<p>This data set consists of .tif files of true colour orthomosaics for expansive areas of mangroves in Kakadu National Park in Australia's Northern Territory.</p> <p>The orthomosaics were generated from 68 stereo pairs of true colour aerial photographs acquired in 1991 in the lower reaches of the East Alligator, West Alligator, South Alligator and Wildman Rivers and Field Island, Kakadu National Park, Northern Australia (Mitchell et al., 2007). The photographs were taken at a flying height of 13,000 ft (3,960 m) using a Wild CR10, a standard photogrammetric camera with a frame size of 230 x 230 mm. The focal length was 152 mm. The photographs were scanned by Airesearch (Darwin) with a photogrammetric scanner to generate digital images with a pixel resolution between 12 and 15 mm. The orthomosaics have a spatial resolution of 1 m, cover an area of approximately 742 km² and a coastal distance of 86 km. </p> <p>These orthomosaics were co-registered using ground control points identified from 1:100,000 digital topographic maps with a Universal Transverse Mercator (UTM), and subsequently co-registered to LiDAR data acquired over the same region in 2011.</p>

<p>This data set consists of a shapefile/kml of mangrove extent and dominant species for Kakadu National Park mangroves generated from true colour aerial photographs acquired in 1991.</p> <p>From true color 1991 orthomosaics of Field Island and the Wildman, West, and South Alligator Rivers, mangroves were mapped by first applying a fine scale spectral difference segmentation within eCognition to all three visible bands (blue, green, and red). A maximum likelihood (ML) algorithm within the environment for visualizing images (ENVI) software was then used to classify all segments using training areas associated with mangroves, but also water, mudflats, sandflats, and coastal woodlands. These were identified through visual interpretation of the imagery. Segmentation was necessary as 1) the diversity of structures and shadows within and between tree crowns limited the application of pixel-based classification procedures and 2) the color balance between the different photographs comprising the orthomosaics varied. All segments were examined individually and methodically to determine whether they should be reallocated to a non-mangrove class (e.g., mudflats) or confirmed as mangroves. Open woodlands dominated by Eucalyptus species could also be visually identified within the aerial photography (AP) orthoimages, although their discrimination was assisted by only considering areas where the underlying LiDAR DTM (Digital Terrain Model) exceeded 10 m, assuming this excludes tidally inundated sections.</p>

The Soil Moisture Integration and Prediction System (SMIPS) produces national extent daily estimates of volumetric soil moisture at a resolution of approximately 1km or 0.01 decimal degrees. SMIPS also generates an index of between 0-1 which approximates how full the 90cm metre soil moisture store is at a particular location and time. The SMIPS model itself consists of two linked soil moisture stores, a shallow quick responding 10cm upper store and a deeper, slower responding 80cm store. SMIPS is parameterised using physical properties from the <a href ='https://www.clw.csiro.au/aclep/soilandlandscapegrid/'>Soil and Landscape Grid of Australia </a>and takes a data model fusion approach for model forcing. Version 1.0 of the SMIPS model uses precipitation and potential evapotranspiration data from the Bureau of Meteorology’s <a href="http://www.bom.gov.au/water/landscape/assets/static/publications/AWRALv6_Model_Description_Report.pdf">AWRA Model</a>. In addition to version 1.0 of the model, an experimental version of the model is available for user testing. This version of the model uses precipitation data supplied by an experimental CSIRO daily rainfall surface generated using spatial data from the NASA Global Precipitation Mission as a base and enhanced using rainfall observations from the Bureau of Meteorology (BoM) rainfall gauge network, and various landscape covariates, processed using a machine learning approach. <br> To help increase model accuracy, the internal SMIPS model states are adjusted or ‘bumped’ by daily observational data from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite mission.

