Deep Gaussian processes for biogeophysical parameter retrieval and model inversion.

Area of Science:

• Remote Sensing and Earth Observation
• Machine Learning
• Geophysics

Background:

• Parameter retrieval and model inversion are crucial in remote sensing.
• Current methods include costly direct RTM inversion, limited statistical inversion, and hybrid modeling.
• Shallow Gaussian Processes (GPs) offer accuracy but struggle with complex, hierarchical models.

Purpose of the Study:

• Introduce Deep Gaussian Processes (DGPs) for bio-geo-physical model inversion.
• Address limitations of shallow GPs in handling complex, nonlinear RTMs.
• Improve prediction accuracy and scalability for large remote sensing datasets.

Main Methods:

• Utilized Deep Gaussian Processes (DGPs), a hierarchical extension of GPs.
• Applied DGPs to invert complex radiative transfer models (RTMs).
• Evaluated performance on estimating surface temperature, dew point, and water quality parameters.

Main Results:

• DGPs effectively capture complex, hierarchical relationships in RTMs.
• Demonstrated improved prediction accuracy compared to shallow GP models.
• Showcased scalability and efficiency for large-scale remote sensing data analysis.

Conclusions:

• DGPs provide a powerful and expressive framework for complex remote sensing model inversion.
• The methodology offers enhanced accuracy and better handling of big datasets.
• DGPs represent a significant advancement for bio-geo-physical parameter retrieval in Earth observation.