Advanced Machine Learning for Predicting Soil Organic Carbon Levels

Overview

Embark on an end-to-end machine learning project designed to revolutionize soil organic carbon (SOC) prediction. Leveraging the power of Google Earth Engine, GEEMAP, and EE, we streamline the data collection process and automate computation of various environmental variables from diverse open-source datasets. Sophisticated techniques, including model evaluation, hyperparameter tuning, grid search, and k-fold cross-validation, are integrated to optimize our predictive models.

Objectives

This comprehensive project aims to achieve the following objectives:

• Accurate SOC Prediction: Utilize advanced machine learning methodologies for precise SOC level predictions.
• Enhanced Model Performance: Incorporate model evaluation techniques to improve model accuracy and robustness.
• Optimal Model Configuration: Select the most effective model configuration to ensure reliable SOC predictions.
• End-to-End Automation: Automate data collection, extraction, and computation of environmental variables using Google Earth Engine, GEEMAP, and EE.

Model Evaluation Parameters

Rigorously evaluate model performance using a comprehensive set of metrics:

• Mean Squared Error (MSE) and Mean Absolute Error (MAE): Assess prediction accuracy and precision.
• R-squared (R²): Measure the proportion of variance in SOC levels explained by independent variables.
• Feature Importance: Identify significant features contributing to SOC predictions.
• Area Under the Curve (AUC): Evaluate overall model performance comprehensively.

Relationship Measurement

Employ advanced techniques to understand the intricate relationship between independent variables and SOC levels:

• OLS Regression: Gain insights into the linear relationship between features and SOC levels.
• Correlation Analysis: Explore correlations between SOC levels and independent variables, revealing complex interactions.

Advanced Techniques

Leverage cutting-edge techniques to optimize predictive models:

• Hyperparameter Tuning: Fine-tune model parameters for optimal performance.
• Grid Search: Exhaustively search parameter combinations to identify the best model configuration.
• k-Fold Cross-Validation: Assess model generalization and robustness across different data splits.

Data Collection and Automation

Automate the data collection process and computation of environmental variables using Google Earth Engine, GEEMAP, and EE:

• Open-Source Data Collection: Collect data from various open-source repositories available through Google Earth Engine.
• Automated Data Extraction: Utilize GEEMAP and EE to extract relevant data efficiently.
• Computation of Environmental Variables: Automate computation of variables such as NDVI, LST, and rainfall using Google Earth Engine's processing capabilities.

Variables

• Independent Variables:

  1. Landuse Landcover
  2. NDVI (Normalized Difference Vegetation Index)
  3. LST (Land Surface Temperature)
  4. NDMI (Normalized Difference Moisture Index)
  5. SMI (Soil Moisture Index)
  6. Soil Type
  7. Rainfall
  8. Temperature
  9. Slope
  10. Elevation

• Dependent Variable:

  • Soil Organic Carbon (SOC) sampling value