MG.112 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: DEM, ERa, RSS, VI

  • Sample size: 112

  • Number of Features: 17

  • Coordinates: With coordinates (EPSG: 32721)

  • Location: Mato Grosso, Brazil

  • Sampling Design: Regular grid sampling

  • Study Area Size: 111 ha

  • Geological Setting: Unknown

  • 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: March 2022

  • Sampling Depth: 0 – 20 cm

pH

  • Code: pH_target

  • Unit: Unitless

  • Protocol: Measured in water suspension with a glass electrode with a 2.5:1 liquid:soil volumetric ratio

  • Sampling Date: March 2022

  • 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: March 2022

  • Sampling Depth: 0 – 20 cm

Groups of Features:

DEM – Digital Elevation Model and Terrain Parameters

  • Number of Features: 2

  • Code(s): Altitude, Slope

  • Unit: Altitude in m, Slope in °

  • Sensing: Digital elevation model raster (30 m) based on synthetic aperture radar from “Copernicus Open Access Hub”

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

  • Sampling Date: April 2011

ERa – Apparent Electrical Resistivity

  • Number of Features: 1

  • Code(s): ERa

  • Unit: Ω m

  • Sensing: Electrical conductivity disc array (VERIS Technologies, Salinas, USA) on Veris U3 platform with exploration depth of 0 - 30 cm, in-situ

  • Processing: Average of five adjacent ERa measurements around the soil sampling locations to align sensing- with soil sampling locations

  • Sampling Date: March 2022

RSS – Remote Sensing Derived Spectral Data

  • Number of Features: 12

  • Code(s): B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12

  • Unit: Unitless

  • Sensing: Sentinel-2 bare soil image (Level-2A) from “Copernicus Open Access Hub”

  • Processing: Extracting RSS values from raster at soil sampling locations

  • Sampling Date: October 2022

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: May 2022

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("MG.112")
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. (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.

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.