Limitations and Sources of Uncertainty:
Data Quality: The accuracy and reliability of the input data (e.g., MODIS, Landsat) used in spatiotemporal fusion can impact the quality of the downscaled predictors. Inaccuracies in the input data can introduce errors in the fusion process.
Model Complexity: Machine learning models like XGBoost may be prone to overfitting, especially when trained on limited data. Overfitting can lead to poor generalization and reduced performance on unseen data.
Feature Selection: The selection of predictors in the machine learning model is crucial. Inadequate or irrelevant predictors can introduce noise and affect the model's predictive capability.
Spatial Heterogeneity: Variability in soil moisture patterns within the study area may pose challenges for both spatiotemporal fusion and machine learning. Capturing this spatial heterogeneity accurately is essential for reliable upscaling.
Addressing Limitations:
Data Quality Assurance: Implementing data quality checks and validation procedures to ensure the accuracy of input data. Pre-processing steps like outlier removal and data normalization can help improve data quality.
Regularization Techniques: Applying regularization techniques in machine learning models to prevent overfitting. Techniques like early stopping, cross-validation, and hyperparameter tuning can optimize model performance.
Feature Engineering: Conducting thorough feature selection and engineering to include relevant predictors and eliminate noise. Techniques like principal component analysis (PCA) or feature importance analysis can aid in selecting informative features.
Spatial Analysis: Incorporating spatial statistics and geostatistical methods to account for spatial heterogeneity. Spatial autocorrelation analysis and variogram modeling can help capture spatial dependencies in the data.