NSW.52 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: DEM, RSS

  • Sample size: 52

  • Number of Features: 5

  • Coordinates: With coordinates (EPSG: 32755)

  • Location: New South Wales, Australia

  • Sampling Design: Two sampling designs and campaigns over multiple fields: (1) random sampling from k-means clustering based on digital soil maps, digital elevation model, air-borne gamma radiometric data and remote sensing satellite images, and (2) stratified random sampling based on soil type and land use parameters

  • Study Area Size: 1,158 ha

  • Geological Setting: Alluvial deposits of basaltic sediments

  • Previous Data Publication: None

  • Contact Information:

  • License: CC BY-SA 4.0

  • Publication/Modification Date (d/m/y): 28.02.25, version 1.0

  • Changelog:

    • Version 1.0 (28.02.25): Initial release

Details

Dataset

The dataset contains the following target soil properties and features:

Target Soil Properties:

SOC - Soil Organic Carbon

  • Code: SOC_target

  • Unit: %

  • Protocol: Measured through titration after oxidization of the organic carbon (Walkley & Black 1934)

  • Sampling Date: July 2018 and December 2018

  • Sampling Depth: 0 - 10 cm

pH

  • Code: pH_target

  • Unit: Unitless

  • Protocol: Measured in CaCl₂ suspension with a glass electrode with a 5:1 liquid:soil volumetric ratio

  • Sampling Date: July 2018 and December 2018

  • Sampling Depth: 0 - 10 cm

Clay

  • Code: Clay_target

  • Unit: %

  • Protocol: Hydrometer method; separation of the fractions by sieving and sedimentation. Measurement of the separated fractions by weighing the density of the suspension (Gee and Bauder 1979)

  • Sampling Date: July 2018 and December 2018

  • Sampling Depth: 0 - 10 cm

Groups of Features:

DEM – Digital Elevation Model and Terrain Parameters

  • Number Features: 2

  • Code(s): Altitude, Slope

  • Unit: Altitude in m, Slope in °

  • Sensing: Digital elevation model raster (5 m) based on LiDAR and photogrammetry from the “Elevation and Depth – Foundation Spatial Data (ELVIS)”

  • Processing: Calculating Slope with terrain function of the raster R-package, extracting DEM values from raster at soil sampling locations

  • Sampling Date: April 2016

RSS – Remote Sensing Derived Spectral Data

  • Number Features: 3

  • Code(s): B02, B8A, B11

  • Unit: Unitless

  • Sensing: Sentinel-2 bare soil image (Level-2A) from “Copernicus Open Access Hub”, with bands of 10 - 20 m spatial resolution

  • Processing: Extracting RSS values from raster at soil sampling locations, selecting bands spread throughout the spectral range with lower intercorrelation due to low sample size

  • Sampling Date: July 2018

Examples

from LimeSoDa import load_dataset, split_dataset
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import numpy as np

# Load and explore the dataset
data = load_dataset("NSW.52")
dataset = data["Dataset"]
folds = data["Folds"]
coords = data["Coordinates"]

# Split into train/test using fold 1
X_train, X_test, y_train, y_test = split_dataset(
    data=data,
    fold=1,
    targets=["pH_target", "SOC_target", "Clay_target"]
)

# Fit model and get predictions
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

# Calculate performance metrics
r2 = r2_score(y_test, predictions)
rmse = np.sqrt(mean_squared_error(y_test, predictions))
print(f"R-squared: {r2:.7f}")
print(f"RMSE: {rmse:.7f}")

References

Gee, G. W., & Bauder, J. W. (1979). Particle size analysis by hydrometer: a simplified method for routine textural analysis and a sensitivity test of measurement parameters. Soil Science Society of America Journal, 43(5), 1004-1007.

Walkley, A. & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37(1), 29-38.