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Found 6 papers, 1 papers with code
Inference over radiative transfer models using variational and expectation maximization methods
Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling.
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding.
Deep Gaussian Processes for geophysical parameter retrieval
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest.
Joint Gaussian Processes for Biophysical Parameter Retrieval
Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models).
