A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing

A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing

In this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRT...

Full description

Bibliographic Details
Main Authors: Xingming Liang, Kevin Garrett, Quanhua Liu, Eric S. Maddy, Kayo Ide, Sid Boukabara
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Advanced Technology Microwave Sounder (ATMS)
Community Radiative Transfer Model (CRTM)
data assimilation
deep learning (DL)
Jacobian
remote sensing
Online Access:https://ieeexplore.ieee.org/document/9909997/

Internet

https://ieeexplore.ieee.org/document/9909997/

Similar Items