Gridded near-surface (2 and 10 m) daily average wind datasets for Australia from 1975 to 2018 have been constructed by interpolating observational data collected by the Australian Bureau of Meteorology (BoM). The new datasets span Australia at 0.05 × 0.05° resolution with a daily time step. The datasets were constructed by blending observational data collected at various heights using local surface roughness information.

<p>This dataset shows the crops grown in Queensland's main cropping areas, for the winter and summer growing-seasons, from 1988 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The basis of the maps is imagery from the (when available) Landsat-5 TM, Landsat-7 ETM+, Landsat-(8,9) OLI, and Sentinel-2(A,B) satellites; MODIS MOD13Q1 imagery was used as a backup in the case of large, temporal data gaps. Clusters of temporally similar pixels, termed 'segments', were identified in the imagery for each growing season, and served as an approximation of field boundaries. Per-segment phenological information, derived from the satellite imagery, was then combined with a tiered, tree-based statistical classifier, using >10000 field observations as training data, and >4000 independent observations for validation. The dataset supersedes a former crop-mapping effort <a href ="https://doi.org/10.3390/rs8040312">(Schmidt et al., 2016)</a>.</p> <p>Each season has 2 maps: an end-of-season prediction and a mid-season prediction. The mid-season prediction is labelled "_vInterim" to indicate that it is based on a relatively short time series, and should be used with caution.</p> <p>For optimum display symbology files have been provided for both QGIS and ArcGIS.</p>

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the brigalow belt bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

Field spectroradiometer measurements have been collected at several locations across Australia (formally known as the AusCover Supersites) to relate field based measurements to satellite data products, such as Landsat and MODIS NBAR products. Collected in collaboration with multiple government and research-based institutions.

The acquisition of sunphotometer measurements are critical to capture vital data on atmospheric properties during airborne hyperspectral imaging campaigns as well as for measurements coinciding with the overpass of satellite sensors. The atmospheric properties measured are used in atmospheric correction of the remotely sensed image data. This data is primarily for input into atmospheric correction systems. It may also prove of use in validation of aerosol products such as MOD04 and the reflectance change method developed as part of CRC-SI project 4.1 which may be integrated into the Auscover 19 band reflectance product processing. It can also be used to check methods that produce water vapour directly from the data (SODA). The MicroTops instruments referred to here capture solar radiance data in 5 wavelengths which are used to extract information on aerosol optical thickness and water vapour content. These two key parameters of interest are used as inputs for the atmospheric correction of remotely sensed image data.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23885. An estimate of persistent green cover per season. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dim) time series.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23884. The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, this is an experimental product which has not been fully validated.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/24070. Two fractional cover decile products, green cover and total cover, are currently produced from the historical timeseries of seasonal fractional cover images. These products compare, at the per-pixel level, the level of cover for the specific season of interest against the long term cover for that same season. For each pixel, all cover values for the relevant seasons within a baseline period (1988 to 2013) are classified into deciles. The cover value for the pixel in the season of interest is then classified according to the decile in which it falls.

For some time, Remote Sensing Sciences, has produced Foliage Projective Cover (FPC) using a model applied to Landsat surface reflectance imagery, calibrated by field observations. An updated model was developed which relates field measurements of FPC to 2-year time series of Normalized Difference Vegetation Index (NDVI) computed from Landsat seasonal surface reflectance composites. The model is intended to be applied to Landsat and Sentinel-2 satellite imagery, given their similar spectral characteristics. However, due to insufficient field data coincident with the Sentinel-2 satellite program, the model was fitted on Landsat imagery using a significantly expanded, national set of field data than was used for the previous Landsat FPC model fitting. The FPC model relates the field measured green fraction of mid- and over-storey foliage cover to the minimum value of NDVI calculated from 2-years of Landsat seasonal surface reflectance composites. NDVI is a standard vegetation index used in remote sensing which is highly correlated with vegetation photosynthesis. The model is then applied to analogous Sentinel-2 seasonal surface reflectance composites to produce an FPC image at Sentinel-2 spatial resolution (i.e. 10&nbsp;m) using the radiometric relationships established between Sentinel-2 and Landsat in Flood (2017). This is intended to represent the FPC for that 2-year period rather than any single date, hence the date range in the dataset file name. The dataset is generally expected to provide a reasonable estimate of the range of FPC values for any given stand of woody vegetation, but it is expected there will be over- and under-estimation of absolute FPC values for any specific location (i.e. pixel) due to a range of factors. The FPC model is sensitive to fluctuations in vegetation greenness, leading to anomalies such as high FPC on irrigated pastures or locations with very green herbaceous or grass understoreys. A given pixel in the FPC image, represents the predicted FPC in the season with the least green/driest vegetation cover over the 2-year period assumed to be that with the least influence of seasonally variable herbaceous vegetation and grasses on the more seasonally stable woody FPC estimates. The two-year period was used partly because it represents a period relative to tree growth but was also constrained due to the limited availability of imagery in the early Sentinel-2 time series. The FPC dataset is constrained by the woody vegetation extent dataset for the FPC year.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23883. The monthly fractional cover product shows representative values for the proportion of bare ground, green and non-green ground cover across a month. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each month. This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and Sentinel-2 time series.