Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia
A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust de...
Main Authors: | , , , , , , |
---|---|
Format: | Other/Unknown Material |
Language: | unknown |
Published: |
2014
|
Subjects: | |
Online Access: | http://hdl.handle.net/2060/20140005405 |
id |
ftnasantrs:oai:casi.ntrs.nasa.gov:20140005405 |
---|---|
record_format |
openpolar |
spelling |
ftnasantrs:oai:casi.ntrs.nasa.gov:20140005405 2023-05-15T13:06:16+02:00 Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia Lee, Jaehwa Park, Sang Seo Chang, Lim Seok Kim, Jeong Soo Lee, Sukjo Kim, Jhoon Ou, Steve Unclassified, Unlimited, Publicly available February 2014 application/pdf http://hdl.handle.net/2060/20140005405 unknown Document ID: 20140005405 http://hdl.handle.net/2060/20140005405 Copyright, Distribution under U.S. Government purpose rights CASI Earth Resources and Remote Sensing GSFC-E-DAA-TN13128 Remote Sensing of Environment; 141; 24-39 2014 ftnasantrs 2019-07-21T00:31:51Z A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia. Other/Unknown Material Aerosol Robotic Network NASA Technical Reports Server (NTRS) |
institution |
Open Polar |
collection |
NASA Technical Reports Server (NTRS) |
op_collection_id |
ftnasantrs |
language |
unknown |
topic |
Earth Resources and Remote Sensing |
spellingShingle |
Earth Resources and Remote Sensing Lee, Jaehwa Park, Sang Seo Chang, Lim Seok Kim, Jeong Soo Lee, Sukjo Kim, Jhoon Ou, Steve Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
topic_facet |
Earth Resources and Remote Sensing |
description |
A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia. |
format |
Other/Unknown Material |
author |
Lee, Jaehwa Park, Sang Seo Chang, Lim Seok Kim, Jeong Soo Lee, Sukjo Kim, Jhoon Ou, Steve |
author_facet |
Lee, Jaehwa Park, Sang Seo Chang, Lim Seok Kim, Jeong Soo Lee, Sukjo Kim, Jhoon Ou, Steve |
author_sort |
Lee, Jaehwa |
title |
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
title_short |
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
title_full |
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
title_fullStr |
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
title_full_unstemmed |
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia |
title_sort |
combined dust detection algorithm by using modis infrared channels over east asia |
publishDate |
2014 |
url |
http://hdl.handle.net/2060/20140005405 |
op_coverage |
Unclassified, Unlimited, Publicly available |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
CASI |
op_relation |
Document ID: 20140005405 http://hdl.handle.net/2060/20140005405 |
op_rights |
Copyright, Distribution under U.S. Government purpose rights |
_version_ |
1765998599594835968 |