<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>

We investigated recovery of soil chemical properties after restoration in semi-arid Western Australia, hypothesising that elevated nutrient concentrations would gradually decline post planting, but available phosphorus (P) concentrations would remain higher than reference conditions. We used a space-for-time substitution approach, comparing 10 planted old field plots with matched fallow cropland and reference woodlands. Sampling on planted old fields and reference woodland plots was stratified into open patches and under tree canopy to account for consistent differences between these areas. Soil samples to 10 cm depth were collected at 20 points across 30 plots. Ten samples were randomly collected and combined from locations beneath trees and a further 10 samples collected in gaps and combined, resulting in one soil sample for beneath tree canopy and another one for gap areas. Sampling occurred in autumn 2017 to capture potentially high concentrations of soil nitrate following the seasonal die-back of exotic annual plants typical of this Mediterranean-climate region. Samples were stored at 4 °C in plastic zip-lock bags until delivery to the CSBP Limited (Bibra Lake, WA) laboratories. Chemical parameters measured were plant available P (Colwell), plant available N (nitrate and ammonium), total N, plant available potassium (Colwell) and plant available sulphur (KCl 40). Lastly, electrical conductivity, pH (H2O, CaCl2), and soil texture were quantified as differences among plots could affect nutrient availability and soil chemistry. Soil available nutrients were also measured using Plant Root Simulator (PRS)TM resin probes (Western Ag Innovations, 2010, https://www.westernag.ca/inn). Probes contain anion or cation exchange membranes within a plastic stake. The membranes act as a sink for collecting nutrients and continuously absorb ions during deployment. Four anion and cation probes were placed vertically in the top 15 cm of soil at each stratification. Probes were left in the ground for three months during the growing season, from August to November 2017. This period was deemed suitable for semi-arid regions to achieve sufficient nutrient uptake but not too long to saturate probes. After removal, probes were cleaned with deionized water and sent to Western Ag Innovations (Canada) for analysis. All soil chemical analyses were conducted under laboratory conditions using standard test procedures. PRS probe nutrients are reported as micrograms/10cm2/time.

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.

This dataset contains maps of woody vegetation extent and woody foliage projective cover (FPC) for New South Wales at 5 metre resolution. <br /><br /> Woody vegetation is a key feature of our landscape and an integral part of our society. We value it because it contributes to the economy, protects the land, provides us with recreation, and gives refuge to the unique and diverse range of fauna that we regard so highly. Yet it poses a significant threat to us in times of fire and storm. So information about trees is vital for a range of business, property planning, monitoring, risk assessment, and conservation activities. <br /><br /> The datasets are: <br /> Woody vegetation extent. A presence/absence map showing areas of trees and shrubs, taller than two metres, that are visible at the resolution of the imagery used in the analysis. This shows the location, extent, and density of foliage cover for stands of woody vegetation, enabling identification of small features such as trees in paddocks and scattered woodlands through to the largest expanses of forest in the State. Woody extent products contain 'bcu' in the file name.<br /><br /> Woody foliage projective cover (FPC). FPC is a measure of the proportion of the ground area covered by foliage (or photosynthetic tissue) held in a vertical plane and is a measure of canopy density. Woody FPC products contain 'bcv' in the file name. <br /><br /> Both mosaics and tiles are available, along with a shape file that identifies the location of the tiles.

This collection contains the data used in the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool. From the Data menu, explore and download individual supplementary layers, or download the entire datapack. The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Australian Bureau of Agricultural and Resource Economics and Sciences that enables multi-criteria analysis (MCA) using spatial data. It is a powerful, easy-to-use and flexible decision-support tool that promotes: - framework for assessing options <br> - common metric for classifying, ranking and weighting of the data <br> - tools to compare, combine and explore spatial data <br> - live-update of alternative scenarios and trade-offs. <br>