The Australian cosmic-ray soil moisture monitoring network was first established in 2010 to provide Australian and global researchers with spatially distributed intermediate scale soil moisture observations. A cosmic-ray sensor (CRS) provides continuous estimates of soil moisture over an area of approximately 30 hectares by measuring naturally generated fast neutrons (energy 10–1000 eV) that are produced by cosmic rays passing through the Earth’s atmosphere. The neutron intensity above the land surface is inversely correlated with soil moisture as it responds to the hydrogen contained in the soil and to a lesser degree to plant and soil carbon compounds. The cosmic-ray technique is also passive, non-contact, and is largely insensitive to bulk density, surface roughness, the physical state of water, and soil texture. The scale of CRS measurements fills the void between point scale sensor measurements and large scale satellite observations. The depth of measurements varies with the moisture content of the soil but is typically between 10-30 cm. The depth of observations is reported as ‘effective depth’. <br> The CosmOz network is expanding as new sensors are added over time. The initial network was funded by CSIRO Land and Water but more recently TERN has funded work to maintain the network add new sensors and deliver data more efficiently. The standard CRS installation includes; a cosmic-ray neutron tube, a rain gauge (2m high), temperature and humidity sensors, and an atmospheric pressure sensor. Measures of all parameters are reported at an hourly interval. Each CRS requires an in-field calibration across the footprint of measurements to convert neutron counts to soil moisture content. The calibration includes collection of soil samples for bulk density, lattice water content and soil organic carbon.<br> The Australia CosmOz network consists of <a href="https://cosmoz.csiro.au/sites">19 stations</a>. The extent of the network and available data can be seen at the CosmOz network web page: <a href="https://cosmoz.csiro.au/">https://cosmoz.csiro.au</a>. The data is also accessible from the <a href="https://landscapes-cosmoz-api.tern.org.au/rest/doc">TERN Cosmoz REST API</a>.<br> The calibration and correction procedures used by the network are described by <a href="https://doi.org/10.1002/2013WR015138">Hawdon et al. 2014 </a>.

<p>This dataset provides accurate, high-resolution (30 m) / high-frequency (monthly) / continuous (no gaps due to cloud) actual evapotranspiration (AET) for Australia using the CMRSET algorithm. The CMRSET algorithm uses reflective remotely sensed indices to estimate AET from potential evapotranspiration (PET; calculated using daily gridded meteorological data generated by the Bureau of Meteorology). Blending high-resolution / low-frequency AET estimates (e.g., Landsat and Sentinel-2) with low-resolution / high-frequency AET estimates (e.g., MODIS and VIIRS) results in AET data that are high-resolution / high-frequency / continuous (no gaps due to cloud) and accurate. These are all ideal characteristics when calculating the water balance for a wetland, paddock, river reach, irrigation area, landscape or catchment. </p><p> Accurate AET information is important for irrigation, food security and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation and better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots", and knowing their water needs allows for enhanced management of these critical areas in the landscape. Having high-resolution, frequent and accurate AET estimates for all of Australia means this AET data source can be used to model the water balance for any catchment / groundwater system in Australia. </p><p> Details of the CMRSET algorithm and its independent validation are provided in Guerschman, J.P., McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Peña-Arancibia, J.L. and Chen, Y. (2022) Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data. Journal of Hydrology. 605, 127318, doi:10.1016/j.jhydrol.2021.127318</p> <p> <i>We strongly recommend users to use the TERN CMRSET AET V2.2</i>. Details of the TERN CMRSET AET V2.2 data product generation are provided in McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Guerschman, J.P., Peña-Arancibia, J.L. and Stenson, M.P. (2022) Generating a multi-decade gap-free high-resolution monthly actual evapotranspiration dataset for Australia using Landsat, MODIS and VIIRS data in the Google Earth Engine platform: Development and use cases. Journal of Hydrology (In Preparation).

This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in semi-arid eucalypt woodland using eddy covariance techniques. It been processed using PyFluxPro (v3.3.3) as described in Isaac et al. (2017), <a href="https://doi.org/10.5194/bg-14-2903-2017">https://doi.org/10.5194/bg-14-2903-2017</a>. PyFluxPro takes data recorded at the flux tower and process this data to a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). For more information about the processing levels, see <a href="https://github.com/OzFlux/PyFluxPro/wiki">https://github.com/OzFlux/PyFluxPro/wiki</a>. <br /> <br /> The Otway flux station was located at Narrinda South in south west Victoria, Australia.The pasture was grazed by dairy cattle with average grass height of 0.1m. Annual average rainfall at the site was around 800mm and was only moderately seasonal. Mean daily temperature ranged from 25°C in February to 12°C in July. The flux station was situated on a 10m tower. Fluxes of heat, water vapour and carbon dioxide were measured using the open-path eddy covariance technique. Supplementary measurements included temperature, humidity, rainfall, total solar, photosynthetically active radiation (PAR) and net radiation. Soil temperature and heat flux were also measured. The Otway flux station was established in February 2007 on private land at Nirranda South and managed by CSIRO Marine and Atmospheric Research staff as part of the Cooperative Research Centre for Greenhouse Gas Technologies.<br />For additional site information, see http://www.ozflux.org.au/monitoringsites/otway/index.html . <br><br>

