &y& Elsevier]

In this study, we present ground-based measurements of column-averaged dry-air mole fractions (DMFs) of CO2 (or XCO2) taken in a semiarid region of Australia with an EM27/SUN portable spectrometer equipped with an automated clamshell cover. We compared these measurements to space-based XCO2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT). Side-by-side measurements of EM27/SUN with the Total Carbon Column Observing Network (TCCON) instrument at the University of Wollongong were conducted in 2015–2016 to derive an XCO2 scaling factor of 0.9954 relative to TCCON. Although we found a slight drift of 0.13 % over 3 months in the calibration curve of the EM27/SUN vs. TCCON XCO2 , the alignment of the EM27/SUN proved stable enough for a 2-week campaign, keeping the retrieved Xair values, another measure of stability, to within 0.5 % and the modulation efficiency to within 2 %. From the measurements in Alice Springs, we confirm a small bias of around 2 ppm in the GOSAT M-gain to H-gain XCO2 retrievals, as reported by the NIES GOSAT validation team. Based on the reported random errors from GOSAT, we estimate the required duration of a future campaign in order to better understand the estimated bias between the EM27/SUN and GOSAT. The dataset from the Alice Springs measurements is accessible at 10.4225/48/5b21f16ce69bc. [ABSTRACT FROM AUTHOR]

The large bush fires which occurred in southeast Australia in February 2009 were unusually destructive. However, they were also unusual in the amounts of various combustion products which were injected directly into the stratosphere. We report the observations by the Microwave Limb Sounder (MLS) instrument on the Aura satellite of some of these combustion products. The highest quality observations are of CO; these clearly show a large region of enhanced mixing ratios to the north of New Zealand which remains in that region for about ten days before drifting westwards and finally dissipating over the Atlantic about a month after the fire. The region of enhanced CO ascends from the tropopause to 46 hPa during this period. Back trajectories run from the points where MLS observes enhanced CO pass close to the site of the fire. TheMLS observations of CH3CN and HCN resemble those of CO except for their poorer vertical resolution and more limited vertical range. An apparent enhancement in ClO is also observed by MLS, but detailed analysis of the measured radiances reveals this feature to be a signature of CH3OH, which is not currently retrieved by the MLS data processing system. The fires of February 2009 are the only event of this type and magnitude in the 7-yr MLS record. [ABSTRACT FROM AUTHOR]

Changes in biological productivity in the Southern Ocean have the potential to have a significant effect on world climate. Here we use a combination of satellite, model and model reanalysis data to examine climate variability in the Australian sector of the Southern Ocean (110-160°E, 40-70°S) to identify the controls on chlorophyll- a (a proxy for phytoplankton biomass) and primary productivity and evaluate trends in these controls over the period 1997-2007. In summer, in the 65-70°S zone, sea-ice concentration together with the Southern Annular Mode explains 51% of the variance in chlorophyll- a, while mean wind stress and sea-surface temperature explains 55% of the variance in the 60-65°S zone. Further north, key controls are photosynthetically active radiation, sea-surface temperature, mixed layer depth and stratification. Trends in hydrodynamic variables are found to often be opposite in sign and up to an order of magnitude larger than those previously identified in the same sector for 1958-2005. Allowing for the effect of shorter time series on the magnitude of the trends, many recent trends seem to be outside the range of previous variability. These results are consistent with a shift in the ocean state in the past 10-15 yr, in response to a shift in climate. [ABSTRACT FROM AUTHOR]

Rosenfeld (2000, hereafter R00), in applying new satellite methodology to analyse case studies in Southeastern Australia and elsewhere, provided evidence that urban and industrial air pollution can suppress precipitation from shallow clouds. He concluded that ‘Air pollution must be an important factor in determining the precipitation amounts in the Snowy Mountains’. These satellite observations were the impetus for our proposed detailed follow-on research program to further validate and quantify these inferences, publicly offered in Rosenfeld et al. (2006, hereafter R06) and repeated here, thereby recognizing the remaining large uncertainties. In response, Ayers (2009, hereafter A09) attempts to deny the significance and validity of the observations of R00. His scientific arguments are refuted here. Furthermore, A09 wrote erroneously that ‘a hypothesis that air pollution in the form of small particles has caused a secular decrease in precipitation over SE Australia was advanced by Rosenfeld (2000) , who concluded that the hypothesis was proven.’ But R00 did not make such a claim, although this is a viable hypothesis that warrants testing (R06). In fact, R00 wrote: ‘trend analyses of snow, winter temperature, and total winter rainfall for the period 1910–1991 showed statistically insignificant decreases... ( Duus, 1992 )’. [ABSTRACT FROM AUTHOR]

Water managers need tools to assist in the management of ever increasing algal bloom problems over wide spatial areas to complement sparse and declining in situ monitoring networks. Optical methods employing satellite data offer rapid and widespread coverage for early detection of bloom events. The advent of the Analysis Ready Data (ARD) and Open Data Cube concepts offer the means to lower the technical challenges confronting managers, allowing them to adopt satellite tools. Exploiting Landsat ARD integrated into the Digital Earth Australia data cube, we developed a prototype algal bloom alerting tool for the state of New South Wales, Australia. A visualization portal allows managers to gain insights into bloom status across the state as a whole and to further investigate spatial patterns in bloom alerts at an individual water body basis. To complement this we also proposed an algal alert system for trial based on chlorophyll and TSM levels which requires further testing. The system was able to retrieve the status of 444 water bodies across the state and outputs from the visualization system are presented. Time series of image acquisitions during an intense bloom in one lake are used to demonstrate the potential of the system. We discuss the implications for further development and operationalisation including the potential for augmentation with alternative algorithms and incorporation of other sensor ARD data to improve both temporal and spectral resolutions. [ABSTRACT FROM AUTHOR]

Sustainable Development Goals (SDGs) are a set of priorities the United Nations and World Bank have set for countries to reach in order to improve quality of life and environment globally by 2030. Free satellite images have been identified as a key resource that can be used to produce official statistics and analysis to measure progress towards SDGs, especially those that are concerned with the physical environment, such as forest, water, and crops. Satellite images can often be unusable due to missing data from cloud cover, particularly in tropical areas where the deforestation rates are high. There are existing methods for filling in image gaps; however, these are often computationally expensive in image classification or not effective at pixel scale. To address this, we use two machine learning methods—gradient boosted machine and random forest algorithms—to classify the observed and simulated 'missing' pixels in satellite images as either grassland or woodland. We also predict a continuous biophysical variable, Foliage Projective Cover (FPC), which was derived from satellite images, and perform accurate binary classification and prediction using only the latitude and longitude of the pixels. We compare the performance of these methods against each other and inverse distance weighted interpolation, which is a well-established spatial interpolation method. We find both of the machine learning methods, particularly random forest, perform fast and accurate classifications of both observed and missing pixels, with up to 0.90 accuracy for the binary classification of pixels as grassland or woodland. The results show that the random forest method is more accurate than inverse distance weighted interpolation and gradient boosted machine for prediction of FPC for observed and missing data. Based on the case study results from a sub-tropical site in Australia, we show that our approach provides an efficient alternative for interpolating images and performing land cover classifications. [ABSTRACT FROM AUTHOR]