LimeSoDa 0.1.0

Precision Liming Soil Datasets (Lime.SoDa) for Python

LimeSoDa

Precision Liming Soil Datasets (LimeSoDa) is a collection of datasets from a field- and farm-scale soil mapping context and this is the associated python dataset package LimeSoDa. These datasets are 'ready-to-go' for modeling purposes, as they contain target soil properties and features in a tabular format. Target soil properties for all datasets are; soil organic matter (SOM) or -carbon (SOC), pH and clay, while the features for modeling are dataset-specific. The goal of LimeSoDa is to enable more reliable benchmarking and comparison of various modeling approaches in Digital Soil Mapping and Pedometrics by providing an open collection of multiple datasets.

Installation

Requirements

• Python 3.8 or later
• numpy >= 1.23.0
• pandas >= 1.5.0
• scikit-learn >= 1.0.0

Optional Development Dependencies

• pytest >= 7.0.0
• black >= 22.0.0
• isort >= 5.0.0
• flake8 >= 4.0.0

Installing with pip

Install LimeSoDa via pip:

pip install LimeSoDa

For development dependencies:

pip install LimeSoDa[dev]

Installing from Source

To install LimeSoDa from source:

1. Clone the repository:

   git clone https://github.com/a11to1n3/LimeSoDa.git
   cd LimeSoDa
   

2. Install the package:

   pip install -e .
   

   For development dependencies:

   pip install -e .[dev]
   

Verifying Installation

To verify that LimeSoDa is installed correctly, run:

import LimeSoDa
print(LimeSoDa.__version__)

Quick Start

Get started with LimeSoDa by accessing and exploring a dataset:

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

# Set random seed
np.random.seed(2025)

# Load dataset
BB_250 = load_dataset('BB.250')

# Perform 10-fold CV
y_true_all = []
y_pred_all = []

for fold in range(1, 11):
    X_train, X_test, y_train, y_test = split_dataset(BB_250, fold=fold, targets='SOC_target')
    
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    y_true_all.extend(y_test.values)
    y_pred_all.extend(y_pred)

# Calculate overall performance
y_true_all = np.array(y_true_all)
y_pred_all = np.array(y_pred_all)
mean_r2 = r2_score(y_true_all, y_pred_all)
mean_rmse = np.sqrt(mean_squared_error(y_true_all, y_pred_all))

print("\nSOC prediction (10-fold CV):")
print(f"Mean R-squared: {mean_r2:.7f}")  # Mean R-squared: 0.7507837
print(f"Mean RMSE: {mean_rmse:.7f}")     # Mean RMSE: 0.2448791

Available Datasets

LimeSoDa includes a diverse collection of datasets, each varying in sample size and geographic focus:

Dataset ID	Sample Size	Target Properties	Feature Groups	Coordinates
B.204	204	SOC, pH, Clay	DEM, RSS, VI	EPSG:32723
BB.250	250	SOC, pH, Clay	DEM, ERa, Gamma, pH-ISE, RSS, VI	EPSG:25833
BB.30_1	30	SOC, pH, Clay	DEM, ERa, pH-ISE, VI	EPSG:25833
BB.30_2	30	SOC, pH, Clay	DEM, ERa, Gamma, RSS, VI	EPSG:25833
BB.51	51	SOC, pH, Clay	DEM, ERa, pH-ISE	EPSG:25833
BB.72	72	SOC, pH, Clay	DEM, ERa, Gamma, pH-ISE, RSS, VI	EPSG:25833
CV.98	98	SOC, pH, Clay	vis-NIR	NA
G.104	104	SOC, pH, Clay	DEM, RSS, VI	EPSG:32722
G.150	150	SOC, pH, Clay	DEM, ERa, RSS, VI	EPSG:32722
H.138	138	SOC, pH, Clay	MIR	EPSG:32649
MG.112	112	SOC, pH, Clay	DEM, ERa, RSS, VI	EPSG:32721
MG.44	44	SOC, pH, Clay	vis-NIR	EPSG:32721
MGS.101	101	SOC, pH, Clay	DEM, RSS, VI	EPSG:32721
MWP.36	36	SOC, pH, Clay	DEM, RSS	EPSG:32633
NRW.115	115	SOC, pH, Clay	MIR	NA
NRW.42	42	SOC, pH, Clay	MIR	NA
NRW.62	62	SOC, pH, Clay	MIR	NA
NSW.52	52	SOC, pH, Clay	DEM, RSS	EPSG:32755
O.32	32	SOC, pH, Clay	MIR	NA
PC.45	45	SOC, pH, Clay	CSMoist, ERa	NA
RP.62	62	SOC, pH, Clay	ERa, Gamma, NIR, pH-ISE, VI	NA
SA.112	112	SOC, pH, Clay	DEM, ERa, Gamma, NIR, pH-ISE, VI	NA
SC.50	50	SOC, pH, Clay	DEM, ERa	EPSG:32722
SC.93	93	SOC, pH, Clay	vis-NIR	EPSG:32722
SL.125	125	SOM, pH, Clay	ERa, vis-NIR	EPSG:4326 (dummy)
SM.40	40	SOC, pH, Clay	DEM, ERa	EPSG:32633
SP.231	125	SOM, pH, Clay	vis-NIR	EPSG:32654
SSP.460	460	SOC, pH, Clay	vis-NIR	NA
SSP.58	58	SOC, pH, Clay	vis-NIR	NA
UL.120	120	SOM, pH, Clay	ERa, vis-NIR	EPSG:4326 (dummy)
W.50	50	SOC, pH, Clay	DEM, ERa, VI, XRF	NA

Datasets comprise:

• Main Dataset: Contains soil properties and features
• Validation Folds: Pre-defined 10-fold cross-validation splits
• Coordinates: Provided where available

Features

The following groups of features are present in datasets of LimeSoDa:

• Capacitive soil moisture sensor (CSMoisture)
• Digital elevation model and terrain parameters (DEM)
• Apparent electrical resistivity (ERa)
• Gamma-ray activity (Gamma)
• Mid infrared spectroscopy (MIR)
• Near infrared spectroscopy (NIR)
• Ion selective electrodes for pH determination (pH-ISE)
• Remote sensing derived spectral data (RSS)
• X-ray fluorescence derived elemental concentrations (XRF)
• Vegetation Indices (VI)
• Visible- and near infrared spectroscopy (vis-NIR)

Documentation

Comprehensive documentation and usage examples are available in the examples directory.

Citation

If you utilize this package in your research, please cite the associated paper:

@article{schmidinger2025limesoda,
  title={LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping},
  author={Schmidinger, J. and Vogel, S. and Barkov, V. and Pham, A.-D. and Gebbers, R. and Tavakoli, H. and Correa, J. and Tavares, T. R. and Filippi, P. and Jones, E. J. and Lukas, V. and Boenecke, E. and Ruehlmann, J. and Schroeter, I. and Kramer, E. and Paetzold, S. and Kodaira, M. and Wadoux, A. M. J.-C. and Bragazza, L. and Metzger, K. and Huang, J. and Valente, D. S. M. and Safanelli, J. L. and Bottega, E. L. and Dalmolin, R. S. D. and Farkas, C. and Steiger, A. and Horst, T. Z. and Ramirez-Lopez, L. and Scholten, T. and Stumpf, F. and Rosso, P. and Costa, M. M. and Zandonadi, R. S. and Wetterlind, J. and Atzmueller, M.},
  year={2025},
  journal={XXX},
  volume={XXX},
  doi   = {XXX}
}

License

LimeSoDa is licensed under CC BY-SA 4.0.

Contributing

We welcome contributions! Feel free to submit a Pull Request to enhance LimeSoDa.