<p>The dataset measures the long-term seasonal means of the chlorophyll a concentrations of ocean surface waters. They are derived from MODIS (aqua) images using NASA's SeaDAS image processing software. The monthly chlorophyll a images between July 2002 and December 2017 are used to calculate the means of the four austral seasons: winter (June, July, and August), spring (September, October and November), summer (December, January and February) and autumn (March, April and May). The extent of the dataset covers the entire Australian EEZ and surrounding waters (including the southern ocean). The unit of the dataset is mg/m3. <p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.

<p>The dataset indicates the long-term overall primary productivity hotspots of ocean surface waters. They are derived from MODIS (aqua) images using NASA's SeaDAS image processing software. The monthly chlorophyll a images between July 2002 and August 2014 are used to identify the overall primary productivity hotspots. The extent of the dataset covers the entire Australian EEZ and surrounding waters (including the southern ocean). The value (between 0 and 1.0) of the dataset represents the likelihood of the location being a primary productivity hotspot. <p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.

<p>The dataset measures the long-term seasonal means of the sea surface temperature (SST) of ocean surface waters. They are derived from MODIS (aqua) images using NASA's SeaDAS image processing software. The monthly SST images between July 2002 and December 2017 are used to calculate the means of the four austral seasons: winter (June, July, and August), spring (September, October and November), summer (December, January and February) and autumn (March, April and May). The extent of the dataset covers the entire Australian EEZ and surrounding waters (including the southern ocean). The unit of the dataset is Celsius degree. <p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.

<p>The dataset measures the long-term seasonal variations of the chlorophyll a concentrations of ocean surface waters. They are derived from MODIS (aqua) images using NASA's SeaDAS image processing software. The monthly chlorophyll a images between July 2002 and December 2017 are used to calculate the standard deviations of the four austral seasons: winter (June, July, and August), spring (September, October and November), summer (December, January and February) and autumn (March, April and May). The extent of the dataset covers the entire Australian EEZ and surrounding waters (including the southern ocean). The unit of the dataset is mg/m3. <p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.

This dataset contains hotspot point data, derived from satellite-born instruments that detect light in the thermal wavelengths found on the Digital Earth Australia Hotspots application. Typically, satellite data are processed with a specific algorithm that highlights areas with an unusually high temperature. Hotspot sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites, the Advanced Very High Resolution Radiometer (AVHRR) night time imagery from the National Oceanic and Atmospheric Administration (NOAA) satellites, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi- NPP satellite. Please note: As these data are stored on a Corporate system, we are only able to supply the web services (see download links). email earth.observation@ga.gov.au.

The Digital Earth Australia Hotspots web service has been developed as part of the Digital Earth Australia Hotspots national bushfire monitoring system. The service delivers hotspot data derived from (a growing number of) satellite-born instruments that detect light in the thermal wavelengths. The colour of the spot represents the time the Hotspot was last observed by a passing satellite (e.g. 0-2 hours). The colour does not indicate severity. Typically, the satellite data are processed with a specific algorithm that highlights areas with an unusually high temperature. In principle, however, Hotspots may be sourced from non-satellite sources.

Coast upwelling is important for marine ecosystems and economic, because of its elevated primary and secondary productivity and large fish catch. This study developed a scale-independent and semi-automatic image processing technique to map the upwelling areas along the 4500 km long south-eastern coast of Australia from 14-year monthly MODIS SST data. The results show that there is significant spatial variability in the mapped upwelling areas, month to month, season to season and year to year. There is also strong temporal (month to month, seasonal and inter-annual) variability of the upwelling characteristics in area of influence, SST anomaly, chlorophyll-a concentrations and upwelling speed. This study identifies two persistent upwelling systems, the NSW system along the coast of the New South Wales and the WVIC/SA system along the coast of western Victoria and adjacent South Australia. The NSW coastal upwelling system occurs more or less continuously from austral spring to autumn. The WVIC/SA coastal upwelling system is a seasonal upwelling system occurred in the austral summer. The NSW coastal upwelling system has a stronger upwelling intensity than the WVIC/SA system, in terms of area of influence, SST anomaly, chlorophyll-a concentrations and upwelling speed. We believe that the NSW coastal upwelling system, especially the northern and central parts, is mainly driven by the EAC and its eddies; while, the WVIC/SA coastal upwelling is a typical wind-driven system. In addition, the results indicate that the ENSO events are likely to have a moderate impact on both the NSW and the WVIC/SA coastal upwelling systems. The El Nino (La Nina) events tend to strength (weaken) upwelling intensity. <b>Citation:</b> Zhi Huang, Xiao Hua Wang, Mapping the spatial and temporal variability of the upwelling systems of the Australian south-eastern coast using 14-year of MODIS data, <i>Remote Sensing of Environment</i>, Volume 227, 2019, Pages 90-109, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.04.002

<p>The dataset measures the long-term seasonal variations of the sea surface temperature (SST) of ocean surface waters. They are derived from MODIS (aqua) images using NASA's SeaDAS image processing software. The monthly SST images between July 2002 and December 2017 are used to calculate the standard deviations of the four austral seasons: winter (June, July, and August), spring (September, October and November), summer (December, January and February) and autumn (March, April and May). The extent of the dataset covers the entire Australian EEZ and surrounding waters (including the southern ocean). The unit of the dataset is Celsius degree. <p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.

The Digital Earth Australia Hotspots web service has been developed as part of the Digital Earth Australia Hotspots national bushfire monitoring system. The service delivers hotspot data derived from (a growing number of) satellite-born instruments that detect light in the thermal wavelengths. The colour of the spot represents the time the Hotspot was last observed by a passing satellite (e.g. 0-2 hours). The colour does not indicate severity. Typically, the satellite data are processed with a specific algorithm that highlights areas with an unusually high temperature. In principle, however, Hotspots may be sourced from non-satellite sources. Lineage (for eCatID 101800 and 101780): The Sentinel Hotspots system was originally developed in 2010. The Sentinel Hotspots webservice was republished in 2016 as part of a platform upgrade. The Digital Earth Australia Hotspots system and webservices was redeveloped in 2019 as part of a platform upgrade.

The mean land-surface temperature represents an important boundary condition for many geothermal studies. This boundary is particularly important to help constrain the models made to analyse resource systems, many of which are shallow in nature and observe relatively small thermal gradients. Consequently, a mean land-surface temperature map of the Australian continent has been produced from 13 years of MODIS satellite imagery, for the period 2003–2015. The map shows good agreement with independent methods of estimating mean landsurface temperature, including borehole surface-temperature extrapolation and long-term, near-surface ground measurements. In comparison to previously used methods of estimating mean land-surface temperature, our new estimates are up to 12 °C warmer. The MODIS-based method presented in this study provides spatially continuous estimates of land-surface temperature that can be incorporated as the surface thermal boundary condition in geothermal studies. The method is also able to provide a quantification of the uncertainties expected in the application of these estimates for the purposes of thermal modelling.