FGARA Digital Soil Mapping Output - Soil Generic Group

Soil generic group (SGG) data has been created for a communication tool to simplify the complex information of individual soils and soil attributes for extension, planning and management. This data groups attributes of soils based around the Australian Soil Classification 'Order' and in some cases splitting of Orders by sub classifications of depth and feature to fit better within each SGG. It is also one of 19 attributes of soils chosen to underpin the land suitability assessment of the FGARA project through the digital soil mapping process (DSM). This raster data represents a modelled surface of 8 Soil generic groups with definitions and explanations supplied with the data. This data is derived from classified site data and environmental covariates.\nValues: \n1 Sand or loam over relatively friable clay subsoils; \n2 Friable non-cracking clay or clay loam soils; \n3 Seasonally or permanently wet soils; \n4 Red, yellow or grey loamy soils; \nNote no 5; \n6 Deep sandy soils; \n7 Shallow sandy and stony soils; \n8 Sand or loam over intractable clay subsoils; \n9 Cracking clay soils. \nThe attribute data file is named "GSGClasses.tif"\nAlso included are data reflecting confidence of the main dataset. This file is named "GSG_CI.tif". "CI" represents "confusion index".\nThe DSM process is described in the technical report: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO.\nThis raster data provides improved soil information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the “Land Suitability” component of FGARA projects.\nLineage: This data has been created from a range of inputs and processing steps. Below is an overview. Broadly, the steps were to: \n1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc.). \n2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. \n3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. \n4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. \n5. Create Soil Generic Group Digital Soil Mapping (DSM) key attribute output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements.\nQuality assessment of the attribute data is mapped spatially as a function of the model output by evaluating the rigour of the DSM attribute data using non-parametric bootstrapping of the DSM modelling. For more information refer to “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.