Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.
Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, incl...
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ftdoajarticles:oai:doaj.org/article:5df4f19b607947538b86002ccb9d97d1 2023-05-15T13:06:54+02:00 Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. Xinxin Ye Mina Deshler Alexi Lyapustin Yujie Wang Shobha Kondragunta Pablo Saide 2022-12-01T00:00:00Z https://doi.org/10.3390/rs14236113 https://doaj.org/article/5df4f19b607947538b86002ccb9d97d1 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/23/6113 https://doaj.org/toc/2072-4292 doi:10.3390/rs14236113 2072-4292 https://doaj.org/article/5df4f19b607947538b86002ccb9d97d1 Remote Sensing, Vol 14, Iss 6113, p 6113 (2022) aerosol optical depth MODIS VIIRS retrieval wildfire smoke Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14236113 2022-12-30T20:59:30Z Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 14 23 6113 |
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Open Polar |
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op_collection_id |
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language |
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topic |
aerosol optical depth MODIS VIIRS retrieval wildfire smoke Science Q |
spellingShingle |
aerosol optical depth MODIS VIIRS retrieval wildfire smoke Science Q Xinxin Ye Mina Deshler Alexi Lyapustin Yujie Wang Shobha Kondragunta Pablo Saide Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
topic_facet |
aerosol optical depth MODIS VIIRS retrieval wildfire smoke Science Q |
description |
Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less ... |
format |
Article in Journal/Newspaper |
author |
Xinxin Ye Mina Deshler Alexi Lyapustin Yujie Wang Shobha Kondragunta Pablo Saide |
author_facet |
Xinxin Ye Mina Deshler Alexi Lyapustin Yujie Wang Shobha Kondragunta Pablo Saide |
author_sort |
Xinxin Ye |
title |
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
title_short |
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
title_full |
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
title_fullStr |
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
title_full_unstemmed |
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. |
title_sort |
assessment of satellite aod during the 2020 wildfire season in the western u.s. |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14236113 https://doaj.org/article/5df4f19b607947538b86002ccb9d97d1 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 14, Iss 6113, p 6113 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/23/6113 https://doaj.org/toc/2072-4292 doi:10.3390/rs14236113 2072-4292 https://doaj.org/article/5df4f19b607947538b86002ccb9d97d1 |
op_doi |
https://doi.org/10.3390/rs14236113 |
container_title |
Remote Sensing |
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14 |
container_issue |
23 |
container_start_page |
6113 |
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1766025483794776064 |