These data describe the Australia-wide, monthly fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR) derived from Advance Very High Resolution Radiometer data spanning July 1981 to Oct 2011. FPAR is linearly related to fractional foliage cover. Here fPAR is split into that of persistent vegetation and of recurrent vegetation, which represent non-deciduous perennial vegetation and annual, ephemeral and deciduous vegetation, respectively. Data have been processed using the "invariant cover triangle" method to remove the majority of errors introduced by sensor calibration and change-over effects.

<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>

Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series. Monthly: The monthly product is aggregated from the 8-day composites using the medoid method. Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series. MODIS fractional cover has been validated for Australia.

This product provides locations of areas affected by fire including the approximate day of burning. Inputs are daily day time observations from MODIS sensors on Terra and Aqua. Observations are atmospherically corrected and the resulting time series is investigated for sudden changes in reflectance, persistent over multiple days. Variations in observation and illumination geometry are taken into account through application of a kernel driven Bidirectional Reflectance Distribution Function (BRDF) model.

Evaporation, Transpiration, and Evapotranspiration Products for Australia based on the Maximum Entropy Production model (MEP). This record is an introduction of a method into the MEP algorithm of estimating the required model parameters over the entire continent of Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. The algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 0.05 degree (5 km) resolution for 2003 – 2013. The MEP evapotranspiration (ET) estimates were validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia and compared the MEP-ET at the EC flux towers with two other ET products over Australia; MOD16 and AWRA-L products. The MEP model outperformed the MOD16 and AWRA-L across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRA-L were 6.21, 7.29 and 6.52 mm/8 days, the average correlations were 0.64, 0.57 and 0.61, respectively. The percentage bias of the MEP ET was within 20% of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRA-L ET were within 20% of the observed ET at 4 and 10 sites respectively. The analysis showed that evaporation and transpiration contribute 38% and 62%, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003 – 2009) in Australia. File naming conventions: E – Evaporation T – Transpiration ET – Evapotranspiration For the 8 day ET, Daily T and ET, the suffix nnn indicates day of year, for example: 001 for January 1, 145 for May 25 (leap year) or 26, etc. While for the daily E, the suffix is in the format mmdd (month,day) for example 0101 for January 1, 0525 for May 25.

<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 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.

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on regression models applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI imagery for the period 1988-2014. An annual woody spectral index image is created for each year using a multiple regression model trained from field data collected mostly over the period 1996-1999. A robust regression of the time series of the annual woody spectral index is then performed. The estimated foliage projective cover is the prediction at the date of the selected dry season image for 2014. Where this deviates significantly from the woody spectral index for that date, further tests are undertaken before this estimate is accepted. In some cases, the final estimate is the woody spectral index value rather than the robust regression prediction. The product is further masked to remove areas classified as non-woody.

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.

Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat.