SC.50 Dataset
=============

Description
-----------
* Target Soil Properties: SOC, pH, Clay
* Groups of Features: DEM, ERa 
* Sample size: 50
* Number of Features: 3
* Coordinates: With coordinates (EPSG: 32722)
* Location: Santa Catarina, Brazil
* Sampling Design: Regular grid sampling
* Study Area Size: 13 ha
* Geological Setting: Heavily weathered soils originating from Mesozoic basalt rocks
* Previous Data Publication: Full dataset published in Bottega & Safanelli (2024)
* Contact Information:
    * Eduardo Bottega (bottega.elb@gmail.com), Federal University of Santa Maria
    * José Lucas Safanelli (jsafanelli@woodwellclimate.org), Woodwell Climate Research Center
* 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.50 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 titration after oxidization of the organic carbon (Walkley & Black 1934)
    * Sampling Date: November 2013
    * Sampling Depth: 0 – 20 cm

pH
    * Code: ``pH_target``
    * Unit: Unitless
    * Protocol: Measured in water suspension with a glass electrode with a 5:1 liquid:soil volumetric ratio
    * Sampling Date: November 2013
    * 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, German adaptation (DIN ISO 11277)
    * Sampling Date: November 2013
    * 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: October 2011

ERa – Apparent Electrical Resistivity
    * Number of Features: 1
    * Code(s): ``ERa``
    * Unit: Ω m
    * Sensing: LandMapper ERM-02 conductivity meter (Landviser, League City, USA) with exploration depth of 0 - 20 cm, in-situ
    * Processing: None
    * Sampling Date: November 2014

Examples
--------

.. code-block:: python

    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.50")
    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
----------

Bottega, E. L. & Safanelli J. L. (2024). Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil". Zenodo repository. https://doi.org/10.5281/zenodo.13770031

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.