These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. <br> The maps are organised into empirical soil maps, digital soil maps, and data cube maps. <br> Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process.<br> Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. <br> Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). <br> A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below:<br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=af9c71935f024f4a8f64cb39f5eba007

The dataset provides information on soil chemistry from a 10 year chronosequence sample of restoration in southern Australia. The parameters include: A) Physical properties- Soil moisture (%), Gravel (%) - ( >2.0 mm), Soil Texture, i.e.Course Sand (%) (200-2000 µm), Fine Sand (%) - (20-200 µm), Sand (%), Silt (%) (2-20 µm), Clay (%) (<2 µm), and B) Chemical properties- such as, Ammonium Nitrogen (mg/Kg), Nitrate Nitrogen (mg/Kg), Phosphorus Colwell (mg/Kg), Potassium Colwell (mg/Kg), Sulphur (mg/Kg), Organic Carbon (%), Conductivity (dS/m), pH (CaCl2), pH (H2O), DTPA Copper (mg/Kg), DTPA Iron (mg/Kg), DTPA Manganese (mg/Kg), DTPA Zinc (mg/Kg), Exc. Aluminium (meq/100g), Exc. Calcium (meq/100g), Exc. Magnesium (meq/100g), Exc. Potassium (meq/100g), Exc. Sodium (meq/100g) and Boron Hot CaCl2 (mg/Kg). This data would have application for land managers. The soil chemistry data is also related to the eDNA OTU table published on "https://doi.org/10.4227/05/5878480a91885", titled "Revegetation rewilds the soil bacterial microbiome of an old field. Part 1: OTU raw data matrix", and as such it would have an appeal to researchers undertaking a meta-analysis on eDNA and restoration outcomes.