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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Published in:Remote Sensing
Main Authors: Xinxin Ye, Mina Deshler, Alexi Lyapustin, Yujie Wang, Shobha Kondragunta, Pablo Saide
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14236113
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/23/6113/ 2023-08-20T03:59:12+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 agris 2022-12-02 application/pdf https://doi.org/10.3390/rs14236113 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs14236113 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 23; Pages: 6113 aerosol optical depth MODIS VIIRS retrieval wildfire smoke Text 2022 ftmdpi https://doi.org/10.3390/rs14236113 2023-08-01T07:37:20Z 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 ... Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 14 23 6113
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic aerosol optical depth
MODIS
VIIRS
retrieval
wildfire smoke
spellingShingle aerosol optical depth
MODIS
VIIRS
retrieval
wildfire smoke
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
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 Text
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 Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14236113
op_coverage agris
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing; Volume 14; Issue 23; Pages: 6113
op_relation https://dx.doi.org/10.3390/rs14236113
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14236113
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