SL.125 Dataset

Description

  • Target Soil Properties: SOM, pH, Clay

  • Groups of Features: ERa, vis-NIR

  • Sample size: 125

  • Number of Features: 2,082

  • Coordinates: Without coordinates because of privacy concerns instead with dummy coordinates (EPSG: 4326)

  • Location: Skåne Län, Sweden

  • Sampling Design: Three sampling designs over multiple adjacent fields: (1) regular grid sampling, targeted sampling through surface tortoise sampling (Persson et al. 2023) based on (2) ERa and (3) reflectance from remote sensing

  • Study Area Size: 78 ha

  • Geological Setting: High spatial variability of sandy till, clay till with chalk and glacial clay

  • 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 SL.125 dataset contains soil measurements and features organized in a dataframe with the following properties:

Target Soil Properties:

SOM - Soil Organic Matter

  • Code: SOM_target

  • Unit: %

  • Protocol: Measured through the weight difference before and after ignition of the soil with additional correction for structural water from clay by using the formula: SOM = LI − 0.46 − 0.047 × clay content (%) (Ekström 1927)

  • Sampling Date: September 2006

  • Sampling Depth: 0 - 20 cm

pH

  • Code: pH_target

  • Unit: Unitless

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

  • Sampling Date: September 2006

  • Sampling Depth: 0 - 20 cm

Clay

  • Code: Clay_target

  • Unit: %

  • Protocol: Sieve-Pipette method, measured through fractioning the soil into the sand fractions by sieving, and the silt and clay fractions by sedimentation in water (Gee and Bauder 1986)

  • Sampling Date: September 2006

  • Sampling Depth: 0 - 20 cm

Groups of Features:

ERa – Apparent Electrical Resistivity

  • Number of Features: 1

  • Code(s): ERa

  • Unit: Ω m

  • Sensing: EM38 sensor (Geonics Ltd., Mississauga, Canada) with exploration depth of 0 - 150 cm, in-situ

  • Processing: Ordinary Kriging to align sensing- with soil sampling locations

  • Sampling Date: April 2006

vis-NIR – Visible and Near Infrared Spectroscopy

  • Number of Features: 2,081

  • Code(s): wl_420, wl_421, wl_422 … wl_2500

  • Unit: % (Reflectance)

  • Sensing: FieldSpec Pro FR scanning instrument (Analytical Spectral Devices Inc., Boulder, USA), on dried and sieved samples (<2 mm) in the laboratory, spectral range was 350 – 2,500 nm at 1.4 – 2.0 nm intervals

  • Processing: Discarding noisy edges of the spectrum (350 - 420 nm), resampling to 1 nm intervals

  • Sampling Date: September 2006

  • Spectral Information (After Data Processing):

    • Data Representation: Wavelength (in nm)

    • Spectral Resolution: 1 nm

    • Spectral Range: 420 - 2,500 nm

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("SL.125")
dataset = data["Dataset"]
folds = data["Folds"]
coords = data["Coordinates"]  # Note: Contains dummy 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", "SOM_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

Ekström, G. (1927). Klassifikation av Svenska Åkerjordar (Classification of Swedish arable soils). Sveriges Geologiska Undersökning, Ser C. 345, 161 pp.

Gee, G.W. & Bauder, J.W. (1986) Particle-Size Analysis. In: Klute, A., Ed., Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods, Agronomy Monograph No. 9, 2nd Edition, American Society of Agronomy/Soil Science Society of America, Madison, WI, 383-411.

Persson, K., Söderström, M. & Mutua, J. (2023). SurfaceTortoise: Find Optimal Sampling Locations Based on Spatial Covariate(s). R package version 2.0.1.