Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction-Reference-Cited by-同舟云学术

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Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Liu Shengjie¹^ORCID,Zhang Lu²^ORCID

Affiliation:

1. Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA

2. Department of Population and Public Health Sciences, Keck School of Medicine, Division of Biostatistics, University of Southern California, Los Angeles, CA, USA

Funder

Southern California Environmental Health Sciences Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10526362.pdf?arnumber=10526362

Reference27 articles.

1. Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective

2. An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data

3. Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong

4. Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA)

5. Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites

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