Digital soil mapping of several soil properties: Forest Hill Agricultural Research Station

Brief description

A soil survey was conducted at CSIRO’s “Forest Hill” research farm to support scientific research, land management, infrastructure planning, and development activities.

This dataset complements Zund et al. (2024) that describes the main soil types and describes their morphological, chemical, and physical properties across the research farm.

This dataset contains digital soil mapping outputs for a range of agronomically relevant attributes, including soil texture, whole-soil bulk density, pH, electrical conductivity, exchangeable cations, and total soil carbon and nitrogen. In this context, comprehensive digital soil mapping refers to three-dimensional (3D) mapping using model-based approaches, with explicit quantification of prediction uncertainty.

Spatial predictions (and associated uncertainties) were made on a 10 m × 10 m raster grid.

Zund, Peter; Cocks, Brett; & Malone, Brendan (2024): Forest Hill Agricultural Research Station Soil Map. v1. CSIRO. Data Collection. https://doi.org/10.25919/xsdz-nj28
Lineage: Digital Soil Mapping Workflow
The development of the digital soil mapping outputs followed a structured and systematic workflow, comprising the following key steps:

1. Data-Informed Site Selection
Soil core sampling locations were strategically selected to capture the maximum possible spatial variability in soil properties across the landscape.

2. Soil Survey and Coring
Field surveys were conducted to extract intact soil cores for detailed analysis.

3. Proximal Soil Sensing and Laboratory Analysis
Each soil core was scanned using visible–near infrared (vis-NIR) spectroscopy and gamma-ray attenuation. Targeted subsampling was then undertaken for laboratory wet chemistry analysis. The resulting analytical data, combined with the spectral responses, were used to calibrate soil inference models capable of generating full-profile characterisations across all cores.

4. Compilation of Environmental Covariates
A suite of gridded environmental covariate layers was assembled to support the spatial modelling process. These covariates represent a diverse range of soil-forming factors.

5. Digital Soil Mapping
Calibrated soil profile data were integrated with the environmental covariates to develop spatial prediction models tailored to each soil attribute.

This workflow aligns with the approach described in Malone et al. (2022). Please see accompanying report for detailed steps and treatment of the data and modelling processes.


Organisation of Outputs
For each soil attribute, the outputs are organised into a folder containing three sub-folders:

mapsout:
Contains visualisations of the 50th percentile (median) predictions for each specified depth.

model_diogs:
Includes model diagnostics and performance metrics (from both calibration and testing sets) for each bootstrap iteration. Reported metrics include:

- Coefficient of determination (R²)

- Lin’s concordance correlation coefficient (CCC)

- Mean squared prediction error (MSE)

- Root mean squared prediction error (RMSE)

- Mean prediction error (bias)

rasters:
Contains GeoTIFF raster files of the mapped predictions and associated uncertainty. These are structured by depth and output type.

Depth Convention and Output Types
Predictions are generated for standard depth intervals from the soil surface to 180 cm. The file naming convention denotes these as d1 to d10, corresponding to the following depth slices:

d1: 0–10 cm

d2: 10–20 cm

d3: 20–40 cm

d4: 40–60 cm

d5: 60–80 cm

d6: 80–100 cm

d7: 100–120 cm

d8: 120–140 cm

d9: 140–160 cm

d10: 160–180 cm


Each depth slice includes three prediction types:

lower_percentile: 5th percentile of bootstrap predictions (lower confidence bound)

50th_percentile: Median (central estimate)

upper_percentile: 95th percentile (upper confidence bound)

These represent uncertainty bounds around the modelled predictions at each grid cell.

Reference
Malone B, Stockmann U, Glover M, McLachlan G, Engelhardt S, Tuomi S (2022). Digital soil survey and mapping underpinning inherent and dynamic soil attribute condition assessments. Soil Security, 6, 100048.