Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Lee, Jangho; Shi, Yingxi Rona; Cai, Changjie; Ciren, Pubu; Wang, Jianwu; Gangopadhyay, Aryya; Zhang, Zhibo

doi:10.3390/rs13030456

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

2021
By Lee, Jangho ; Shi, Yingxi Rona ; Cai, Changjie ; ...
Source: Remote Sens. 2021, 13(3), 456

[PDF-5.26 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

Details You May Also Like

Details:

Journal Title:

Remote Sensing
Personal Author:

Lee, Jangho ; Shi, Yingxi Rona ; Cai, Changjie ; Ciren, Pubu ; Wang, Jianwu ; ... More +

Lee, Jangho ; Shi, Yingxi Rona ; Cai, Changjie ; Ciren, Pubu ; Wang, Jianwu ; Gangopadhyay, Aryya ; Zhang, Zhibo Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) Less -
Description:

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track.
Keywords:

[+]

CALIOP Deep Learning Dust Detection Machine Learning VIIRS
Source:

Remote Sens. 2021, 13(3), 456
DOI:

https://doi.org/10.3390/rs13030456
Document Type:

Journal Article
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:37c38a76c9945ba9da905beb7f702cadc1c4244fadc1ccfa9a0134e632855fd9
Download URL:

https://staging-noaa.cdc.gov/view/noaa/41804/noaa_41804_DS1.pdf
File Type:

No Additional Files

More +

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Details:

Supporting Files

You May Also Like