Modeling in remote sensing involves diverse approaches to analyze, interpret, and predict phenomena observed in remotely sensed data. These approaches are broadly categorized into analytical, semi-analytical, and empirical methods, each offering unique strengths and applications.
Analytical modeling uses physical principles and equations to simulate interactions between electromagnetic waves and materials. This method focuses on radiative transfer equations and the physics of light interaction. It provides a robust foundation for understanding the theoretical aspects of remote sensing processes.
Example topics include:
Semi-analytical modeling combines theoretical foundations with empirical relationships to estimate key environmental variables. These models use indices or parameterizations derived from physical laws and observational data.
Example indices and models:
Empirical modeling involves creating data-driven models using machine learning or statistical approaches. These methods rely on training and testing datasets to learn relationships and predict outcomes, often without explicit reliance on physical or semi-analytical principles.
Common techniques include:
For more on machine learning in remote sensing, visit NASA Earthdata.
Modeling in remote sensing is applied in numerous domains:
Common tools for modeling in remote sensing include:
Explore these resources for deeper insights:
