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:
    * Taciara Zborowski Horst (taciaraz@utfpr.edu.br), Federal University of Technology – Paraná
    * Ricardo Simão Diniz Dalmolin (dalmolin@ufsm.br), Federal University of Santa Maria
* 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
--------

.. 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.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.