학술논문
Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks
Document Type
Periodical
Author
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 15:6473-6484 2022
Subject
Language
ISSN
1939-1404
2151-1535
2151-1535
Abstract
In this article, we present three algorithms for aerosol parameters retrieval from TROPOspheric Monitoring Instrument measurements in the $\text {O}_{2}$ A-band. These algorithms use neural networks 1) to emulate the radiative transfer model and a Bayesian approach to solve the inverse problem, 2) to learn the inverse model from the synthetic radiances, and 3) to learn the inverse model from the principal-component transform of synthetic radiances. The training process is based on full-physics radiative transfer simulations. The accuracy and efficiency of the neural network based retrieval algorithms are analyzed with synthetic and real data.