Norfolk Island soil clay content DSM attributes

Full description

Surface soil clay % and maximum clay % to 2m soil depth are two of nine soil attributes analysed as one component of a digital soil mapping exercise undertaken as part of the Norfolk Island Water Resource Assessment (NIWRA). It was selected due to its importance to groundwater recharge, surface water storage and managed aquifer recharge. Clay percent is a parameter influencing surface water infiltration, subsoil permeability and water holding capacity and has also been applied in the gully dam suitability rules in relation to construction and engineering properties for gully dams. These raster data represent modelled datasets of clay percent and are derived from field measured site data, limited laboratory analysis and environmental covariates. Data values are expressed as a percent. The measured site data attribute soil texture is used to represent clay content as defined by the National Committee on Soil and Terrain 2009 (NCST). Companion datasets presenting reliability of these data is also provided and can be found described in the lineage section of this metadata record. Processing was carried out in the ranger package inside R and attributes were modelled using a Random Forest approach. Further clay percent information can be found in the NIWRA technical report (Petheram et al., 2020). The DSM process is described in Appendix E of the NIWRA technical report.
Lineage: The surface soil clay % and maximum clay % to 2m soil depth datasets have been generated from a range of inputs and processing steps. The following is an overview of the methods detailed in Petheram et al. 2020. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Created surface soil clay % and maximum clay% to 2m soil depth Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations coupled with environmental covariate 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 and remote sensing images. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of these DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and R squared results, giving an estimate of the reliability of the model predictions. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For continuous attributes the method for estimating reliability is the Coefficient of Variation. This data is supplied. Method 3: On-ground expert (qualitative) examination of outputs.