SC.93 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: vis-NIR

  • Sample size: 93

  • Number of Features: 2,146

  • Coordinates: With coordinates (EPSG: 32722)

  • Location: Santa Catarina, Brazil

  • Sampling Design: Conditioned latin hypercube sampling based on terrain parameters

  • Study Area Size: 108 ha

  • Geological Setting: Heavily weathered soils originating from volcanic rock of the Serra Geral Formation (basalt and dacite)

  • 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 SC.93 dataset contains soil measurements and features organized in a dataframe with the following properties:

Target Soil Properties:

SOC - Soil Organic Carbon

  • Code: SOC_target

  • Unit: %

  • Protocol: Measured through light absorption after oxidization of the organic carbon in suspension (Tedesco et al. 1995)

  • Sampling Date: December 2016

  • Sampling Depth: 0 - 20 cm

pH

  • Code: pH_target

  • Unit: Unitless

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

  • Sampling Date: December 2016

  • 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: December 2016

  • Sampling Depth: 0 - 20 cm

Groups of Features:

vis-NIR – Visible and Near Infrared Spectroscopy

  • Number of Features: 2,146

  • Code(s): wl_355, wl_356, wl_357 … wl_2500

  • Unit: % (Reflectance)

  • Sensing: ASD FieldSpec 4 (Analytical Spectral Devices Inc., Boulder, USA), on dried and sieved samples (<2 mm) in the laboratory, spectral range was 355 - 2,500 nm at 3 - 8 nm intervals

  • Processing: Resampling to 1 nm intervals

  • Sampling Date: March 2017

  • Spectral Information (After Data Processing):

    • Data Representation: Wavelength (in nm)

    • Spectral Resolution: 1 nm

    • Spectral Range: 355 – 2500 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("SC.93")
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.

Tedesco, M.J., Gianello, C., Bissani, C., Bohnen, H. & Volkweiss, S.J. (1995) Análise de solo, plantas e outros materiais. [Analysis of soil, plants and other materials.] 2nd Edition, Departamento de Solos da Universidade Federal do Rio Grande do Sul, Porto Alegre, 174.