RP.62 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: ERa, Gamma, NIR, pH-ISE, VI

  • Sample size: 62

  • Number of Features: 1,410

  • Coordinates: Without coordinates because of privacy concerns

  • Location: Rhineland-Palatinate, Germany

  • Sampling Design: Regular grid sampling

  • Study Area Size: 3.3 ha

  • Geological Setting: Pleistocene periglacial slope deposits consisting of Weichselian loess with variable amounts of weathered Devonian sand-, silt-, and claystones and scattered Tertiary basalt bombs

  • 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: October 2017

  • Sampling Depth: 0 - 30 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 (DIN ISO 10390)

  • Sampling Date: May 2020

  • Sampling Depth: 0 - 30 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, German adaptation (DIN ISO 11277)

  • Sampling Date: October 2017

  • Sampling Depth: 0 - 30 cm

Groups of Features:

ERa – Apparent Electrical Resistivity

  • Number of Features: 1

  • Code(s): ERa

  • Unit: Ω m

  • Sensing: Array of multiple rolling electrodes (Geophilus company, Caputh, Germany) on RapidMapper platform with exploration depth of 0 - 50 cm, in-situ

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

  • Sampling Date: September 2020

Gamma

  • Number of Features: 5

  • Code(s): G_Total_Counts, G_K, G_U, G_Th, G_Cs

  • Unit: Unitless

  • Sensing: Passive gamma sensor (MS-2000-CsI-MTS, Medusa Radiometrics BV, Groningen, Netherlands) on RapidMapper platform, in-situ

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

  • Sampling Date: September 2020

NIR – Near Infrared Spectroscopy

  • Number of Features: 1,401

  • Code(s): wl_1000, wl_1001, wl_1002 … wl_2400

  • Unit: % (Reflectance)

  • Sensing: NIR spectrometer (C11118GA, Hamamatsu Photonics K.K., Shizuoka Prefecture, Japan) on RapidMapper platform, in-situ, spectral range was 900 - 2550 nm at 15 nm intervals

  • Processing: Kriging to align sensing- with soil sampling locations, discarding noisy edges of the spectrum (900 - 1,000 nm & 2,400 - 2,550 nm), resampling to 1 nm intervals

  • Sampling Date: September 2020

  • Spectral Information (After Data Processing):

    • Data Representation: Wavelength (in nm)

    • Spectral Resolution: 1 nm

    • Spectral Range: 1,000 - 2,400 nm

pH-ISE – Ion Selective Electrodes for pH Determination

  • Number of Features: 1

  • Code(s): pH-ISE

  • Unit: Unitless

  • Sensing: Soil pH Manager (VERIS Technologies, Salinas, USA) on RapidMapper platform, in-situ

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

  • Sampling Date: September 2020

VI - Vegetation Indices

  • Number of Features: 2

  • Code(s): NDVI, GNDVI

  • Unit: Unitless

  • Sensing: Sentinel-2 image during vegetative period (Level-2A) from “Copernicus Open Access Hub”

  • Processing: Calculating NDVI as (B08 - B04) / (B08 + B04) and GNDVI as (B08 - B03) / (B08 + B03), extracting VI values from raster at soil sampling locations

  • Sampling Date: March 2017

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("RP.62")
dataset = data["Dataset"]
folds = data["Folds"]
coords = data["Coordinates"]  # Will be NA for RP.62

# 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

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.