<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.5.0) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br> The Otway flux station was located at Narrinda South in south west Victoria, Australia.The pasture was grazed by dairy cattle with average grass height of 0.1&nbsp;m. Annual average rainfall at the site was around 800&nbsp;mm and was only moderately seasonal. Mean daily temperature ranged from 25&nbsp;°C in February to 12&nbsp;°C in July. The flux station was situated on a 10&nbsp;m tower. Fluxes of heat, water vapour and carbon dioxide were measured using the open-path eddy covariance technique. Supplementary measurements included temperature, humidity, rainfall, total solar, photosynthetically active radiation (PAR) and net radiation. Soil temperature and heat flux were also measured. The Otway flux station was established in February 2007 on private land at Nirranda South and managed by CSIRO Marine and Atmospheric Research staff as part of the Cooperative Research Centre for Greenhouse Gas Technologies.<br /> <br><br>

<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.15) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br> The Otway flux station was located at Narrinda South in south west Victoria, Australia.The pasture was grazed by dairy cattle with average grass height of 0.1&nbsp;m. Annual average rainfall at the site was around 800&nbsp;mm and was only moderately seasonal. Mean daily temperature ranged from 25&nbsp;°C in February to 12&nbsp;°C in July. The flux station was situated on a 10&nbsp;m tower. Fluxes of heat, water vapour and carbon dioxide were measured using the open-path eddy covariance technique. Supplementary measurements included temperature, humidity, rainfall, total solar, photosynthetically active radiation (PAR) and net radiation. Soil temperature and heat flux were also measured. The Otway flux station was established in February 2007 on private land at Nirranda South and managed by CSIRO Marine and Atmospheric Research staff as part of the Cooperative Research Centre for Greenhouse Gas Technologies.<br /> <br><br>

<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.17) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br> The Otway flux station was located at Narrinda South in south west Victoria, Australia.The pasture was grazed by dairy cattle with average grass height of 0.1&nbsp;m. Annual average rainfall at the site was around 800&nbsp;mm and was only moderately seasonal. Mean daily temperature ranged from 25&nbsp;°C in February to 12&nbsp;°C in July. The flux station was situated on a 10&nbsp;m tower. Fluxes of heat, water vapour and carbon dioxide were measured using the open-path eddy covariance technique. Supplementary measurements included temperature, humidity, rainfall, total solar, photosynthetically active radiation (PAR) and net radiation. Soil temperature and heat flux were also measured. The Otway flux station was established in February 2007 on private land at Nirranda South and managed by CSIRO Marine and Atmospheric Research staff as part of the Cooperative Research Centre for Greenhouse Gas Technologies.<br /> <br><br>

<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.15) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br>Tumbarumba flux station is located in Bago State Forest in south eastern New South Wales. It was established in 2000 and is managed by CSIRO Marine and Atmospheric Research. The forest is classified as wet sclerophyll, the dominant species is <em>Eucalyptus delegatensis</em>, and average tree height is 40&nbsp;m. Elevation of the site is 1200&nbsp;m and mean annual precipitation is 1000&nbsp;mm. Bago and Maragle State Forests are adjacent to the south west slopes of southern New South Wales and the 48,400&nbsp;ha of native forest have been managed for wood production for over 100 years. The instrument mast is 70&nbsp;m tall. Fluxes of heat, water vapour and carbon dioxide are measured using the open-path eddy flux technique. Supplementary measurements above the canopy include temperature, humidity, wind speed, wind direction, rainfall, incoming and reflected shortwave radiation and net radiation. Profiles of temperature, humidity and CO₂ are measured at seven levels within the canopy. Soil moisture content is measured using time domain reflectometry. Soil heat fluxes and temperature are also measured. Hyper-spectral radiometric measurements are being used to determine canopy leaf-level properties.</br>